From 0447b84596da58f640be7d0654928e7eaf152cd9 Mon Sep 17 00:00:00 2001 From: OOKOWUOR Date: Tue, 10 Feb 2026 20:48:46 +0300 Subject: [PATCH] Hover effect and Data Anonymization module --- WWW/Image1.PNG | Bin 0 -> 20076 bytes WWW/Image2.PNG | Bin 0 -> 18448 bytes WWW/Image3.PNG | Bin 0 -> 121236 bytes WWW/Image4.PNG | Bin 0 -> 41052 bytes WWW/anon_assets/custom.css | 290 +++++ WWW/clipboard.min.js | 7 + WWW/styles.css | 671 ++++++---- modules/anonymization_functions.R | 129 ++ modules/mod_quant_anonymization.R | 362 +++++ server.R | 1468 ++++++++++----------- server/anon/server_module_quant.R | 1687 ++++++++++++++++++++++++ server/deep_learning.R | 2028 ++++++++++++++--------------- server/header_footer_configs.R | 188 +-- server/load_libraries.R | 19 + static_files/labelling_file.xlsx | Bin 1177355 -> 1347905 bytes ui.R | 232 ++-- ui/anonymization_ui.R | 14 + ui/dashboard_body.R | 231 ++-- ui/load_r_packages.R | 20 +- ui/sidebar_hover_collapse.R | 175 +++ users_db/users.sqlite | Bin 12288 -> 20480 bytes 21 files changed, 5263 insertions(+), 2258 deletions(-) create mode 100644 WWW/Image1.PNG create mode 100644 WWW/Image2.PNG create mode 100644 WWW/Image3.PNG create mode 100644 WWW/Image4.PNG create mode 100644 WWW/anon_assets/custom.css create mode 100644 WWW/clipboard.min.js create mode 100644 modules/anonymization_functions.R create mode 100644 modules/mod_quant_anonymization.R create mode 100644 server/anon/server_module_quant.R create mode 100644 server/load_libraries.R create mode 100644 ui/anonymization_ui.R create mode 100644 ui/sidebar_hover_collapse.R diff --git a/WWW/Image1.PNG b/WWW/Image1.PNG new file mode 100644 index 0000000000000000000000000000000000000000..f172327706bebc6322cbf3c794c0546bf0154a3e GIT binary patch literal 20076 zcmcHgWl&px`1XrZ+@0X=uEkw~Ybj8iKyh~|UfiAH2~gaM7I$lLcXxM+?)?7GGiPSc zoY}MYnSEYFzB4OpB`e9juIqDOca(;jJO(NWDhvz^hN6P377WaX80hmlWJKuwN6(~N z=)(s$EqQ5}>T$9o=mOqKN>vKFK?2%~83J^T;;it+4F(3Y@4wrJA*WIc7#Ot=in3DL z-o|Gg5xUw-P4CZdsn3JnPA>r$Ykor_536`jIp^XINcEWn1bERX^&wPL5Cwd=Xn2Bt zHXt^>l$>jFj@vi_ewv&AMqW(seCH5{&B~0z!~~I_M&tc*_A=|=iyXJAkasxrJ}z`~ zBmi2t8POoeMP!(9O@vy>PAHc6<7Q}rSG-q0q`jR?PEPLIWe*bNx67;Xt9U&=etyW$ zpW*+r4VL)2koQ=K8Bus0S`5N}J0twxJ7WVt_Z@4b$3_I*cU!F<7j)@qR%Z%bp7w%z zpv%QQtASoqE=jP4WR*@IsIJ?#Fy zWyu85Nc~La;kAQ@yK8W8uwUxTj^~HH)qA7L?Ug%GO&33&+m|x!=pBFad22qklY74! zy-ADe>S~~@EWG1dYgkee4tHWqOiWT{CKE}Vk3?Z%p@On<-_B41x2Px^!_EP+zvFMi z_~c|h4AtZ-?IJ^3>Bwm6lE7@>9i~8Oo55<$?FD5qk*ppoY>B?a&8Dj{%lIEfY{xwp565-J5I3NtE#$jHdhyg-7Lzv6pn zl!uHb>8jGm0Oc7{qJqWD1dk}_gtagH!D|P|VrV{8ZJuYnm0#**>G-ey&H`rR-8Dos zNkW!mN6W|$3m!g&O;2l88Z={Oa2VktMv0RWKXrDByX1`?@_v%g+EoN5K4s?FK_-qE z(>{?-3WBesLBf9b_S`5a!t!^eMu!LedXNRa^wJk|jBV|ZyoBZZ=Zh{v2gwQVr(2sT zhP%7F-Yw)~6? zg%4Zy-WI)M9mDt)$`^1XmRpkV9=KiNW?c@_WmBNLKVM^opeaeIMk0Jvb91Xl&VM*t zVeHkl6(l*-25#x8sGy6AippwB!!3`EsS>G`%fnsWoh(EqBoL0OKICzPNRd`FNgCi36a4psBx5IGJ-L& zun4DK`y#O9R{s9rl_&CvRx5oitARiu{ok^!pMU-OCGbES3IwS?6#i54$HB!7VK-

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100%; + min-height: 85vh; + position: relative; + overflow: hidden; /* keep module self-contained */ + font-family: Arial, sans-serif; + padding: 0 !important; + margin: 0 !important; +} + +/* Allow children to shrink properly (important for scroll containers) */ +.anon-root .container-fluid, +.anon-root .row, +.anon-root [class*="col-"], +.anon-root .tab-content, +.anon-root .tab-pane, +.anon-root .tab-pane.active { + min-height: 0 !important; + min-width: 0 !important; +} + +/* ----------------------------------------------------------------------- + 1) Dashboard container (landing removed) + ----------------------------------------------------------------------- */ +.anon-root .anon-dashboard { + min-height: 85vh; +} + +/* tab content should not force the whole page to grow */ +.anon-root .anon-dashboard .tab-content { + min-height: 0 !important; +} + +/* ----------------------------------------------------------------------- + 2) Layout heights (make the anonymization dashboard row fixed-height) + ----------------------------------------------------------------------- */ +/* Targets the row inside the Dashboard tabPanel */ +.anon-root .anon-dashboard .tab-pane.active > .row { + height: calc(100vh - 160px); /* adjust if your top header spacing differs */ + overflow: hidden !important; +} + +/* LEFT PANEL: the primary vertical scroller */ +.anon-root [id$="left-panel"] { + height: 100%; + overflow-y: auto !important; + overflow-x: hidden !important; + padding-right: 8px; +} + +/* left scrollbar styling */ +.anon-root [id$="left-panel"]::-webkit-scrollbar { width: 8px; } +.anon-root [id$="left-panel"]::-webkit-scrollbar-thumb { background: #c9c9c9; border-radius: 4px; } +.anon-root [id$="left-panel"]::-webkit-scrollbar-track { background: transparent; } + +/* RIGHT PANEL: fill height and split 50/50 */ +.anon-root [id$="right-panel"] { + height: 100%; + overflow: hidden !important; +} + +/* Right containers stretch to full height */ +.anon-root .right-containers { + height: 100% !important; + min-height: 0 !important; + display: flex !important; + flex-direction: column !important; + gap: 12px !important; + overflow: hidden !important; +} + +/* Each box gets equal share */ +.anon-root .right-box { + flex: 1 1 0 !important; /* equal split */ + min-height: 0 !important; + display: flex !important; + flex-direction: column !important; + + background: #ffffff; + border: 1px solid #e6e6e6; + border-radius: 8px; +} + +/* Headers */ +.anon-root .right-header { + flex: 0 0 auto !important; + padding: 10px 12px; + border-bottom: 1px solid #eeeeee; + background: #ffffff; + z-index: 2; +} + +.anon-root .right-header h3 { + margin: 0; + font-size: 18px; + font-weight: 700; +} + +/* Body becomes internal scroll container */ +.anon-root .right-body { + flex: 1 1 auto !important; + min-height: 0 !important; + overflow-y: auto !important; + overflow-x: hidden !important; + padding: 10px 12px; +} + +/* Preview table wrapper: allow scroll (both directions if needed) */ +.anon-root .table-wrap { + height: 100%; + min-height: 0 !important; + overflow: auto !important; +} + +.anon-root table { + width: 100% !important; + border-collapse: collapse; +} + +/* ----------------------------------------------------------------------- + 3) Risk summary cards + ----------------------------------------------------------------------- */ +.anon-root .risk-summary-grid { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 12px; + align-items: start; +} + +.anon-root .summary-card { + border: 1px solid #e6e6e6; + border-radius: 8px; + padding: 10px 12px; + background: #fafafa; +} + +/* ----------------------------------------------------------------------- + 4) Gauges + ----------------------------------------------------------------------- */ +.anon-root .gauge .gauge-value, +.anon-root .gauge .gauge-label { + display: none !important; +} + +.anon-root #preview-gauges .gauge-box { + display: flex; + flex-direction: column; + align-items: center; + text-align: center; +} + +.anon-root #preview-gauges h4 { + margin: 0 0 6px 0; +} + +/* ----------------------------------------------------------------------- + 5) Copy buttons (module-scoped) + ----------------------------------------------------------------------- */ +.anon-root button[id^="copy_"] { + float: right; + font-size: 12px; + background-color: #e0e0e0; + border: none; + border-radius: 5px; + padding: 6px 10px; + box-shadow: 1px 1px 3px rgba(0,0,0,0.15); + cursor: pointer; + margin-bottom: 10px; + position: relative; + z-index: 10; +} + +/* ----------------------------------------------------------------------- + 6) Inline suppress icon button + ----------------------------------------------------------------------- */ +.anon-root .qid-header { + display: flex; + align-items: center; + justify-content: space-between; + gap: 8px; + margin-bottom: 6px; +} + +.anon-root .icon-button { + background: #dc3545; + color: #ffffff; + border: none; + border-radius: 6px; + padding: 0; + cursor: pointer; + height: 32px; + width: 32px; + display: flex; + align-items: center; + justify-content: center; +} + +.anon-root .icon-button:hover { opacity: 0.9; } +.anon-root .icon-button .fa { pointer-events: none; } + +/* ----------------------------------------------------------------------- + 7) Leaflet map sizing + ----------------------------------------------------------------------- */ +.anon-root [id$="geo_map"] { + height: 420px; + border: 1px solid #e6e6e6; + border-radius: 8px; + overflow: hidden; +} + +/* ----------------------------------------------------------------------- + 8) Download note + ----------------------------------------------------------------------- */ +.anon-root .download-note { + font-size: 12px; + color: #444444; + background: #f7f7f7; + border: 1px dashed #dddddd; + border-radius: 8px; + padding: 8px; + margin-top: 8px; +} + +/* ----------------------------------------------------------------------- + 9) Dark mode (toggle should add/remove .dark-mode on .anon-root) + ----------------------------------------------------------------------- */ +.anon-root.dark-mode { + background-color: #1e1e1e !important; + color: #ffffff; +} + +.anon-root.dark-mode .right-box { + background: #2a2a2a; + border-color: #444444; +} + +.anon-root.dark-mode .right-header { + background: #2a2a2a; + border-bottom-color: #444444; +} + +.anon-root.dark-mode .summary-card { + background: #2e2e2e; + border-color: #444444; +} + +.anon-root.dark-mode button[id^="copy_"] { + background-color: #444444; + color: #ffffff; +} + +/* ----------------------------------------------------------------------- + 10) Responsive adjustments + ----------------------------------------------------------------------- */ +@media (max-width: 992px) { + /* On smaller screens, let the page flow naturally */ + .anon-root .anon-dashboard .tab-pane.active > .row { + height: auto; + overflow: visible !important; + } + + .anon-root [id$="left-panel"] { + height: auto; + max-height: none; + } + + .anon-root [id$="right-panel"] { + height: auto; + } + + .anon-root .right-containers { + height: auto !important; + } + + .anon-root .risk-summary-grid { + grid-template-columns: 1fr; + } +} diff --git a/WWW/clipboard.min.js b/WWW/clipboard.min.js new file mode 100644 index 0000000..513aa38 --- /dev/null +++ b/WWW/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * clipboard.js v2.0.11 + * https://clipboardjs.com/ + * + * Licensed MIT © Zeno Rocha + */ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return function(n){var o={};function r(t){if(o[t])return o[t].exports;var e=o[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,r),e.l=!0,e.exports}return r.m=n,r.c=o,r.d=function(t,e,n){r.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:n})},r.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"= diff --git a/WWW/styles.css b/WWW/styles.css index 72210d0..87b8bec 100644 --- a/WWW/styles.css +++ b/WWW/styles.css @@ -1,146 +1,362 @@ -/* Media query for tablets */ -@media (min-width: 577px) and (max-width: 992px) { - .roundbuttons { - width: 50%; /* Adjust the width for tablets */ - } +/* ========================================================================== + APHRC — FINAL PERFECT CSS + - Outer sidebar transparent (no extra layer) + - Only inner pill visible + - Pill is dark blue/gray (AdminLTE-like) + - No dark ring around dashboard + ========================================================================== */ + +/* ========================= + 0) Palette / globals + ========================= */ +:root{ + --aphrc-green: #7BC148; + --aphrc-cyan: #00BFC4; + --aphrc-text: #333333; + --aphrc-bg: #FFFFFF; + + /* Layout constants */ + --aphrc-topbar-height: 110px; /* adjust if needed */ + --aphrc-sidebar-collapsed: 56px; + --aphrc-sidebar-expanded: 240px; + --aphrc-transition-ms: 320ms; + + /* Pill styling */ + --aphrc-pill-color: #1f2d33; /* dark blue/gray */ + --aphrc-pill-radius: 18px; + --aphrc-pill-pad-y: 10px; + --aphrc-pill-pad-x: 8px; } +html, body{ height: 100%; } -input[type="radio"] { - width: 12px; - height: 12px; - accent-color: #17a2b8; - border: 1px solid #7bc148; /* Green border */ - cursor: pointer; +body{ + color: var(--aphrc-text); + background: var(--aphrc-bg); + overflow-x: hidden; } +/* ========================= + 1) Remove AdminLTE dark canvas/ring + ========================= */ +html, +body, +.wrapper, +.content-wrapper, +.right-side, +.main-footer{ + background-color: var(--aphrc-bg) !important; +} -.box.box-success > .box-header { - background-color: #bde0a3 !important; /* lighter green header */ - } +.wrapper, +.content-wrapper, +.right-side{ + box-shadow: none !important; + border: 0 !important; +} +.content-wrapper{ + border-left: 0 !important; +} -.footer { - background-color: #7bc148; - color: white; - padding: 0px; - text-align: center; - } - - .socialform { - text-align: center; - padding: 10px; - } - .footer a { - margin: 0px; - color: white; - } - .footer a:hover { - color: #337ab7; - } - - hr { - width: 100%; /* Adjust the percentage to control the length */ - } - - - .header { - background: white; - color: #7bc148; - padding: 10px; - display: flex; - align-items: center; - text-align:center; - } - - hr { - width: 100%; /* Adjust the percentage to control the length */ - } - - .logo { - flex-shrink: 0; - margin-right: 20px; - max-height: 80%; - max-width: 80%; - } - - .header-text h1 { - font-size: 30px; - font-weight: bold; - text-align:center; - } - - .header-text h3 { - font-size: 20px; - - } - - - /* navbar (rest of the header) */ - .skin-blue .main-header .navbar { - background-color:green; +/* ========================= + 2) Remove AdminLTE header bar (you use .custom-header) + ========================= */ +.main-header, +.main-header .navbar, +.skin-blue .main-header .navbar, +.skin-green .main-header .navbar{ + display: none !important; + height: 0 !important; + min-height: 0 !important; + border: 0 !important; + margin: 0 !important; + padding: 0 !important; +} - } +/* Make room for your custom header */ +body .wrapper{ + padding-top: var(--aphrc-topbar-height) !important; +} - /* main sidebar */ - .skin-blue .main-sidebar { - background-color: #7BC148; +.content-wrapper, .right-side, .content{ + padding-top: 0 !important; + margin-top: 0 !important; +} - } - /* active selected tab in the sidebarmenu */ - .skin-blue .main-sidebar .sidebar .sidebar-menu .active a{ - background-color:#7BC148; - } +/* ========================= + 3) Your custom header (.custom-header) + ========================= */ +.custom-header{ + position: fixed !important; + top: 0 !important; + left: 0 !important; + right: 0 !important; + z-index: 3000 !important; + background-color: var(--aphrc-green) !important; + color: #fff !important; + padding: 10px !important; + margin: 0 !important; + border: 0 !important; + box-shadow: none !important; +} - /* other links in the sidebarmenu */ - .skin-blue .main-sidebar .sidebar .sidebar-menu a{ - background-color: #7BC148; - border-color: #7BC148; - color: #ffffff; - } +/* Keep your older .header (white) if used elsewhere (login etc.) */ +.header{ + background: #fff; + color: var(--aphrc-green); + padding: 10px; + display: flex; + align-items: center; + text-align: center; +} - /* other links in the sidebarmenu when hovered */ - .skin-blue .main-sidebar .sidebar .sidebar-menu a:hover{ - background-color: #2c3b41; - } - /* toggle button when hovered */ - .skin-blue .main-header .navbar .sidebar-toggle:hover{ - background-color: #D37D28; - } - /* toggle button when hovered */ - .skin-blue .main-header .navbar .sidebar-toggle{ - margin:1em; - } +.logo{ + flex-shrink: 0; + margin-right: 20px; + max-height: 80%; + max-width: 80%; +} - /* body */ - .content-wrapper, .right-side { - background-color: #ffffff; - padding-left: 1.5em; +.header-text h1{ + font-size: 30px; + font-weight: bold; + text-align: center; +} +.header-text h3{ + font-size: 20px; +} + +/* ========================= + 4) Sidebar: transparent shell + dark pill only + ========================= */ + +/* Sidebar shell (outer area) is TRANSPARENT and non-blocking */ +.main-sidebar, +.left-side, +.skin-green .main-sidebar, +.skin-blue .main-sidebar, +.main-sidebar .sidebar{ + background: transparent !important; + background-color: transparent !important; + border: 0 !important; + box-shadow: none !important; +} + +/* Fixed positioning and hover-expand */ +.main-sidebar{ + position: fixed !important; + left: 0 !important; + top: var(--aphrc-topbar-height) !important; + height: calc(100vh - var(--aphrc-topbar-height)) !important; + + width: var(--aphrc-sidebar-collapsed) !important; + + overflow: visible !important; /* important: pill can have shadow */ + z-index: 2000 !important; + + transition: width var(--aphrc-transition-ms) cubic-bezier(0.22, 1, 0.36, 1) !important; + will-change: width; + + pointer-events: none !important; /* outer shell won’t block footer/clicks */ +} + +.main-sidebar:hover{ + width: var(--aphrc-sidebar-expanded) !important; +} + +/* Content aligned to collapsed sidebar */ +.content-wrapper, .right-side, .main-footer{ + margin-left: var(--aphrc-sidebar-collapsed) !important; + transition: margin-left var(--aphrc-transition-ms) cubic-bezier(0.22, 1, 0.36, 1) !important; +} + +.main-sidebar:hover ~ .content-wrapper, +.main-sidebar:hover ~ .right-side, +.main-sidebar:hover ~ .main-footer{ + margin-left: var(--aphrc-sidebar-expanded) !important; +} + +/* Sidebar inner container */ +.main-sidebar .sidebar{ + padding-top: 8px !important; + margin-top: 0 !important; + overflow: visible !important; +} + +/* Make ONLY the pill interactive */ +.main-sidebar .sidebar-menu{ + pointer-events: auto !important; +} + +/* Stop old rules that paint every green */ +.skin-green .main-sidebar .sidebar .sidebar-menu a, +.skin-blue .main-sidebar .sidebar .sidebar-menu a{ + background: transparent !important; + background-color: transparent !important; + border-color: transparent !important; +} + +/* The DARK PILL panel (height = content) */ +.main-sidebar .sidebar-menu{ + background: var(--aphrc-pill-color) !important; + background-color: var(--aphrc-pill-color) !important; + + margin: 12px var(--aphrc-pill-pad-x) !important; + padding: var(--aphrc-pill-pad-y) 0 !important; + + border-radius: var(--aphrc-pill-radius) !important; + box-shadow: 0 10px 24px rgba(0,0,0,0.18) !important; + + overflow: hidden !important; + list-style: none !important; +} + +/* Menu items */ +.main-sidebar .sidebar-menu > li{ margin: 0 !important; } + +.main-sidebar .sidebar-menu > li > a{ + height: 44px !important; + display: flex !important; + align-items: center !important; + box-sizing: border-box !important; + line-height: 44px !important; + + /* collapsed: icons centered */ + justify-content: center !important; + padding: 0 !important; + + color: #ffffff !important; + border-radius: 12px !important; +} + +/* Icons */ +.main-sidebar .sidebar-menu > li > a > i{ + font-size: 16px !important; + margin: 0 !important; + width: auto !important; +} + +/* Hover/active (dark theme) */ +.main-sidebar .sidebar-menu > li > a:hover{ + background: rgba(255,255,255,0.10) !important; +} +.main-sidebar .sidebar-menu > li.active > a, +.main-sidebar .sidebar-menu > li > a:focus{ + background: rgba(255,255,255,0.16) !important; +} + +/* Collapsed: hide labels */ +.main-sidebar .sidebar-menu > li > a > span, +.main-sidebar .sidebar-menu > li > a > .pull-right-container{ + display: none !important; +} + +/* Expanded hover: show labels and align left */ +.main-sidebar:hover .sidebar-menu > li > a{ + justify-content: flex-start !important; + padding: 0 15px !important; +} +.main-sidebar:hover .sidebar-menu > li > a > i{ + margin-right: 10px !important; +} +.main-sidebar:hover .sidebar-menu > li > a > span, +.main-sidebar:hover .sidebar-menu > li > a > .pull-right-container{ + display: inline-block !important; +} + +/* Small screens: tighten pill */ +@media (max-width: 576px){ + .main-sidebar .sidebar-menu{ + border-radius: 16px !important; + margin: 10px 6px !important; } - - - /*Tables*/ - -.dataTables_wrapper , .dataTables_wrapper .dataTables_wrapper .dataTables_info, .dataTables_wrapper .dataTables_processing, .dataTables_wrapper{ - color:#000000; - background-color:#ffffff; - } - - thead { - color: #ffffff; - font-size:1.2em; - background-color:#17a2b8; - } - - tbody { - color: #000000; - font-size:1.2em; - } - +} + +/* ========================= + 5) Content area styling + ========================= */ +.content-wrapper, .right-side{ + background-color: #ffffff !important; + padding-left: 1.5em !important; +} + +/* ========================= + 6) Tables / DataTables + ========================= */ +.dataTables_wrapper, +.dataTables_wrapper .dataTables_info, +.dataTables_wrapper .dataTables_processing{ + color:#000000 !important; + background-color:#ffffff !important; +} + +thead{ + color: #ffffff !important; + font-size:1.2em !important; + background-color:#17a2b8 !important; +} +tbody{ + color: #000000 !important; + font-size:1.2em !important; +} + +table{ + background-color: white !important; + color: black !important; + border: 1px solid #ddd !important; +} +th{ + background-color: #17a2b8 !important; + color: white !important; +} +td{ + background-color: #ffffff !important; + color: black !important; +} + +/* ========================= + 7) Forms / inputs + ========================= */ +input[type="radio"]{ + width: 12px; + height: 12px; + accent-color: #17a2b8; + border: 1px solid var(--aphrc-green); + cursor: pointer; +} + +.shiny-input-checkbox input[type='checkbox']{ + background-color: #4CAF50; +} + +.bootstrap-switch{ + background-color: #4CAF50 !important; +} + +@media (min-width: 577px) and (max-width: 992px){ + .roundbuttons{ width: 50%; } +} + +#primaryTabs{ + color: var(--aphrc-green); + width: 100%; +} + +/* ========================= + 8) Upload / overview panels and cards + ========================= */ #upload_form{ padding-left: 1.2em; justify-content: left; align-items: left; + + background: var(--aphrc-bg); + border: 2px solid var(--aphrc-green); + border-radius: 14px; + box-shadow: 0 10px 24px rgba(0,0,0,0.10); + padding: 14px 16px; + margin: 16px auto; } #OverViewMenu{ @@ -148,166 +364,161 @@ input[type="radio"] { overflow-y: auto; } -table { - background-color: white; - color: black; - border: 1px solid #ddd; +.aphrc-row{ + background: var(--aphrc-bg); + border: 2px solid var(--aphrc-green); + border-radius: 14px; + box-shadow: 0 10px 24px rgba(0,0,0,0.10); + padding: 14px 16px; + margin: 16px auto; } -th { - background-color: #17a2b8; - color: white; +.aphrc-row:hover, +.upload_form:hover, +#aphrc-row1:hover{ + box-shadow: 0 14px 28px rgba(0,0,0,0.12); + transform: translateY(-1px); + transition: all .18s ease-in-out; } -td { - background-color: #ffffff; - color: black; +/* ========================= + 9) Boxes + ========================= */ +.box.box-success > .box-header{ + background-color: #bde0a3 !important; } -.shiny-input-checkbox input[type='checkbox'] { - background-color: #4CAF50; - } - - - .bootstrap-switch{ - background-color: #4CAF50 !important; +/* ========================= + 10) Footer (your custom .footer) + ========================= */ +.footer{ + background-color: var(--aphrc-green); + color: white; + padding: 0px; + text-align: center; } -#primaryTabs { - color: #7bc148; - width: 100%; +.socialform{ + text-align: center; + padding: 10px; +} +.footer a{ + margin: 0px; + color: white; +} +.footer a:hover{ + color: #337ab7; } +/* ========================= + 11) HR + ========================= */ +hr{ width: 100%; } -.auth-container { +/* ========================= + 12) Auth / login page UI + ========================= */ +.auth-container{ max-width: 400px; margin: 6% auto; padding: 30px 25px; - border-color:#7bc148; + border-color: var(--aphrc-green); border-radius: 15px; - border-width:2px; + border-width: 2px; background-color: #fff; box-shadow: 0 0 25px rgba(0, 0, 0, 0.1); } -.auth-title { +.auth-title{ font-weight: 600; font-size: 24px; margin-bottom: 20px; } -.toggle-buttons { +.toggle-buttons{ display: flex; justify-content: center; margin-bottom: 20px; } -.toggle-buttons button { +.toggle-buttons button{ margin: 0 5px; - background-color: #7bc148; + background-color: var(--aphrc-green); } -#logoutID { +#logoutID{ background-color: white; color: gray; font-size: 18px; border: 2px solid gray; cursor: pointer; - /* Center icon */ display: flex; justify-content: center; align-items: center; } - -#logoutBtn:hover { - background-color: #c82333; - } - -#loading_screen { - position: fixed; - top: 0; left: 0; - width: 100%; height: 100%; - background-color: #ffffff; - z-index: 9999; - display: flex; - justify-content: center; - align-items: center; - font-size: 40px; - color: #7bc148; - } - - -#loader svg circle { - stroke-width: 20px !important; /* increase this for a thicker ring */ - } - - /* APHRC palette */ -:root{ - --aphrc-green: #7BC148; - --aphrc-cyan: #00BFC4; - --aphrc-text: #333333; - --aphrc-bg: #FFFFFF; -} -/* Row container with shadow + border */ -.aphrc-row { - background: var(--aphrc-bg); - border: 2px solid var(--aphrc-green); - border-radius: 14px; - box-shadow: 0 10px 24px rgba(0,0,0,0.10); - padding: 14px 16px; /* space around row contents */ - margin: 16px auto; /* vertical rhythm */ -} -/* Optional subtle hover lift */ -.aphrc-row:hover{ - box-shadow: 0 14px 28px rgba(0,0,0,0.12); - transform: translateY(-1px); - transition: all .18s ease-in-out; +#logoutBtn:hover{ + background-color: #c82333; } -.aphrc-row:hover{ - box-shadow: 0 14px 28px rgba(0,0,0,0.12); - transform: translateY(-1px); - transition: all .18s ease-in-out; +/* ========================= + 13) Loading screen / loader + ========================= */ +#loading_screen{ + position: fixed; + top: 0; left: 0; + width: 100%; height: 100%; + background-color: #ffffff; + z-index: 9999; + display: flex; + justify-content: center; + align-items: center; + font-size: 40px; + color: var(--aphrc-green); } - - -#upload_form{ - padding-left: 1.2em; - justify-content: left; - align-items: left; - background: var(--aphrc-bg); - border: 2px solid var(--aphrc-green); - border-radius: 14px; - box-shadow: 0 10px 24px rgba(0,0,0,0.10); - padding: 14px 16px; /* space around row contents */ - margin: 16px auto; /* vertical rhythm */ +#loader svg circle{ + stroke-width: 20px !important; } -.upload_form:hover{ - box-shadow: 0 14px 28px rgba(0,0,0,0.12); - transform: translateY(-1px); - transition: all .18s ease-in-out; +/* ========================= + 14) Progress bar + ========================= */ +.top-progress .progress-bar{ + background-color: var(--aphrc-green) !important; } - - -#aphrc-row1:hover{ - box-shadow: 0 14px 28px rgba(0,0,0,0.12); - transform: translateY(-1px); - transition: all .18s ease-in-out; +/* ========================= + 15) Anonymization module isolation + ========================= */ +.anon-root table, +.anon-root th, +.anon-root td, +.anon-root thead, +.anon-root tbody{ + all: unset; } -#aphrc-row1:hover{ - box-shadow: 0 14px 28px rgba(0,0,0,0.12); - transform: translateY(-1px); - transition: all .18s ease-in-out; +.anon-root table{ + width: 100% !important; + border-collapse: collapse !important; + display: table; } -.top-progress .progress-bar { - background-color: #7bc148 !important; - } +.anon-root thead{ display: table-header-group; } +.anon-root tbody{ display: table-row-group; } +.anon-root tr{ display: table-row; } +.anon-root th, +.anon-root td{ + display: table-cell; + padding: 6px 10px; + border: 1px solid #e6e6e6; + vertical-align: top; +} +.anon-root{ + padding: 0 !important; + margin: 0 !important; +} diff --git a/modules/anonymization_functions.R b/modules/anonymization_functions.R new file mode 100644 index 0000000..acc227b --- /dev/null +++ b/modules/anonymization_functions.R @@ -0,0 +1,129 @@ +# anonymization_functions.R + +# Load required packages +library(dplyr) +library(sdcMicro) +library(digest) +library(uuid) + +# --- MASKING --- +# Replace every character in character columns with "*" +apply_masking <- function(df, cols){ + df %>% + mutate(across( + all_of(cols), + ~ if (is.character(.x)) stringr::str_replace_all(.x, ".", "*") else .x + )) +} +attr(apply_masking, "description") <- "Replace each character in specified columns with '*'" + +# --- SUPPRESSION --- +# Remove the specified columns entirely +apply_suppression <- function(df, cols) { + df %>% select(-any_of(cols)) +} +attr(apply_suppression, "description") <- "Drop the specified columns" + +# --- BUCKETING --- +# Groups numeric values into fixed-width bins starting at 0 +apply_bucketing <- function(df, cols, bin_size) { + df %>% + mutate(across( + all_of(cols), + ~ { + max_val <- max(.x, na.rm = TRUE) + breaks <- seq(0, max_val + bin_size, by = bin_size) + labels <- paste0(head(breaks, -1), "-", breaks[-1] - 1) + cut(.x, breaks = breaks, labels = labels, include.lowest = TRUE, right = FALSE) + } + )) +} +attr(apply_bucketing, "description") <- "Bucket numeric columns into fixed-width intervals starting at 0" + +# --- PSEUDONYMIZATION --- +# Hash each value in the specified columns with SHA-256 +apply_pseudonymization <- function(df, cols) { + df %>% + mutate(across( + all_of(cols), + ~ digest(as.character(.x), algo = "sha256") + )) +} +attr(apply_pseudonymization, "description") <- "Replace values with SHA-256 hash digests" + +# --- TOKENIZATION --- +# Replace each value with a random 10-character alphanumeric token +apply_tokenization <- function(df, cols, seed = 123) { + set.seed(seed) + df %>% + mutate(across( + all_of(cols), + ~ replicate(n(), paste0(sample(c(0:9, letters, LETTERS), 10, TRUE), collapse = "")) + )) +} +attr(apply_tokenization, "description") <- "Replace values with random 10-character tokens" + +# --- BASIC K-ANONYMITY --- +# Keeps only those QID-groups of size >= k +apply_k_anonymity <- function(df, cols, k) { + df %>% + group_by(across(all_of(cols))) %>% + mutate(.group_size = n()) %>% + ungroup() %>% + filter(.group_size >= k) %>% + select(-.group_size) +} +attr(apply_k_anonymity, "description") <- "Filter to only those groups whose size >= k" + +# --- EXTENDED K-ANONYMITY --- +# Suppress direct IDs, generalize numeric QIDs, then enforce k-anonymity via sdcMicro +apply_k_extended <- function(df, qids, k, bucket_cols = list(), direct_ids = character()) { + # 1) Suppress direct identifiers + df_proc <- df %>% select(-any_of(direct_ids)) + df_proc$row_id_temp <- seq_len(nrow(df_proc)) + + # 2) Generalize numeric QIDs + for (col in names(bucket_cols)) { + df_proc[[col]] <- bucket_cols[[col]](df_proc[[col]]) + } + + # 3) sdcMicro k-anonymity + qids2 <- intersect(qids, names(df_proc)) + sdcObj <- createSdcObj(dat = df_proc, keyVars = qids2) + sdcObj <- kAnon(sdcObj, k = k) + df_k <- extractManipData(sdcObj) + + # 4) Combine matched & unmatched + matched <- df_k %>% select(-row_id_temp) + unmatched <- df_proc %>% filter(!row_id_temp %in% df_k$row_id_temp) %>% select(-row_id_temp) + bind_rows(matched, unmatched) +} +attr(apply_k_extended, "description") <- "Extended k-anonymity with bucketing & suppression" + +# --- L-DIVERSITY --- +# Keep only those QID groups where the sensitive attribute has >= l distinct values +apply_l_diversity <- function(df, qids, sensitive_attr, l) { + df %>% + group_by(across(all_of(qids))) %>% + filter(n_distinct(.data[[sensitive_attr]]) >= l) %>% + ungroup() +} +attr(apply_l_diversity, "description") <- "Filter groups to ensure >= l diversity in the sensitive attribute" + +# --- T-CLOSENESS --- +# Keep only those QID groups whose distribution of the sensitive attribute is within threshold t of the global distribution +apply_t_closeness <- function(df, qids, sensitive_attr, t) { + # compute global distribution + global_dist <- df %>% count(.data[[sensitive_attr]]) %>% mutate(prop = n / sum(n)) + + df %>% + group_by(across(all_of(qids))) %>% + filter({ + local <- count(cur_data(), .data[[sensitive_attr]]) %>% mutate(prop = n / sum(n)) + # total variation distance + tvd <- sum(abs(global_dist$prop - local$prop)) / 2 + tvd <= t + }) %>% + ungroup() +} +attr(apply_t_closeness, "description") <- "Filter groups whose sensitive-attribute distribution is within t of the global distribution" diff --git a/modules/mod_quant_anonymization.R b/modules/mod_quant_anonymization.R new file mode 100644 index 0000000..f119dd1 --- /dev/null +++ b/modules/mod_quant_anonymization.R @@ -0,0 +1,362 @@ +# modules/mod_quant_anonymization.R + +# IMPORTANT: keep these global so anon_quant_server_logic() is visible +source(.anon_server_mod, local = FALSE) + +# ---- Quantitative anonymization module ---- +mod_quant_anon_ui <- function(id) { + ns <- shiny::NS(id) + + # Precompute namespaced IDs used in JS strings + ns_right_panel <- ns("right-panel") + ns_main_tabs <- ns("main_tabs") + ns_remove_ids <- ns("remove_ids") # must exist in identifier_selector output + ns_remove_inline <- ns("remove_ids_inline") + ns_method <- ns("method") + + # ACE IDs + ns_r_ace <- ns("r_code_ace") + ns_stata_ace <- ns("stata_code_ace") + ns_py_ace <- ns("python_code_ace") + ns_copy_r <- ns("copy_r") + ns_copy_st <- ns("copy_stata") + ns_copy_py <- ns("copy_py") + + # Single main section + ns_dashboard <- ns("dashboard") + + shiny::div( + id = ns("anon_root"), + class = "anon-root", + shiny::tagList( + shinyjs::useShinyjs(), + + shiny::tags$head( + # Font Awesome (icons) + shiny::tags$link( + rel = "stylesheet", + href = "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.0/css/all.min.css" + ), + + # Copy helper JS (works with namespaced ACE ids) + shiny::tags$script(shiny::HTML(" + function copyAce(editorId, btnId) { + var ed = ace.edit(editorId); + if(!ed) return; + var code = ed.getValue(); + var ta = document.createElement('textarea'); + ta.value = code; + document.body.appendChild(ta); + ta.select(); + document.execCommand('copy'); + document.body.removeChild(ta); + var btn = document.getElementById(btnId); + if(btn){ + btn.innerText = 'Copied!'; + setTimeout(function(){ btn.innerText = '📋 Copy'; }, 2000); + } + } + ")) + ), + + # ==== MAIN DASHBOARD (visible immediately) ==== + shiny::div( + id = ns_dashboard, + class = "anon-dashboard", + style = "display:block; padding:20px;", + + shiny::tabsetPanel( + id = ns_main_tabs, + + # -------------------------- DASHBOARD TAB ------------------------------- + shiny::tabPanel( + "Dashboard", + shiny::fluidRow( + + # ---------- LEFT PANEL ---------- + shiny::column( + width = 4, id = ns("left-panel"), + + # Step 0 (platform dataset picker + optional file upload fallback) + shiny::wellPanel( + shiny::tags$h4("Step 0: Load Data"), + + shiny::div( + style = "padding:8px; border:1px solid #eee; border-radius:6px;", + shiny::tags$strong("Use already uploaded and Selecetd data"), + shiny::br(), + shiny::uiOutput(ns("platform_dataset_picker")), + shiny::actionButton( + ns("use_platform_data"), + "Load selected dataset", + class = "btn btn-success btn-block" + ), + shiny::tags$small( + style = "display:block; margin-top:6px; color:#666;", + "" + ) + ), + + shiny::tags$hr(), + shiny::textOutput(ns("n_obs_text")) + ), + + + # Step 1 + shiny::wellPanel( + shiny::div( + class = "qid-header", + shiny::tags$h4("Step 1: Remove Direct Identifiers"), + shiny::tags$button( + id = ns_remove_inline, + class = "icon-button", + title = "Suppress & Remove Identifiers", + onclick = sprintf("$('#%s').click();", ns_remove_ids), + shiny::tags$i(class = "fa fa-eraser") + ) + ), + shiny::uiOutput(ns("identifier_selector")) + ), + + # Step 2 + shiny::wellPanel( + shiny::tags$h4("Step 2: Select Quasi-Identifiers"), + shiny::uiOutput(ns("bucket_ui")) + ), + + # Step 3 + shiny::wellPanel( + shiny::tags$h4("Step 3: Choose Method & Parameters"), + shiny::selectInput( + ns_method, + "Anonymization Method:", + choices = c( + "Masking", "Suppression", "Bucketing", + "Pseudonymization", "Tokenization", + "K-Anonymity", + "Generalization", + "Anonymize Coordinates" + ), + selected = "Masking" + ), + + shiny::uiOutput(ns("extra_input")), + + shiny::conditionalPanel( + condition = sprintf("input['%s'] == 'Generalization'", ns_method), + shiny::div( + style = "margin-top:8px; padding:10px; background:#f5f5f5; border-radius:4px;", + shiny::tags$strong("Generalization tips:"), + shiny::tags$ul( + shiny::tags$li("Use drag & drop for categorical variables."), + shiny::tags$li(shiny::HTML("Switch to Custom numeric ranges for numeric variables. Non-numeric fields will be hidden in that mode.")), + shiny::tags$li("Ranges must not overlap or touch.") + ) + ) + ), + + shiny::fluidRow( + shiny::column(4, shiny::actionButton(ns("apply"), "Apply", class = "btn btn-primary btn-block")), + shiny::column(4, shiny::actionButton(ns("undo"), "Undo", class = "btn btn-warning btn-block")), + shiny::column(4, shiny::actionButton(ns("reset"), "Reset", class = "btn btn-danger btn-block")) + ), + + # Advisor + shiny::wellPanel( + shiny::tags$h4("Bin-Size Advisor"), + shiny::selectInput( + ns("advisor_var"), + "Choose numeric variable:", + choices = NULL, + selectize = TRUE + ), + shiny::actionButton(ns("advisor_run"), "Show suggestions", class = "btn btn-info btn-block"), + shiny::tags$hr(), + shiny::tags$h5("Overall Histogram"), + shiny::plotOutput(ns("advisor_dist"), height = "220px"), + shiny::tags$h5("Summary Statistics"), + shiny::verbatimTextOutput(ns("advisor_summary")), + shiny::tags$h5("Bin-width Suggestions"), + shiny::tableOutput(ns("advisor_table")), + shiny::plotOutput(ns("advisor_plot"), height = "220px") + ), + + # Downloads / Report + shiny::wellPanel( + shiny::checkboxInput(ns("dark_mode"), "Enable Dark Mode"), + shiny::tags$hr(), + shiny::tags$h4("Downloads"), + shiny::downloadButton(ns("download"), "Download CSV", class = "btn-block mb-1"), + shiny::downloadButton(ns("download_excel"), "Download Excel", class = "btn-block mb-1"), + shiny::downloadButton(ns("download_dta"), "Download Stata", class = "btn-block mb-1"), + shiny::downloadButton(ns("download_report"), "Download Risk Report As pdf", class = "btn-block mb-1"), + shiny::actionButton(ns("view_report"), "View Report", class = "btn btn-info btn-block"), + + shiny::conditionalPanel( + condition = sprintf("input['%s'] == 'Anonymize Coordinates'", ns_method), + shiny::div( + class = "download-note", + shiny::HTML("Note: When Anonymize Coordinates is the last applied method, the standard downloads above will export the anonymized coordinates. No separate download is needed.") + ) + ), + + shiny::tags$hr(), + shiny::uiOutput(ns("k_report")), + shiny::tags$hr(), + shiny::tags$h4("Steps Log"), + shiny::verbatimTextOutput(ns("step_log"), placeholder = TRUE) + ) + ) + ), + + # ---------- RIGHT PANEL ---------- + shiny::column( + width = 8, id = ns_right_panel, + shiny::div( + class = "right-containers", + + # === Container 1: Data Preview / Map === + shiny::div( + class = "right-box", + shiny::div(class = "right-header", shiny::tags$h3("Data Preview")), + shiny::div( + class = "right-body", + + shiny::conditionalPanel( + condition = sprintf("input['%s'] == 'Anonymize Coordinates'", ns_method), + leaflet::leafletOutput(ns("geo_map"), height = "420px") + ), + + shiny::conditionalPanel( + condition = sprintf("input['%s'] != 'Anonymize Coordinates'", ns_method), + shiny::div( + id = ns("preview-table"), + class = "table-wrap", + shiny::tableOutput(ns("preview_merged")) + ) + ) + ) + ), + + # === Container 2: Risk Assessment === + shiny::div( + class = "right-box", + shiny::div(class = "right-header", shiny::tags$h3("Risk Assessment")), + shiny::div( + class = "right-body", + shiny::div( + class = "risk-summary-grid", + shiny::div(class = "summary-card", shiny::uiOutput(ns("risk_before"))), + shiny::div(class = "summary-card", shiny::uiOutput(ns("risk_after"))) + ), + shiny::tags$br(), + shiny::div( + id = ns("preview-gauges"), + shiny::fluidRow( + shiny::column( + width = 6, + shiny::div( + class = "gauge-box", + shiny::tags$h4("Risk Before"), + flexdashboard::gaugeOutput(ns("gauge_before"), height = "200px") + ) + ), + shiny::column( + width = 6, + shiny::div( + class = "gauge-box", + shiny::tags$h4("Risk After"), + flexdashboard::gaugeOutput(ns("gauge_after"), height = "200px") + ) + ) + ) + ) + ) + ) + ) + ) + ) + ), + + # ----------------------------- CODES TAB --------------------------------- + shiny::tabPanel( + "Codes", + shiny::fluidRow( + shiny::column( + width = 4, + shiny::tags$h4("R Code"), + shinyAce::aceEditor( + outputId = ns_r_ace, + mode = "r", + theme = "chrome", + readOnly = TRUE, + height = "400px" + ), + shiny::actionButton( + ns_copy_r, "📋 Copy", + class = "btn btn-primary btn-block", + onclick = sprintf("copyAce('%s','%s')", ns_r_ace, ns_copy_r) + ) + ), + shiny::column( + width = 4, + shiny::tags$h4("Stata Code"), + shinyAce::aceEditor( + outputId = ns_stata_ace, + mode = "stata", + theme = "chrome", + readOnly = TRUE, + height = "400px" + ), + shiny::actionButton( + ns_copy_st, "📋 Copy", + class = "btn btn-primary btn-block", + onclick = sprintf("copyAce('%s','%s')", ns_stata_ace, ns_copy_st) + ) + ), + shiny::column( + width = 4, + shiny::tags$h4("Python Code"), + shinyAce::aceEditor( + outputId = ns_py_ace, + mode = "python", + theme = "chrome", + readOnly = TRUE, + height = "400px" + ), + shiny::actionButton( + ns_copy_py, "📋 Copy", + class = "btn btn-primary btn-block", + onclick = sprintf("copyAce('%s','%s')", ns_py_ace, ns_copy_py) + ) + ) + ) + ), + + # -------------------------- DESCRIPTIONS TAB ---------------------------- + shiny::tabPanel( + "Descriptions", + shiny::uiOutput(ns("descriptions_panel")) + ) + ) + ) + ) + ) +} + +# Allow passing rv_current from main server to anon logic +mod_quant_anon_server <- function(id, rv_current = NULL) { + shiny::moduleServer(id, function(input, output, session) { + if (exists("anon_quant_server_logic", mode = "function", inherits = TRUE)) { + get("anon_quant_server_logic", mode = "function", inherits = TRUE)( + input, output, session, + rv_current = rv_current + ) + } else { + shiny::showNotification( + "anon_quant_server_logic() not found. Check server/anon/server_module_quant.R", + type = "error" + ) + } + }) +} diff --git a/server.R b/server.R index dc5355a..f17d59d 100644 --- a/server.R +++ b/server.R @@ -12,735 +12,749 @@ function(input, output, session){ USER = user_auth(input, output, session) authed_started = reactiveVal(FALSE) - - observeEvent(USER$logged_in, { - req(isTRUE(USER$logged_in)) - waiter_show( - html = spin_loaders(id = 3, style="width:56px;height:56px;color:#7BC148;"), - color = "#FFF" - ) - - if (authed_started()) return() - authed_started(TRUE) - - app_username = USER$username - model_training_caret_pb = Attendant$new("model_training_caret_pb", hide_on_max = TRUE) - data_upload_id_pb = Attendant$new("data_upload_id_pb", hide_on_max = TRUE) - model_metrics_caret_pb = Attendant$new("model_metrics_caret_pb", hide_on_max = TRUE) - deploy_models_caret_pb = Attendant$new("deploy_models_caret_pb", hide_on_max = TRUE) - predict_models_caret_pb = Attendant$new("predict_models_caret_pb", hide_on_max = TRUE) - predict_endpoint_models_caret_pb = Attendant$new("predict_endpoint_models_caret_pb", hide_on_max = TRUE) - generate_research_questions_outcome_pb = Attendant$new("generate_research_questions_outcome_pb", hide_on_max = TRUE) - generate_research_questions_additional_analysis_pb = Attendant$new("generate_research_questions_additional_analysis_pb", hide_on_max = TRUE) - feature_engineering_perform_preprocess_pb = Attendant$new("feature_engineering_perform_preprocess_pb", hide_on_max = TRUE) - model_training_caret_metrics_download_all_zip_pb = Attendant$new("model_training_caret_metrics_download_all_zip_pb", hide_on_max = TRUE) - - #### ---- Input validators --------------------------------------------------- - source("server/input_validators.R") - #### ---- Create needed folders for datasets and logs ------------------------ - source("server/create_dirs.R", local=TRUE) - - #### ---- FastAPI base URL réactif (lié au champ fastapi_base) ---- - source("R/utils_logging.R") - - DEFAULT_API_BASE <- Sys.getenv("FASTAPI_BASE", "http://api:8000") - - source("server/automl_controls_server.R") - source("server/train_model_server.R") - source("R/utils_api.R") - source("server/deploy_model_server.R", local=TRUE) - source("ui/deploy_model_ui.R", local=TRUE) - source("server/predict_pycaret_server.R", local = TRUE) - - api_base <- reactive({ - val <- input$fastapi_base - if (is.null(val) || !nzchar(trimws(val))) { - DEFAULT_API_BASE - } else { - trimws(val) - } - }) - - - #### ---- Placeholder for reactive values ------------------------------------ - ##### -------- Currently selected dataset ------------------------------------ - rv_current = reactiveValues( - dataset_id = NULL - , metadata_id = NULL - , data = NULL - , selected_vars = NULL - , selected_var = NULL - , working_df = NULL - , current_filter = NULL - , current_filter_reset = NULL - , manage_data_title_explore = NULL - , missing_prop = NULL - , has_missing_data_check=FALSE - , manage_data_title_transform = NULL - , merge_data_title_merge = NULL - , transform_data_select_vars = NULL - , vartype = NULL - , changed_variable_type_log = NULL - , transform_data_plot_df = NULL - , renamed_variable_log = NULL - , transform_data_quick_plot_out = NULL - , recoded_variable_labels_log = NULL - , missing_prop_df = NULL - , created_missing_values_log = NULL - , outlier_values = NULL - , handle_missing_values_log = NULL - , handle_outlier_values_log = NULL - , transform_data_plot_missing_data_out = NULL - , quick_explore_summary = NULL - , max_tockens = 10000 - , seed = 9991 - , outcome = NULL - , vartype_all = NULL - ) - - #####------------------Plots Reactive------------------- - - plots_sec_rv <- reactiveValues( - plot_rv=NULL - ,tab_rv=NULL - ,plot_bivariate_auto=NULL - ,plot_corr = NULL - ) - - - ##### --------- Meta data --------------------------------------------- - rv_metadata = reactiveValues( - upload_logs = NULL - , dataset_ids = NULL - , data_summary_str = NULL - , data_summary_skim = NULL - , data_summary_summary = NULL - , data_summary_summarytools = NULL - ) - - rv_database <- reactiveValues(schema_list = NULL - , table_list = NULL - , conn = NULL - , schema_selected = NULL - , table_selected = NULL - , df_table = data.frame() - , df_table_str = NULL - , query_table_name = NULL - , database_host = NULL - , database_name = NULL - , database_user = NULL - , database_pass = NULL - , details = NULL - ) - - ## --- - - rv_omop<- reactiveValues( - url = NULL ) - - - ## LLM/GAI - rv_generative_ai = reactiveValues( - history = NULL - ) - - ## Reactive values for ML/AI module - rv_ml_ai = reactiveValues( - session_id = NULL - , seed_value = NULL - , dataset_id = NULL - , analysis_type = NULL - , task = NULL - , outcome = NULL - , model_formula = NULL - , partition_ratio = NULL - , predictors = NULL - , excluded_predictors = NULL - , ml_ai_setup_result = NULL - , history = NULL - , split = NULL - , train_df = NULL - , test_df = NULL - , preprocessed = NULL - , feature_engineering_preprocessed_log = NULL - , at_least_one_model = FALSE - ) - - ## RV to hold UIs - rv_ui_models = reactiveValues( - model_training_caret_models_ols_check = NULL - , model_training_caret_models_ols_advance_control = NULL - ) - - ## Train control caret - rv_train_control_caret = reactiveValues( - method = "cv" - , number = 5 - , repeats = NA - , search = "grid" - , verboseIter = FALSE - , savePredictions = FALSE - , classProbs = TRUE - ) - - ## Trained models - rv_training_models = reactiveValues( - ols_model = NULL - , ols_param = FALSE - , ols_name = NULL - , ols_trained_model = NULL - , rf_model = NULL - , rf_param = FALSE - , rf_name = NULL - , rf_trained_model = NULL - , all_trained_models = NULL - ) - - rv_training_results = reactiveValues( - models = NULL - , train_metrics_df = NULL - , test_metrics_objs = NULL - , post_model_metrics_objs = NULL - , control_parameters = NULL - , tuned_parameters = NULL - ) - - # Update training results when a new model is trained - automl_controls_server( - id = "automl_controls", - rv_current = rv_current, - rv_ml_ai = rv_ml_ai, - api_base = api_base - ) - - train_model_server( - id = "train_model", - rv_current = rv_current, - rv_ml_ai = rv_ml_ai, - api_base = api_base - ) - - # End update training - # ---- (A) Detect if a complete PyCaret run is available (leaderboard displayed) ---- - .can_show_by_train <- reactive({ - curr_ds <- rv_current$dataset_id %||% rv_ml_ai$dataset_id - isTRUE(rv_ml_ai$status %in% c("Finished","Finished_NoPlots")) && - !is.null(rv_ml_ai$leaderboard) && NROW(rv_ml_ai$leaderboard) > 0 && - isTRUE(nzchar(rv_ml_ai$trained_dataset_id)) && - identical(rv_ml_ai$trained_dataset_id, curr_ds) - }) - - observeEvent(rv_current$dataset_id, { - # If we change the dataset, we clean up the transient state linked to the previous train. - if (!identical(rv_current$dataset_id, rv_ml_ai$trained_dataset_id)) { - rv_ml_ai$leaderboard <- NULL - rv_ml_ai$leaderboard_full <- NULL - rv_ml_ai$test_leaderboard <- NULL - rv_ml_ai$test_leaderboard_full <- NULL - rv_ml_ai$models <- NULL - rv_ml_ai$eval_metrics <- NULL - rv_ml_ai$eval_plots <- NULL - rv_ml_ai$status <- NULL - } - }, ignoreInit = FALSE) - - - - # ---- (B) Datasets with pre-trained models (historical) ---- - .get_models_index_csv <- function() file.path(getwd(), app_username, "logs", "models", "index.csv") - dataset_has_history <- reactive({ - idx <- .get_models_index_csv() - if (!file.exists(idx)) return(FALSE) - df <- tryCatch(read.csv(idx, stringsAsFactors = FALSE), error = function(e) NULL) - if (is.null(df) || !"dataset_id" %in% names(df)) return(FALSE) - ds <- rv_ml_ai$dataset_id %||% rv_current$dataset_id - if (is.null(ds) || !nzchar(ds)) return(FALSE) - any(df$dataset_id == ds & (df$framework %in% c("PyCaret","pycaret","Pycaret"))) - }) - - # Expose known datasets for the Deploy module (used in its selector) - observe({ - idx <- .get_models_index_csv() - if (!file.exists(idx)) return(invisible(NULL)) - df <- tryCatch(read.csv(idx, stringsAsFactors = FALSE), error = function(e) NULL) - if (is.null(df) || !"dataset_id" %in% names(df)) return(invisible(NULL)) - rv_current$known_datasets <- sort(unique(df$dataset_id)) - }) - .can_show_deploy <- reactive({ - isTRUE(.can_show_by_train()) || isTRUE(dataset_has_history()) - }) - # (keep this if you still use it on the JS side) - output$can_show_deploy <- reactive({ .can_show_deploy() }) - outputOptions(output, "can_show_deploy", suspendWhenHidden = FALSE) - - # ---- Deploy tab UI container ---- - output$deploy_container <- renderUI({ - if (!isTRUE(.can_show_deploy())) return(NULL) # => onglet totalement vide - column(width = 12, deployment_ui("deploy")) - }) - - outputOptions(output, "can_show_deploy", suspendWhenHidden = FALSE) - ## Deployed models table - rv_deploy_models = reactiveValues( - trained_models_table = NULL - ) - - ## Deployed models - rv_deployed_models = reactiveValues() - - ## Reactive values to stock AutoML leaderboard - rv_automl <- reactiveValues( - leaderboard = NULL - ) - - #### ---- App title ---------------------------------------------------- - source("server/header_footer_configs.R", local=TRUE) - app_title() - - ###-------App Footer-------------------------- - - footer_language_translation() - ###-------Menu Translate--------- - - menu_translation() - - #### ---- Change language ---------------------------------------------------- - output$change_language = change_language - - source("server/change_language_update.R", local = TRUE) - change_language_update() - - #### ---- Upload data UI -------------------------------------------- - source("ui/upload_data.R", local = TRUE) - output$upload_type = upload_type - - #### ---- Upload dataset/files UI -------------------------------------------- - source("server/input_files.R", local = TRUE) - output$input_files = input_files - - #### ---- Show uploaded datasets UI -------------------------------------------- - output$show_uploaded = show_uploaded - - #### ---- Data upload form ----------------------------------------------- - source("ui/upload_form.R", local = TRUE) - output$study_name = study_name - output$study_country = study_country - output$additional_info = additional_info - output$submit_upload = submit_upload - - #### ---- Databse and API connection warning --------------------- - db_api_con_future - - #### ---- Upload datasets ---------------------------------------- - source("server/upload_data.R", local = TRUE) - upload_data_server() - - #### ---- Database integration ---------------------------------------- - source("server/database_integration.R", local = TRUE) - database_integration_server() - - #### --- Database related form elements ---### - output$db_type = db_type - output$db_host = db_host - output$db_name = db_name - output$db_user = db_user - output$db_pwd = db_pwd - output$db_connect = db_connect - output$db_schema_list = db_schema_list - output$db_table_list = db_table_list - output$db_custom_query = db_custom_query - output$db_run_query = db_run_query - output$db_port = db_port - output$db_disconnect = db_disconnect - output$db_tab_query = db_tab_query - output$existing_connection = existing_connection - - source("server/omop_analysis.R", local = TRUE) - omop_analysis_server() - - stderr_file_path <- file.path(getwd(), app_username, "output", "dq_stderr.txt") - - stderr_content<-create_log_reader(stderr_file_path) - - - #### ---- Collect logs ---------------------------------------- - source("server/collect_logs.R", local = TRUE) - collect_logs_server() - - #### ---- Display uploaded datasets ---------------------------------------- - source("server/display_uploaded_data.R", local = TRUE) - display_uploaded_data_server() - - #### ---- Delete uploaded dadatsets ---------------------------------------- - source("server/delete_uploaded_data.R", local = TRUE) - delete_uploaded_data_server() - - #### ---- Update logfiles based on existing datasets -------------------#### - source("server/update_logs.R", local = TRUE) - update_logs_server() - - #### ---- Manage data ---------------------------------------------- - - ##### ---- Select data --------------------------------------------- - source("server/select_data.R", local = TRUE) - select_data_server() - manage_data_show_server() - - ##### ---- Display meta data for the selected dataset --------------------------------------------- - source("server/display_metadata.R", local = TRUE) - display_selected_metadata_server() - reset_display_selected_metadata_server() - - ##### ---- Currently selected data --------------------------------------------- - source("server/selected_data.R", local = TRUE) - currently_selected_data_server() - - ##### ----Generate summary stats for the row data ------------------- - source("server/manage_data_summary.R", local = TRUE) - generate_data_summary_server() - display_data_summary_server() - - #### ----- Explore data ----------------------------------------------- - source("server/explore_data.R", local = TRUE) - explore_data_server() - explore_data_subactions_server() - - ##----User Defined Visualization section----------------------- - source("ui/user_defined_visualization_header.R", local = TRUE) - output$user_output_type = user_output_type - output$user_tab_options = user_tab_options - output$user_calc_var = user_calc_var - #output$user_strata_var = user_strata_var - output$user_row_var = user_row_var - output$usr_create_cross_tab = usr_create_cross_tab - output$user_download_table = user_download_table - - output$user_table_options = user_table_options - output$user_report_numeric = user_report_numeric - output$user_add_p_value = user_add_p_value - output$user_add_confidence_interval = user_add_confidence_interval - output$user_drop_missing_values = user_drop_missing_values - output$user_table_caption = user_table_caption - - output$user_plot_options = user_plot_options - output$user_select_variable_on_x_axis = user_select_variable_on_x_axis - output$user_select_variable_on_y_axis = user_select_variable_on_y_axis - output$user_plot_title = user_plot_title - output$user_x_axis_label = user_x_axis_label - output$user_y_axis_label = user_y_axis_label - output$user_create = user_create - output$user_download = user_download - - output$user_more_plot_options = user_more_plot_options - output$user_transform_to_doughnut = user_transform_to_doughnut - output$user_select_color_variable = user_select_color_variable - output$user_select_group_variable = user_select_group_variable - output$user_visual_orientation = user_visual_orientation - output$user_bar_width = user_bar_width - output$user_line_size = user_line_size - output$user_select_line_type = user_select_line_type - output$user_add_shapes = user_add_shapes - - output$user_select_shape = user_select_shape - output$user_add_smooth = user_add_smooth - output$user_display_confidence_interval = user_display_confidence_interval - output$user_level_of_confidence_interval = user_level_of_confidence_interval - output$user_select_line_join = user_select_line_join - output$user_add_line_type = user_add_line_type - output$user_add_points = user_add_points - output$user_y_variable_summary_type = user_y_variable_summary_type - output$user_title_position = user_title_position - - output$user_size_of_plot_title = user_size_of_plot_title - output$user_axis_title_size = user_axis_title_size - output$user_facet_title_size = user_facet_title_size - output$user_axis_text_size = user_axis_text_size - output$user_data_label_size = user_data_label_size - output$user_x_axis_text_angle = user_x_axis_text_angle - output$user_legend_title = user_legend_title - output$user_stacked = user_stacked - output$user_add_density = user_add_density - output$user_remove_histogram = user_remove_histogram - output$user_select_color_variable_single = user_select_color_variable_single - output$user_select_color_parlet = user_select_color_parlet - output$user_numeric_summary = user_numeric_summary - output$user_tab_more_out = user_tab_more_out - output$user_graph_more_out = user_tab_more_out - - output$bivariate_header_label = bivariate_header_label - output$corrplot_header_label = corrplot_header_label - - output$user_select_bivariate_single_color = user_select_bivariate_single_color - output$user_select_color_parlet_bivariate = user_select_color_parlet_bivariate - output$user_select_color_parlet_corrplot = user_select_color_parlet_corrplot - output$bivariate_plot_title = bivariate_plot_title - output$corrplot_title = corrplot_title - output$user_download_autoreport = user_download_autoreport - output$user_generatebivriate = user_generatebivriate - - - - ##### ---- Explore data actions ---------------------------------- - explore_data_actions_server() - - ##### ---- Filter data -------------------------------------------- - explore_data_filter_server() - explore_data_apply_filter_server() - explore_data_current_filter_server() - - ##### ---- Show/display ------------------------------------------------------- - explore_show_data_server() - explore_data_reset_current_filter_server() - - ##### ---- Compute proportion of missing data --------------------------- - explore_missing_data_server() - - ##### ---- Select variables --------------------------------------------- - explore_data_select_variables_server() - explore_data_selected_variables_server() - - ##### ---- Update data ----------------------------------------------- - explore_data_update_data_server() - - #### ---- Transform variables -------------------------------------- #### - source("server/transform_data.R", local = TRUE) - - ##### ---- Select variables to transform ------------------------------------### - transform_data_select_variables_server() - - ##### ---- Change type -----------------------------------------------### - transform_data_change_type_server() - - ##### ---- Rename variables -----------------------------------------------### - transform_data_rename_variables_server() - - ##### ---- Recode/change value labels ---------------------------------------### - transform_data_quick_explore_recode_server() - - ##### ---- Handle missing data ---------------------------------------### - transform_data_create_missing_values_server() - - ##### ---- Identify outliers ---------------------------------------### - transform_data_identify_outliers_server() - - ##### ---- Handle missing values ---------------------------------------### - transform_data_handle_missing_values_server() - - ##### ---- Plot transform data ----------------------------------------------### - transform_data_quick_explore_plot_server() - - ##### ---- Plot missing data ----------------------------------------------### - transform_data_plot_missing_data_server() - - #### ---- Combine datasets with the existing one --------------------------------------#### - source("server/combine_data.R", local = TRUE) - - ##### ---- List of internal data ------------------------------------------#### - combine_data_list_datasets() - - ##### ---- Combine data options ------------------------------------------#### - combine_data_type() - - ##### ---- Combine data mtch type ------------------------------------------#### - combine_data_match_type() - - ##### ---- Combine data variables matched --------------------#### - combine_data_variable_matching() - - #### ----- Perform matching ---------------------------------#### - combine_data_perform_variable_match() - - ##### ---- Combine data perform merging --------------------#### - combine_data_perform_merging() - - #### ---- Reset combine data --------------------------------#### - combine_data_reset() - - ##### ---- Control Custom visualizations ------------------ ##### - source("server/user_defined_visualization.R", local = TRUE) - user_defined_server() - - ### ------- OMOP ------------------------------------------ ##### - - #### ----- Cohort Constructor ---------##### - source("server/run_cohort_pipeline.R", local = TRUE) - run_cohort_pipeline() - - #### ----- Feature Extraction ---------##### - source("server/feature_extraction_pipeline.R", local = TRUE) - feature_extraction_pipeline() - - #### ---- Achilles Integration -------------------#### - - source("server/run_achilles.R", local = TRUE) - achilles_integration_server() - - ### ---- OMOP CDM Summaries---------------------------#### - source("server/omop_summaries.R", local = TRUE) - omopVizServer() - - #### ---- Generate Research Questions --------------------------------------#### - source("server/research_questions.R", local = TRUE) - generate_research_questions_choices() - - - ##### ---- API Token ------------------ #### - generate_research_questions_api_token() - generate_research_questions_api_store() - - #### ---- Addional prompts --------------- #### - generate_research_questions_additional() - - #### ---- Generate insights using Gemini --------------- #### - generate_research_questions_gemini() - - #### ---- Machine learning and AI --------------- #### - - ##### ----- Set ML/AI UI ------------------- #### - source("server/setup_models.R", local=TRUE) - setup_models_ui() - - ##### ----- Preprocessing ------------------- #### - source("server/feature_engineering.R", local=TRUE) - - #### Preprocessing ------------------------------------------- #### - feature_engineering_perform_preprocess_server() - - #### ------ Missing value imputation -------------------------- #### - feature_engineering_recipe_server() - feature_engineering_impute_missing_server() - - #### ----- Modelling framework --------------------------------- #### - - source("server/modelling_framework.R", local=TRUE) - modelling_framework_choices() - - #### ----- Model setup ----------------------------------------- #### - source("server/model_training_setup.R", local=TRUE) - model_training_setup_server() - - #### ----- Caret models --------------------------------------- #### - source("server/model_training_caret_models.R", local=TRUE) - - ## LM/GLM - model_training_caret_models_ols_server() - - ## RF - model_training_caret_models_rf_server() - - ## GBM - model_training_caret_models_gbm_server() - - ## xgbTree - model_training_caret_models_xgbTree_server() - - ## xgbLinear - model_training_caret_models_xgbLinear_server() - - ## svmRadial - model_training_caret_models_svmRadial_server() - - ## svmLinear - model_training_caret_models_svmLinear_server() - - ## svmPoly - model_training_caret_models_svmPoly_server() - - ## glmnet - model_training_caret_models_glmnet_server() - - ## LASSO - model_training_caret_models_lasso_server() - - ## Ridge - model_training_caret_models_ridge_server() - - ## KNN - model_training_caret_models_knn_server() - - ## NNET - model_training_caret_models_nnet_server() - - ## TREEBAG - model_training_caret_models_treebag_server() - - ## avNNet - model_training_caret_models_avNNet_server() - - ## PLS - model_training_caret_models_pls_server() - - ## GAM - model_training_caret_models_gam_server() - - #### ----- Train all models ----------------------------------- #### - source("server/train_caret_models.R", local=TRUE) - model_training_caret_train_all_server() - - #### ----- Compare trained models ------------------------------ #### - source("server/compare_trained_caret_models.R", local=TRUE) - model_training_caret_train_metrics_server() - - #### ----- Deploy trained models ------------------------------- #### - source("server/deploy_trained_caret_models.R", local=TRUE) - deploy_trained_caret_models() - - #### ---- Predict using no-code models ------------------------ #### - source("server/predict_trained_caret_models.R", local=TRUE) - predict_trained_caret_models() - - #### ---- PyCaret Integration (API) ---------------------------------------------------- - - # New ADD - rv_ml_ai <- rv_ml_ai %||% reactiveValues(target = NULL, outcome = NULL) - rv_current <- rv_current %||% reactiveValues(target = NULL) - - deployment_server(id="deploy",rv_ml_ai=rv_ml_ai,rv_current = rv_current,api_base=api_base) - predict_pycaret_server("predict_pycaret", api_base , rv_current, rv_ml_ai) - - # END NEW ADD - #### ---- Call current dataset for FastAPI --------------------------------------------------- - source("server/automl_server.R", local=TRUE) - automl_server("automl_module", rv_current, rv_ml_ai) - - observe({ - req(!is.null(rv_ml_ai$modelling_framework)) # Check if value exist - - if (tolower(rv_ml_ai$modelling_framework) == "pycaret") { - output$automl_module_ui <- renderUI({ - automl_ui("automl_module") - }) - } else { - output$automl_module_ui <- renderUI({ - h4("") - }) - } - }) - - observeEvent(input$modelling_framework_choices, { - rv_ml_ai$framework <- tolower(input$modelling_framework_choices %||% "") - }, ignoreInit = FALSE) - - #### ---- Deep Learning Server ----- ### - source("server/deep_learning.R", local=TRUE) - deep_learning() - - #### ---- Reset various components --------------------------------------#### - ## Various components come before this - source("server/resets.R", local = TRUE) - - ##### ---- Reset on delete or language change ------------------- #### - reset_data_server() - - #### ---- Activate required fields --------------------------------------#### - iv$enable() - iv_url$enable() - iv_ml$enable() - - waiter_hide() + observeEvent(USER$logged_in, { + req(isTRUE(USER$logged_in)) + waiter_show( + html = spin_loaders(id = 3, style="width:56px;height:56px;color:#7BC148;"), + color = "#FFF" + ) + + if (authed_started()) return() + authed_started(TRUE) + + app_username = USER$username + + # ---- Anonymization sources ---- + source("modules/anonymization_functions.R", local = FALSE) + source("server/anon/server_module_quant.R", local = FALSE) + source("modules/mod_quant_anonymization.R", local = FALSE) + # ---- ---- + + model_training_caret_pb = Attendant$new("model_training_caret_pb", hide_on_max = TRUE) + data_upload_id_pb = Attendant$new("data_upload_id_pb", hide_on_max = TRUE) + model_metrics_caret_pb = Attendant$new("model_metrics_caret_pb", hide_on_max = TRUE) + deploy_models_caret_pb = Attendant$new("deploy_models_caret_pb", hide_on_max = TRUE) + predict_models_caret_pb = Attendant$new("predict_models_caret_pb", hide_on_max = TRUE) + predict_endpoint_models_caret_pb = Attendant$new("predict_endpoint_models_caret_pb", hide_on_max = TRUE) + generate_research_questions_outcome_pb = Attendant$new("generate_research_questions_outcome_pb", hide_on_max = TRUE) + generate_research_questions_additional_analysis_pb = Attendant$new("generate_research_questions_additional_analysis_pb", hide_on_max = TRUE) + feature_engineering_perform_preprocess_pb = Attendant$new("feature_engineering_perform_preprocess_pb", hide_on_max = TRUE) + model_training_caret_metrics_download_all_zip_pb = Attendant$new("model_training_caret_metrics_download_all_zip_pb", hide_on_max = TRUE) + + #### ---- Input validators --------------------------------------------------- + source("server/input_validators.R") + #### ---- Create needed folders for datasets and logs ------------------------ + source("server/create_dirs.R", local=TRUE) + + #### ---- FastAPI base URL réactif (lié au champ fastapi_base) ---- + source("R/utils_logging.R") + + DEFAULT_API_BASE <- Sys.getenv("FASTAPI_BASE", "http://api:8000") + + source("server/automl_controls_server.R") + source("server/train_model_server.R") + source("R/utils_api.R") + source("server/deploy_model_server.R", local=TRUE) + source("ui/deploy_model_ui.R", local=TRUE) + source("server/predict_pycaret_server.R", local = TRUE) + + api_base <- reactive({ + val <- input$fastapi_base + if (is.null(val) || !nzchar(trimws(val))) { + DEFAULT_API_BASE + } else { + trimws(val) + } + }) + + + #### ---- Placeholder for reactive values ------------------------------------ + ##### -------- Currently selected dataset ------------------------------------ + rv_current = reactiveValues( + dataset_id = NULL + , metadata_id = NULL + , data = NULL + , selected_vars = NULL + , selected_var = NULL + , working_df = NULL + , current_filter = NULL + , current_filter_reset = NULL + , manage_data_title_explore = NULL + , missing_prop = NULL + , has_missing_data_check=FALSE + , manage_data_title_transform = NULL + , merge_data_title_merge = NULL + , transform_data_select_vars = NULL + , vartype = NULL + , changed_variable_type_log = NULL + , transform_data_plot_df = NULL + , renamed_variable_log = NULL + , transform_data_quick_plot_out = NULL + , recoded_variable_labels_log = NULL + , missing_prop_df = NULL + , created_missing_values_log = NULL + , outlier_values = NULL + , handle_missing_values_log = NULL + , handle_outlier_values_log = NULL + , transform_data_plot_missing_data_out = NULL + , quick_explore_summary = NULL + , max_tockens = 10000 + , seed = 9991 + , outcome = NULL + , vartype_all = NULL + ) + + # ---- Anonymization module server ---- + if (exists("mod_quant_anon_server", mode = "function", inherits = TRUE)) { + mod_quant_anon_server("quant_anon", rv_current = rv_current) + } else { + showNotification("mod_quant_anon_server() not found. Check modules/mod_quant_anonymization.R", type = "error") + } + # ---- ---- + + #####------------------Plots Reactive------------------- + + plots_sec_rv <- reactiveValues( + plot_rv=NULL + ,tab_rv=NULL + ,plot_bivariate_auto=NULL + ,plot_corr = NULL + ) + + + ##### --------- Meta data --------------------------------------------- + rv_metadata = reactiveValues( + upload_logs = NULL + , dataset_ids = NULL + , data_summary_str = NULL + , data_summary_skim = NULL + , data_summary_summary = NULL + , data_summary_summarytools = NULL + ) + + rv_database <- reactiveValues(schema_list = NULL + , table_list = NULL + , conn = NULL + , schema_selected = NULL + , table_selected = NULL + , df_table = data.frame() + , df_table_str = NULL + , query_table_name = NULL + , database_host = NULL + , database_name = NULL + , database_user = NULL + , database_pass = NULL + , details = NULL + ) + + ## --- + + rv_omop<- reactiveValues( + url = NULL ) + + + ## LLM/GAI + rv_generative_ai = reactiveValues( + history = NULL + ) + + ## Reactive values for ML/AI module + rv_ml_ai = reactiveValues( + session_id = NULL + , seed_value = NULL + , dataset_id = NULL + , analysis_type = NULL + , task = NULL + , outcome = NULL + , model_formula = NULL + , partition_ratio = NULL + , predictors = NULL + , excluded_predictors = NULL + , ml_ai_setup_result = NULL + , history = NULL + , split = NULL + , train_df = NULL + , test_df = NULL + , preprocessed = NULL + , feature_engineering_preprocessed_log = NULL + , at_least_one_model = FALSE + ) + + ## RV to hold UIs + rv_ui_models = reactiveValues( + model_training_caret_models_ols_check = NULL + , model_training_caret_models_ols_advance_control = NULL + ) + + ## Train control caret + rv_train_control_caret = reactiveValues( + method = "cv" + , number = 5 + , repeats = NA + , search = "grid" + , verboseIter = FALSE + , savePredictions = FALSE + , classProbs = TRUE + ) + + ## Trained models + rv_training_models = reactiveValues( + ols_model = NULL + , ols_param = FALSE + , ols_name = NULL + , ols_trained_model = NULL + , rf_model = NULL + , rf_param = FALSE + , rf_name = NULL + , rf_trained_model = NULL + , all_trained_models = NULL + ) + + rv_training_results = reactiveValues( + models = NULL + , train_metrics_df = NULL + , test_metrics_objs = NULL + , post_model_metrics_objs = NULL + , control_parameters = NULL + , tuned_parameters = NULL + ) + + # Update training results when a new model is trained + automl_controls_server( + id = "automl_controls", + rv_current = rv_current, + rv_ml_ai = rv_ml_ai, + api_base = api_base + ) + + train_model_server( + id = "train_model", + rv_current = rv_current, + rv_ml_ai = rv_ml_ai, + api_base = api_base + ) + + # End update training + # ---- (A) Detect if a complete PyCaret run is available (leaderboard displayed) ---- + .can_show_by_train <- reactive({ + curr_ds <- rv_current$dataset_id %||% rv_ml_ai$dataset_id + isTRUE(rv_ml_ai$status %in% c("Finished","Finished_NoPlots")) && + !is.null(rv_ml_ai$leaderboard) && NROW(rv_ml_ai$leaderboard) > 0 && + isTRUE(nzchar(rv_ml_ai$trained_dataset_id)) && + identical(rv_ml_ai$trained_dataset_id, curr_ds) + }) + + observeEvent(rv_current$dataset_id, { + # If we change the dataset, we clean up the transient state linked to the previous train. + if (!identical(rv_current$dataset_id, rv_ml_ai$trained_dataset_id)) { + rv_ml_ai$leaderboard <- NULL + rv_ml_ai$leaderboard_full <- NULL + rv_ml_ai$test_leaderboard <- NULL + rv_ml_ai$test_leaderboard_full <- NULL + rv_ml_ai$models <- NULL + rv_ml_ai$eval_metrics <- NULL + rv_ml_ai$eval_plots <- NULL + rv_ml_ai$status <- NULL + } + }, ignoreInit = FALSE) + + + + # ---- (B) Datasets with pre-trained models (historical) ---- + .get_models_index_csv <- function() file.path(getwd(), app_username, "logs", "models", "index.csv") + dataset_has_history <- reactive({ + idx <- .get_models_index_csv() + if (!file.exists(idx)) return(FALSE) + df <- tryCatch(read.csv(idx, stringsAsFactors = FALSE), error = function(e) NULL) + if (is.null(df) || !"dataset_id" %in% names(df)) return(FALSE) + ds <- rv_ml_ai$dataset_id %||% rv_current$dataset_id + if (is.null(ds) || !nzchar(ds)) return(FALSE) + any(df$dataset_id == ds & (df$framework %in% c("PyCaret","pycaret","Pycaret"))) + }) + + # Expose known datasets for the Deploy module (used in its selector) + observe({ + idx <- .get_models_index_csv() + if (!file.exists(idx)) return(invisible(NULL)) + df <- tryCatch(read.csv(idx, stringsAsFactors = FALSE), error = function(e) NULL) + if (is.null(df) || !"dataset_id" %in% names(df)) return(invisible(NULL)) + rv_current$known_datasets <- sort(unique(df$dataset_id)) + }) + .can_show_deploy <- reactive({ + isTRUE(.can_show_by_train()) || isTRUE(dataset_has_history()) + }) + # (keep this if you still use it on the JS side) + output$can_show_deploy <- reactive({ .can_show_deploy() }) + outputOptions(output, "can_show_deploy", suspendWhenHidden = FALSE) + + # ---- Deploy tab UI container ---- + output$deploy_container <- renderUI({ + if (!isTRUE(.can_show_deploy())) return(NULL) # => onglet totalement vide + column(width = 12, deployment_ui("deploy")) + }) + + outputOptions(output, "can_show_deploy", suspendWhenHidden = FALSE) + ## Deployed models table + rv_deploy_models = reactiveValues( + trained_models_table = NULL + ) + + ## Deployed models + rv_deployed_models = reactiveValues() + + ## Reactive values to stock AutoML leaderboard + rv_automl <- reactiveValues( + leaderboard = NULL + ) + + #### ---- App title ---------------------------------------------------- + source("server/header_footer_configs.R", local=TRUE) + app_title() + + ###-------App Footer-------------------------- + + footer_language_translation() + ###-------Menu Translate--------- + + menu_translation() + + #### ---- Change language ---------------------------------------------------- + output$change_language = change_language + + source("server/change_language_update.R", local = TRUE) + change_language_update() + + #### ---- Upload data UI -------------------------------------------- + source("ui/upload_data.R", local = TRUE) + output$upload_type = upload_type + + #### ---- Upload dataset/files UI -------------------------------------------- + source("server/input_files.R", local = TRUE) + output$input_files = input_files + + #### ---- Show uploaded datasets UI -------------------------------------------- + output$show_uploaded = show_uploaded + + #### ---- Data upload form ----------------------------------------------- + source("ui/upload_form.R", local = TRUE) + output$study_name = study_name + output$study_country = study_country + output$additional_info = additional_info + output$submit_upload = submit_upload + + #### ---- Databse and API connection warning --------------------- + db_api_con_future + + #### ---- Upload datasets ---------------------------------------- + source("server/upload_data.R", local = TRUE) + upload_data_server() + + #### ---- Database integration ---------------------------------------- + source("server/database_integration.R", local = TRUE) + database_integration_server() + + #### --- Database related form elements ---### + output$db_type = db_type + output$db_host = db_host + output$db_name = db_name + output$db_user = db_user + output$db_pwd = db_pwd + output$db_connect = db_connect + output$db_schema_list = db_schema_list + output$db_table_list = db_table_list + output$db_custom_query = db_custom_query + output$db_run_query = db_run_query + output$db_port = db_port + output$db_disconnect = db_disconnect + output$db_tab_query = db_tab_query + output$existing_connection = existing_connection + + source("server/omop_analysis.R", local = TRUE) + omop_analysis_server() + + stderr_file_path <- file.path(getwd(), app_username, "output", "dq_stderr.txt") + + stderr_content<-create_log_reader(stderr_file_path) + + + #### ---- Collect logs ---------------------------------------- + source("server/collect_logs.R", local = TRUE) + collect_logs_server() + + #### ---- Display uploaded datasets ---------------------------------------- + source("server/display_uploaded_data.R", local = TRUE) + display_uploaded_data_server() + + #### ---- Delete uploaded dadatsets ---------------------------------------- + source("server/delete_uploaded_data.R", local = TRUE) + delete_uploaded_data_server() + + #### ---- Update logfiles based on existing datasets -------------------#### + source("server/update_logs.R", local = TRUE) + update_logs_server() + + #### ---- Manage data ---------------------------------------------- + + ##### ---- Select data --------------------------------------------- + source("server/select_data.R", local = TRUE) + select_data_server() + manage_data_show_server() + + ##### ---- Display meta data for the selected dataset --------------------------------------------- + source("server/display_metadata.R", local = TRUE) + display_selected_metadata_server() + reset_display_selected_metadata_server() + + ##### ---- Currently selected data --------------------------------------------- + source("server/selected_data.R", local = TRUE) + currently_selected_data_server() + + ##### ----Generate summary stats for the row data ------------------- + source("server/manage_data_summary.R", local = TRUE) + generate_data_summary_server() + display_data_summary_server() + + #### ----- Explore data ----------------------------------------------- + source("server/explore_data.R", local = TRUE) + explore_data_server() + explore_data_subactions_server() + + ##----User Defined Visualization section----------------------- + source("ui/user_defined_visualization_header.R", local = TRUE) + output$user_output_type = user_output_type + output$user_tab_options = user_tab_options + output$user_calc_var = user_calc_var + #output$user_strata_var = user_strata_var + output$user_row_var = user_row_var + output$usr_create_cross_tab = usr_create_cross_tab + output$user_download_table = user_download_table + + output$user_table_options = user_table_options + output$user_report_numeric = user_report_numeric + output$user_add_p_value = user_add_p_value + output$user_add_confidence_interval = user_add_confidence_interval + output$user_drop_missing_values = user_drop_missing_values + output$user_table_caption = user_table_caption + + output$user_plot_options = user_plot_options + output$user_select_variable_on_x_axis = user_select_variable_on_x_axis + output$user_select_variable_on_y_axis = user_select_variable_on_y_axis + output$user_plot_title = user_plot_title + output$user_x_axis_label = user_x_axis_label + output$user_y_axis_label = user_y_axis_label + output$user_create = user_create + output$user_download = user_download + + output$user_more_plot_options = user_more_plot_options + output$user_transform_to_doughnut = user_transform_to_doughnut + output$user_select_color_variable = user_select_color_variable + output$user_select_group_variable = user_select_group_variable + output$user_visual_orientation = user_visual_orientation + output$user_bar_width = user_bar_width + output$user_line_size = user_line_size + output$user_select_line_type = user_select_line_type + output$user_add_shapes = user_add_shapes + + output$user_select_shape = user_select_shape + output$user_add_smooth = user_add_smooth + output$user_display_confidence_interval = user_display_confidence_interval + output$user_level_of_confidence_interval = user_level_of_confidence_interval + output$user_select_line_join = user_select_line_join + output$user_add_line_type = user_add_line_type + output$user_add_points = user_add_points + output$user_y_variable_summary_type = user_y_variable_summary_type + output$user_title_position = user_title_position + + output$user_size_of_plot_title = user_size_of_plot_title + output$user_axis_title_size = user_axis_title_size + output$user_facet_title_size = user_facet_title_size + output$user_axis_text_size = user_axis_text_size + output$user_data_label_size = user_data_label_size + output$user_x_axis_text_angle = user_x_axis_text_angle + output$user_legend_title = user_legend_title + output$user_stacked = user_stacked + output$user_add_density = user_add_density + output$user_remove_histogram = user_remove_histogram + output$user_select_color_variable_single = user_select_color_variable_single + output$user_select_color_parlet = user_select_color_parlet + output$user_numeric_summary = user_numeric_summary + output$user_tab_more_out = user_tab_more_out + output$user_graph_more_out = user_tab_more_out + + output$bivariate_header_label = bivariate_header_label + output$corrplot_header_label = corrplot_header_label + + output$user_select_bivariate_single_color = user_select_bivariate_single_color + output$user_select_color_parlet_bivariate = user_select_color_parlet_bivariate + output$user_select_color_parlet_corrplot = user_select_color_parlet_corrplot + output$bivariate_plot_title = bivariate_plot_title + output$corrplot_title = corrplot_title + output$user_download_autoreport = user_download_autoreport + output$user_generatebivriate = user_generatebivriate + + + + ##### ---- Explore data actions ---------------------------------- + explore_data_actions_server() + + ##### ---- Filter data -------------------------------------------- + explore_data_filter_server() + explore_data_apply_filter_server() + explore_data_current_filter_server() + + ##### ---- Show/display ------------------------------------------------------- + explore_show_data_server() + explore_data_reset_current_filter_server() + + ##### ---- Compute proportion of missing data --------------------------- + explore_missing_data_server() + + ##### ---- Select variables --------------------------------------------- + explore_data_select_variables_server() + explore_data_selected_variables_server() + + ##### ---- Update data ----------------------------------------------- + explore_data_update_data_server() + + #### ---- Transform variables -------------------------------------- #### + source("server/transform_data.R", local = TRUE) + + ##### ---- Select variables to transform ------------------------------------### + transform_data_select_variables_server() + + ##### ---- Change type -----------------------------------------------### + transform_data_change_type_server() + + ##### ---- Rename variables -----------------------------------------------### + transform_data_rename_variables_server() + + ##### ---- Recode/change value labels ---------------------------------------### + transform_data_quick_explore_recode_server() + + ##### ---- Handle missing data ---------------------------------------### + transform_data_create_missing_values_server() + + ##### ---- Identify outliers ---------------------------------------### + transform_data_identify_outliers_server() + + ##### ---- Handle missing values ---------------------------------------### + transform_data_handle_missing_values_server() + + ##### ---- Plot transform data ----------------------------------------------### + transform_data_quick_explore_plot_server() + + ##### ---- Plot missing data ----------------------------------------------### + transform_data_plot_missing_data_server() + + #### ---- Combine datasets with the existing one --------------------------------------#### + source("server/combine_data.R", local = TRUE) + + ##### ---- List of internal data ------------------------------------------#### + combine_data_list_datasets() + + ##### ---- Combine data options ------------------------------------------#### + combine_data_type() + + ##### ---- Combine data mtch type ------------------------------------------#### + combine_data_match_type() + + ##### ---- Combine data variables matched --------------------#### + combine_data_variable_matching() + + #### ----- Perform matching ---------------------------------#### + combine_data_perform_variable_match() + + ##### ---- Combine data perform merging --------------------#### + combine_data_perform_merging() + + #### ---- Reset combine data --------------------------------#### + combine_data_reset() + + ##### ---- Control Custom visualizations ------------------ ##### + source("server/user_defined_visualization.R", local = TRUE) + user_defined_server() + + ### ------- OMOP ------------------------------------------ ##### + + #### ----- Cohort Constructor ---------##### + source("server/run_cohort_pipeline.R", local = TRUE) + run_cohort_pipeline() + + #### ----- Feature Extraction ---------##### + source("server/feature_extraction_pipeline.R", local = TRUE) + feature_extraction_pipeline() + + #### ---- Achilles Integration -------------------#### + + source("server/run_achilles.R", local = TRUE) + achilles_integration_server() + + ### ---- OMOP CDM Summaries---------------------------#### + source("server/omop_summaries.R", local = TRUE) + omopVizServer() + + #### ---- Generate Research Questions --------------------------------------#### + source("server/research_questions.R", local = TRUE) + generate_research_questions_choices() + + + ##### ---- API Token ------------------ #### + generate_research_questions_api_token() + generate_research_questions_api_store() + + #### ---- Addional prompts --------------- #### + generate_research_questions_additional() + + #### ---- Generate insights using Gemini --------------- #### + generate_research_questions_gemini() + + #### ---- Machine learning and AI --------------- #### + + ##### ----- Set ML/AI UI ------------------- #### + source("server/setup_models.R", local=TRUE) + setup_models_ui() + + ##### ----- Preprocessing ------------------- #### + source("server/feature_engineering.R", local=TRUE) + + #### Preprocessing ------------------------------------------- #### + feature_engineering_perform_preprocess_server() + + #### ------ Missing value imputation -------------------------- #### + feature_engineering_recipe_server() + feature_engineering_impute_missing_server() + + #### ----- Modelling framework --------------------------------- #### + + source("server/modelling_framework.R", local=TRUE) + modelling_framework_choices() + + #### ----- Model setup ----------------------------------------- #### + source("server/model_training_setup.R", local=TRUE) + model_training_setup_server() + + #### ----- Caret models --------------------------------------- #### + source("server/model_training_caret_models.R", local=TRUE) + + ## LM/GLM + model_training_caret_models_ols_server() + + ## RF + model_training_caret_models_rf_server() + + ## GBM + model_training_caret_models_gbm_server() + + ## xgbTree + model_training_caret_models_xgbTree_server() + + ## xgbLinear + model_training_caret_models_xgbLinear_server() + + ## svmRadial + model_training_caret_models_svmRadial_server() + + ## svmLinear + model_training_caret_models_svmLinear_server() + + ## svmPoly + model_training_caret_models_svmPoly_server() + + ## glmnet + model_training_caret_models_glmnet_server() + + ## LASSO + model_training_caret_models_lasso_server() + + ## Ridge + model_training_caret_models_ridge_server() + + ## KNN + model_training_caret_models_knn_server() + + ## NNET + model_training_caret_models_nnet_server() + + ## TREEBAG + model_training_caret_models_treebag_server() + + ## avNNet + model_training_caret_models_avNNet_server() + + ## PLS + model_training_caret_models_pls_server() + + ## GAM + model_training_caret_models_gam_server() + + #### ----- Train all models ----------------------------------- #### + source("server/train_caret_models.R", local=TRUE) + model_training_caret_train_all_server() + + #### ----- Compare trained models ------------------------------ #### + source("server/compare_trained_caret_models.R", local=TRUE) + model_training_caret_train_metrics_server() + + #### ----- Deploy trained models ------------------------------- #### + source("server/deploy_trained_caret_models.R", local=TRUE) + deploy_trained_caret_models() + + #### ---- Predict using no-code models ------------------------ #### + source("server/predict_trained_caret_models.R", local=TRUE) + predict_trained_caret_models() + + #### ---- PyCaret Integration (API) ---------------------------------------------------- + + # New ADD + rv_ml_ai <- rv_ml_ai %||% reactiveValues(target = NULL, outcome = NULL) + rv_current <- rv_current %||% reactiveValues(target = NULL) + + deployment_server(id="deploy",rv_ml_ai=rv_ml_ai,rv_current = rv_current,api_base=api_base) + predict_pycaret_server("predict_pycaret", api_base , rv_current, rv_ml_ai) + + # END NEW ADD + #### ---- Call current dataset for FastAPI --------------------------------------------------- + source("server/automl_server.R", local=TRUE) + automl_server("automl_module", rv_current, rv_ml_ai) + + observe({ + req(!is.null(rv_ml_ai$modelling_framework)) # Check if value exist + + if (tolower(rv_ml_ai$modelling_framework) == "pycaret") { + output$automl_module_ui <- renderUI({ + automl_ui("automl_module") + }) + } else { + output$automl_module_ui <- renderUI({ + h4("") + }) + } + }) + + observeEvent(input$modelling_framework_choices, { + rv_ml_ai$framework <- tolower(input$modelling_framework_choices %||% "") + }, ignoreInit = FALSE) + + #### ---- Deep Learning Server ----- ### + source("server/deep_learning.R", local=TRUE) + deep_learning() + + #### ---- Reset various components --------------------------------------#### + ## Various components come before this + source("server/resets.R", local = TRUE) + + ##### ---- Reset on delete or language change ------------------- #### + reset_data_server() + + #### ---- Activate required fields --------------------------------------#### + iv$enable() + iv_url$enable() + iv_ml$enable() + + waiter_hide() }, ignoreInit = FALSE) - + waiter_hide() } diff --git a/server/anon/server_module_quant.R b/server/anon/server_module_quant.R new file mode 100644 index 0000000..23d55b9 --- /dev/null +++ b/server/anon/server_module_quant.R @@ -0,0 +1,1687 @@ +# server_module_quant.R + +anon_quant_server_logic <- function(input, output, session, rv_current = NULL) { + + # ====================================================================== + # Quant Anon — Module-safe server with coordinate-safe risk + QGIS export + # ====================================================================== + + options(shiny.maxRequestSize = 1024^3) + + # ---- DEBUG: show full stack traces inside Shiny ---- + options(shiny.fullstacktrace = TRUE) + + if (requireNamespace("rlang", quietly = TRUE)) { + options(error = function() { + message("\n--- ERROR TRACE (rlang::last_trace) ---\n") + try(print(rlang::last_trace()), silent = TRUE) + message("\n--- BASE TRACEBACK() ---\n") + traceback(2) + }) + } else { + options(error = function() { + message("\n--- BASE TRACEBACK() ---\n") + traceback(2) + }) + } + + ns <- session$ns # IMPORTANT: used for dynamic UI ids created in renderUI() + + `%||%` <- function(a, b) if (is.null(a)) b else a + + # CRITICAL FIX: avoid htmlwidgets::validate masking shiny::validate + vld <- shiny::validate + need <- shiny::need + + get_after_col <- function(q, df_after) { + anon_col <- paste0(q, "_anon") + if (anon_col %in% names(df_after)) anon_col else q + } + + # (IDs kept harmlessly for backwards-compat; landing is removed in UI) + landing_dom <- ns("landing") + dashboard_dom <- ns("dashboard") + + shinyjs::inlineCSS(" + .gauge-value { display: none !important; } + #preview-table { padding-right: 0 !important; } + ") + + # ---------- Helpers ----------------------------------------------------- + parse_ranges_text <- function(text) { + entries <- unlist(strsplit(text, ";", fixed = TRUE)) + parsed <- lapply(entries, function(e) { + e <- trimws(e) + if (e == "") return(NULL) + + parts <- strsplit(e, ":", fixed = TRUE)[[1]] + if (length(parts) != 2) stop(sprintf("Invalid entry '%s': must be of form lower-upper:label", e)) + + range_part <- trimws(parts[1]) + label <- trimws(parts[2]) + if (label == "") stop(sprintf("Label missing in entry '%s'", e)) + + bounds <- strsplit(range_part, "-", fixed = TRUE)[[1]] + if (length(bounds) == 1) { + num <- as.numeric(bounds[1]) + if (is.na(num)) stop(sprintf("Non-numeric bound in '%s'", range_part)) + lower <- num + upper <- num + } else if (length(bounds) == 2) { + lower_raw <- trimws(bounds[1]) + upper_raw <- trimws(bounds[2]) + lower <- if (lower_raw == "" || lower_raw == "-Inf") -Inf else as.numeric(lower_raw) + upper <- if (upper_raw == "" || upper_raw == "Inf") Inf else as.numeric(upper_raw) + if (is.na(lower) || is.na(upper)) stop(sprintf("Non-numeric bound in '%s'", range_part)) + } else { + stop(sprintf("Range part '%s' is malformed", range_part)) + } + + if (lower > upper) stop(sprintf("Range '%s' has lower > upper", label)) + list(lower = lower, upper = upper, label = label) + }) + + parsed <- Filter(Negate(is.null), parsed) + if (length(parsed) == 0) stop("No valid ranges found.") + + df <- do.call(rbind, lapply(parsed, function(p) { + data.frame(lower = p$lower, upper = p$upper, label = p$label, stringsAsFactors = FALSE) + })) + + df <- df[order(df$lower, df$upper), ] + if (nrow(df) > 1) { + for (i in seq_len(nrow(df) - 1)) { + if (df$upper[i] >= df$lower[i + 1]) { + stop(sprintf( + "Ranges '%s' and '%s' overlap or touch; make upper of one < lower of next.", + df$label[i], df$label[i + 1] + )) + } + } + } + df + } + + is_finite_bbox <- function(bbox) { + if (is.null(bbox)) return(FALSE) + all(is.finite(unname(as.numeric(bbox)))) + } + + nz_pos <- function(x, default = 100) { + x2 <- suppressWarnings(as.numeric(x)) + if (length(x2) == 0 || is.na(x2) || !is.finite(x2) || x2 <= 0) default else x2 + } + + is_geo_name <- function(nm) { + tolower(nm) %in% c("lat", "latitude", "lon", "long", "longitude", "geom", "geometry", + "wkt", "cell_wkt", "polygon") + } + + risk_qids <- function(qids) { + qids[!sapply(qids, is_geo_name)] + } + + drop_geo_cols <- function(dat) { + drop <- intersect(names(dat), names(dat)[sapply(names(dat), is_geo_name)]) + drop <- setdiff(drop, c("cell_wkt", "wkt", "polygon")) + dat[, setdiff(names(dat), drop), drop = FALSE] + } + + # ----- CRS safety layer ------------------------------------------------- + is_valid_utm_epsg <- function(epsg) { + if (is.na(epsg)) return(FALSE) + epsg %in% c(3857, 4326) || + (epsg >= 32601 && epsg <= 32660) || + (epsg >= 32701 && epsg <= 32760) + } + + guess_utm_epsg <- function(lon, lat) { + lon <- lon[is.finite(lon)] + lat <- lat[is.finite(lat)] + if (!length(lon) || !length(lat)) return(3857) + zone <- floor((mean(lon) + 180) / 6) + 1 + epsg <- if (mean(lat) >= 0) 32600 + zone else 32700 + zone + if (!is_valid_utm_epsg(epsg)) 3857 else epsg + } + + safe_transform <- function(obj, target_epsg) { + if (inherits(obj, "sf") && is.na(sf::st_crs(obj))) { + suppressWarnings(sf::st_crs(obj) <- 4326) + } + if (!is_valid_utm_epsg(target_epsg)) target_epsg <- 3857 + + out <- try(suppressWarnings(sf::st_transform(obj, target_epsg)), silent = TRUE) + if (inherits(out, "try-error")) { + out <- try(suppressWarnings(sf::st_transform(obj, 4326)), silent = TRUE) + if (inherits(out, "try-error")) { + out <- try(suppressWarnings(sf::st_transform(obj, 3857)), silent = TRUE) + if (inherits(out, "try-error")) return(obj) + } + } + out + } + + build_grid_agg <- function(pts_utm, cell_m) { + if (!inherits(pts_utm, "sf")) stop("pts_utm must be sf") + if (!inherits(sf::st_geometry(pts_utm), "sfc_POINT")) stop("pts_utm must be POINT geometries") + if (is.na(sf::st_crs(pts_utm))) sf::st_crs(pts_utm) <- 3857 + + cell_m <- nz_pos(cell_m, default = 100) + + bbox <- suppressWarnings(sf::st_bbox(pts_utm)) + if (!is_finite_bbox(bbox)) { + return(sf::st_sf(geometry = sf::st_sfc(), n = integer(0), crs = sf::st_crs(pts_utm))) + } + + grd <- try(sf::st_make_grid(pts_utm, cellsize = c(cell_m, cell_m), square = TRUE), silent = TRUE) + if (inherits(grd, "try-error") || length(grd) == 0) { + return(sf::st_sf(geometry = sf::st_sfc(), n = integer(0), crs = sf::st_crs(pts_utm))) + } + + grd_sf <- sf::st_sf(geometry = grd) + sf::st_crs(grd_sf) <- sf::st_crs(pts_utm) + + ints <- try(sf::st_intersects(grd_sf, pts_utm), silent = TRUE) + if (inherits(ints, "try-error")) { + return(sf::st_sf(geometry = sf::st_sfc(), n = integer(0), crs = sf::st_crs(pts_utm))) + } + + grd_sf$n <- lengths(ints) + grd_sf <- grd_sf[grd_sf$n > 0, , drop = FALSE] + grd_sf <- sf::st_make_valid(grd_sf) + grd_sf <- sf::st_zm(grd_sf, drop = TRUE, what = "ZM") + if (nrow(grd_sf)) grd_sf <- grd_sf[!sf::st_is_empty(grd_sf), , drop = FALSE] + + if (!nrow(grd_sf)) { + return(sf::st_sf(geometry = sf::st_sfc(), n = integer(0), crs = sf::st_crs(pts_utm))) + } + grd_sf + } + + if (requireNamespace("sf", quietly = TRUE)) { + sf::sf_use_s2(FALSE) + } + + # ---------- Reactives --------------------------------------------------- + raw_data <- reactiveVal() + data <- reactiveVal() + anon_data <- reactiveVal() + anonymized_cols <- reactiveVal(character()) + initial_qids <- reactiveVal(character()) + import_snip <- reactiveVal(character()) + import_stata_snip <- reactiveVal(character()) + import_py_snip <- reactiveVal(character()) + r_steps <- reactiveVal(character()) + stata_steps <- reactiveVal(character()) + python_steps <- reactiveVal(character()) + previous_data_stack <- reactiveVal(list()) + previous_r_stack <- reactiveVal(list()) + previous_stata_stack <- reactiveVal(list()) + previous_py_stack <- reactiveVal(list()) + log_steps <- reactiveVal(character()) + k_bins <- reactiveVal(list()) + gen_assigned <- reactiveVal(list()) + previous_gen_assigned_stack <- reactiveVal(list()) + last_method <- reactiveVal(NULL) + report_temp <- reactiveVal(NULL) + + r_obj_name <- reactiveVal(NULL) + py_obj_name <- reactiveVal(NULL) + + geo_preview_layer <- reactiveVal(NULL) + geo_after_layer <- reactiveVal(NULL) + geo_lat_col <- reactiveVal(NULL) + geo_lon_col <- reactiveVal(NULL) + + # Perf: cache risk metrics so they don't recompute many times + risk_before_metrics <- reactiveVal(NULL) + risk_after_metrics <- reactiveVal(NULL) + + # ====================================================================== + # Landing removed: NO continue/manual handlers here + # (Descriptions tab still works via output$descriptions_panel below) + # ====================================================================== + + # ====================================================================== + # Guard against missing input$method/input$selected_qids + # ====================================================================== + safe_method <- reactive(input$method %||% "Masking") + safe_selected_qids <- reactive(input$selected_qids %||% character()) + + # ====================================================================== + # NEW: Load data from MAIN PLATFORM (rv_current) instead of upload + # UI must provide actionButton(ns("use_platform_data"), ...) + # ====================================================================== + get_platform_df <- function() { + if (is.null(rv_current)) return(NULL) + # Try common locations used in your app + df <- rv_current$working_df %||% rv_current$data %||% rv_current$df %||% NULL + if (is.null(df)) return(NULL) + tryCatch(as.data.frame(df, stringsAsFactors = FALSE), error = function(e) NULL) + } + + init_state_from_df <- function(df, source_label = "platform_dataset") { + # object names for code panes + r_obj_name(make.names(source_label)) + py_obj_name(gsub("[^A-Za-z0-9_]", "_", source_label)) + + raw_data(df) + data(df) + anon_data(NULL) + + anonymized_cols(character()) + initial_qids(character()) + + previous_data_stack(list()) + previous_r_stack(list()) + previous_stata_stack(list()) + previous_py_stack(list()) + + log_steps(character()) + k_bins(list()) + + gen_assigned(list()) + previous_gen_assigned_stack(list()) + + geo_preview_layer(NULL) + geo_after_layer(NULL) + geo_lat_col(NULL) + geo_lon_col(NULL) + + risk_before_metrics(NULL) + risk_after_metrics(NULL) + + # Code panes: platform load note + import_snip(c( + "# Using dataset already loaded in the main platform", + "library(dplyr)", + "# (No file import step needed here)" + )) + r_steps(import_snip()) + + import_stata_snip(c( + "// Using dataset already loaded in the main platform", + "// (No file import step needed here)" + )) + stata_steps(import_stata_snip()) + + import_py_snip(c( + "# Using dataset already loaded in the main platform", + "# (No file import step needed here)" + )) + python_steps(import_py_snip()) + + shinyAce::updateAceEditor(session, "r_code_ace", value = paste(r_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "stata_code_ace", value = paste(stata_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "python_code_ace", value = paste(python_steps(), collapse = "\n\n")) + } + + observeEvent(input$use_platform_data, { + df <- get_platform_df() + if (is.null(df) || !nrow(df)) { + showNotification("No dataset found in the main platform. Upload/select data there first.", type = "error") + return() + } + init_state_from_df(df, source_label = "platform_dataset") + showNotification("Dataset loaded from the main platform.", type = "message") + }, ignoreInit = TRUE) + + # ====================================================================== + # (Optional) Keep your existing upload behavior — only runs if UI has fileInput + # ====================================================================== + observeEvent(input$file, { + req(input$file) + + fname <- input$file$name + ext <- tools::file_ext(fname) + dname_raw <- tools::file_path_sans_ext(fname) + + r_name <- make.names(dname_raw) + py_name <- gsub("[^A-Za-z0-9_]", "_", dname_raw) + r_obj_name(r_name) + py_obj_name(py_name) + + df <- switch( + tolower(ext), + csv = data.table::fread(input$file$datapath, data.table = FALSE, showProgress = FALSE), + xlsx = readxl::read_excel(input$file$datapath), + dta = haven::read_dta(input$file$datapath), + { showNotification("Unsupported file type.", type = "error"); return() } + ) + + raw_data(df) + data(df) + anon_data(NULL) + + anonymized_cols(character()) + initial_qids(character()) + + previous_data_stack(list()) + previous_r_stack(list()) + previous_stata_stack(list()) + previous_py_stack(list()) + + log_steps(character()) + k_bins(list()) + + gen_assigned(list()) + previous_gen_assigned_stack(list()) + + geo_preview_layer(NULL) + geo_after_layer(NULL) + geo_lat_col(NULL) + geo_lon_col(NULL) + + risk_before_metrics(NULL) + risk_after_metrics(NULL) + + r_pkg_base <- c("library(dplyr)", "library(sdcMicro)") + r_pkg_read <- if (tolower(ext) == "xlsx") "library(readxl)" else if (tolower(ext) == "dta") "library(haven)" else NULL + + r_read <- if (tolower(ext) == "csv") { + sprintf("%s <- read.csv(%s, stringsAsFactors = FALSE)", r_name, shQuote(fname)) + } else if (tolower(ext) == "xlsx") { + sprintf("%s <- read_excel(%s)", r_name, shQuote(fname)) + } else { + sprintf("%s <- read_dta(%s)", r_name, shQuote(fname)) + } + + import_snip(c("# Load data", r_pkg_base, r_pkg_read, r_read)) + r_steps(import_snip()) + + stata_snip <- switch( + tolower(ext), + csv = sprintf("import delimited %s, clear", shQuote(fname)), + xlsx = sprintf("import excel %s, clear", shQuote(fname)), + dta = sprintf("use %s, clear", shQuote(fname)) + ) + + import_stata_snip(c("// Load data", stata_snip)) + stata_steps(import_stata_snip()) + + python_snip <- switch( + tolower(ext), + csv = c("# Load data", "import pandas as pd", sprintf("%s = pd.read_csv(%s)", py_name, shQuote(fname))), + xlsx = c("# Load data", "import pandas as pd", sprintf("%s = pd.read_excel(%s)", py_name, shQuote(fname))), + dta = c("# Load data", "import pandas as pd", "import pyreadstat", + sprintf("%s, meta = pyreadstat.read_dta(%s)", py_name, shQuote(fname))) + ) + + import_py_snip(python_snip) + python_steps(import_py_snip()) + + shinyAce::updateAceEditor(session, "r_code_ace", value = paste(r_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "stata_code_ace", value = paste(stata_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "python_code_ace", value = paste(python_steps(), collapse = "\n\n")) + }) + + # ---------- Bin-Size Advisor ------------------------------------------- + observe({ + req(data()) + nums <- names(data())[sapply(data(), is.numeric)] + updateSelectInput( + session, "advisor_var", + choices = nums, + selected = if (length(nums) > 0) nums[1] else NULL + ) + }) + + advisor_x <- reactive({ + req(data(), input$advisor_var) + na.omit(data()[[input$advisor_var]]) + }) + + output$advisor_dist <- renderPlot({ + req(advisor_x()) + x <- advisor_x() + hist( + x, + main = paste("Histogram of", input$advisor_var), + xlab = input$advisor_var, + border = "white" + ) + }) + + output$advisor_summary <- renderPrint({ + req(advisor_x()) + x <- advisor_x() + stats <- c( + Mean = mean(x), + Median = median(x), + IQR = IQR(x), + SD = sd(x), + Min = min(x), + Max = max(x) + ) + print(stats) + }) + + bin_advice <- eventReactive(input$advisor_run, { + req(advisor_x()) + x <- advisor_x() + n <- length(x) + rng <- range(x) + range_x <- diff(rng) + iqr_x <- IQR(x) + sd_x <- sd(x) + + k_sturges <- ceiling(log2(n) + 1) + w_sturges <- range_x / k_sturges + + w_fd <- if (iqr_x > 0) 2 * iqr_x / (n^(1 / 3)) else NA + k_fd <- if (!is.na(w_fd) && w_fd > 0) ceiling(range_x / w_fd) else NA + + w_scott <- if (sd_x > 0) 3.5 * sd_x / (n^(1 / 3)) else NA + k_scott <- if (!is.na(w_scott) && w_scott > 0) ceiling(range_x / w_scott) else NA + + k_sqrt <- ceiling(sqrt(n)) + w_sqrt <- range_x / k_sqrt + + data.frame( + Method = c("Sturges", "Freedman–Diaconis", "Scott", "Square root"), + Bin_Width = c(w_sturges, w_fd, w_scott, w_sqrt), + Num_Bins = c(k_sturges, k_fd, k_scott, k_sqrt), + stringsAsFactors = FALSE + ) + }) + + output$advisor_table <- renderTable({ + df <- bin_advice() + df$Bin_Width <- round(df$Bin_Width, 2) + df + }, striped = TRUE, hover = TRUE, spacing = "l") + + output$advisor_plot <- renderPlot({ + df <- bin_advice() + req(nrow(df) > 0) + x <- advisor_x() + + old_par <- par(no.readonly = TRUE) + on.exit(par(old_par), add = TRUE) + + rows <- ceiling(nrow(df) / 2) + cols <- 2 + par(mfrow = c(rows, cols), mar = c(4, 4, 2, 1)) + + for (i in seq_len(nrow(df))) { + bw <- df$Bin_Width[i] + if (!is.finite(bw) || bw <= 0) next + brks <- seq(min(x, na.rm = TRUE), max(x, na.rm = TRUE) + bw, by = bw) + hist(x, breaks = brks, main = df$Method[i], xlab = input$advisor_var, border = "white") + } + }) + + # ---------- Risk metric calculators (cached) ---------------------------- + calc_risk_metrics <- function(df, cols) { + if (!nrow(df) || !length(cols)) return(NULL) + tbl <- df %>% + dplyr::group_by(dplyr::across(dplyr::all_of(cols))) %>% + dplyr::summarise(n = dplyr::n(), .groups = "drop") %>% + dplyr::mutate(r = 1 / n) + list( + avg = mean(tbl$r), + max = max(tbl$r), + pct_unique = mean(tbl$n == 1) + ) + } + + observeEvent(list(data(), initial_qids()), { + req(data(), initial_qids()) + q <- risk_qids(initial_qids()) + if (!length(q)) { risk_before_metrics(NULL); return() } + risk_before_metrics(calc_risk_metrics(data(), q)) + }, ignoreInit = TRUE) + + observeEvent(list(anon_data(), initial_qids()), { + req(anon_data(), initial_qids()) + q <- risk_qids(initial_qids()) + if (!length(q)) { risk_after_metrics(NULL); return() } + df_a <- anon_data() + after_cols <- sapply(q, get_after_col, df_after = df_a, USE.NAMES = FALSE) + risk_after_metrics(calc_risk_metrics(df_a, after_cols)) + }, ignoreInit = TRUE) + + # ---------- Risk AFTER (coords excluded) -------------------------------- + output$risk_after <- renderUI({ + req(anon_data(), initial_qids()) + q <- risk_qids(initial_qids()) + vld(need(length(q) > 0, "Select non-coordinate QIDs.")) + + m <- risk_after_metrics() + req(m) + + tags$p(sprintf( + "After: Average Risk: %.4f; Maximum Risk: %.4f; Percentage Unique: %.4f%%", + m$avg, m$max, m$pct_unique * 100 + )) + }) + + output$gauge_after <- flexdashboard::renderGauge({ + req(anon_data(), initial_qids()) + q <- risk_qids(initial_qids()) + vld(need(length(q) > 0, "Select non-coordinate QIDs.")) + + m <- risk_after_metrics() + req(m) + + pct <- round(m$avg * 100, 2) + flexdashboard::gauge( + pct, min = 0, max = 100, symbol = "%", + sectors = flexdashboard::gaugeSectors(success = c(0, 20), warning = c(20, 50), danger = c(50, 100)), + label = sprintf("%.2f%%", pct), abbreviate = FALSE + ) + }) + + output$gauge_after_value <- renderText({ + req(anon_data(), initial_qids()) + q <- risk_qids(initial_qids()) + vld(need(length(q) > 0, "Select non-coordinate QIDs.")) + + m <- risk_after_metrics() + req(m) + + sprintf("%.2f%%", round(m$avg * 100, 2)) + }) + + # ---------- Descriptions (TRANSLATABLE) ------------------------------------ + app_dir <- shiny::getShinyOption("appDir") + if (is.null(app_dir) || !nzchar(app_dir)) app_dir <- getwd() + + manual2_rmd <- normalizePath( + file.path(app_dir, "server", "anon", "docs", "descriptions1.Rmd"), + mustWork = FALSE + ) + + manual2_html <- normalizePath( + file.path(app_dir, "server", "anon", "www", "descriptions1.html"), + mustWork = FALSE + ) + + + # Point to the SAME excel file your get_rv_labels() uses + labels_xlsx <- normalizePath( + file.path(app_dir, "labelling_file_with_manual_full.xlsx"), + mustWork = FALSE + ) + + # ---- IMPORTANT: define a *reactive* language getter used by the rest of the app ---- + # Replace the body of this function with your app's real language source. + get_current_language <- reactive({ + # Common patterns (pick the one that matches your app): + # input$change_language + # input$language + # rv_labels$language + # rv_translation$lang + + # BEST fallback (won't break even if input doesn't exist): + lang <- NULL + if (!is.null(input$change_language) && nzchar(input$change_language)) lang <- input$change_language + if (!is.null(input$language) && nzchar(input$language)) lang <- input$language + if (is.null(lang)) lang <- "English" + + lang + }) + + output$descriptions_panel <- renderUI({ + lang <- get_current_language() # <- THIS makes it reactive to language changes + req(file.exists(manual2_rmd)) + req(file.exists(labels_xlsx)) + + out_dir <- tempdir() + + # Create a language-specific output name so it doesn't reuse the old HTML + out_file <- file.path(out_dir, paste0("descriptions1_", lang, ".html")) + + ok <- TRUE + tryCatch({ + rmarkdown::render( + input = manual2_rmd, + output_file = basename(out_file), + output_dir = out_dir, + output_format = "html_document", + params = list( + language = lang, + labels_path = labels_xlsx + ), + envir = new.env(parent = globalenv()), + quiet = TRUE + ) + }, error = function(e) { + ok <<- FALSE + showNotification(paste("Descriptions render error:", e$message), type = "error") + }) + + if (!ok) return(NULL) + + # Resource path must be unique per language (and optionally per render) + res_name_tmp <- paste0("anon_docs_tmp_", gsub("[^A-Za-z0-9]", "_", lang), "_", session$token) + addResourcePath(res_name_tmp, out_dir) + + # Cache-bust so iframe reloads when switching languages + tags$iframe( + src = paste0(res_name_tmp, "/", basename(out_file), "?v=", as.integer(Sys.time())), + style = "width:100%; height:calc(100vh - 150px); border:none;" + ) + }) + + + + output$descriptions_panel <- renderUI({ + res_name_html <- paste0("manuals2_", gsub("[^A-Za-z0-9]", "_", landing_dom)) + res_name_tmp <- paste0("manuals2_tmp_", gsub("[^A-Za-z0-9]", "_", landing_dom)) + + if (file.exists(manual2_html)) { + addResourcePath(res_name_html, normalizePath(file.path("..", "Anon", "www"), mustWork = TRUE)) + tags$iframe( + src = paste0(res_name_html, "/", basename(manual2_html)), + style = "width:100%; height:calc(100vh - 150px); border:none;" + ) + + } else if (file.exists(manual2_rmd)) { + out_dir <- tempdir() + out_file <- file.path(out_dir, "descriptions1.html") + ok <- TRUE + + tryCatch({ + rmarkdown::render( + manual2_rmd, + output_file = basename(out_file), + output_dir = out_dir, + output_format = "html_document", + quiet = TRUE + ) + }, error = function(e) { + ok <<- FALSE + showNotification(paste("Manual render error:", e$message), type = "error") + }) + + if (!ok) return(NULL) + addResourcePath(res_name_tmp, out_dir) + + tags$iframe( + src = paste0(res_name_tmp, "/", basename(out_file)), + style = "width:100%; height:calc(100vh - 150px); border:none;" + ) + + } else { + tags$div(style = "padding:10px;", "Manual not available.") + } + }) + + # ---------- Suppress & Remove Direct Identifiers ------------------------ + output$identifier_selector <- renderUI({ + req(data()) + tagList( + checkboxGroupInput(ns("direct_ids"), "Select Direct Identifiers to Remove:", choices = names(data())), + actionButton(ns("remove_ids"), "Suppress & Remove Identifiers", class = "btn btn-danger btn-block") + ) + }) + + observeEvent(input$remove_ids, { + req(data(), input$direct_ids) + + previous_data_stack(c(previous_data_stack(), list(if (is.null(anon_data())) data() else anon_data()))) + previous_r_stack(c(previous_r_stack(), list(r_steps()))) + previous_stata_stack(c(previous_stata_stack(), list(stata_steps()))) + previous_py_stack(c(previous_py_stack(), list(python_steps()))) + + df2 <- data()[, !names(data()) %in% input$direct_ids, drop = FALSE] + data(df2) + anon_data(NULL) + anonymized_cols(character()) + initial_qids(character()) + + risk_before_metrics(NULL) + risk_after_metrics(NULL) + + cols <- input$direct_ids + r_name <- r_obj_name() %||% "platform_dataset" + py_name <- py_obj_name() %||% "platform_dataset" + + code_r <- paste0( + "# Suppression\nlibrary(dplyr)\n", + r_name, " <- ", r_name, " %>% dplyr::select(-", paste(cols, collapse = ", -"), ")" + ) + code_s <- paste0("// Suppression\n", "drop ", paste(cols, collapse = " ")) + code_py <- paste0( + "# Suppression\nimport pandas as pd\n", + py_name, ".drop(columns=[", paste(shQuote(cols), collapse = ", "), "], inplace=True)" + ) + + r_steps(c(r_steps(), code_r)) + stata_steps(c(stata_steps(), code_s)) + python_steps(c(python_steps(), code_py)) + log_steps(c(log_steps(), paste(Sys.time(), "- Suppressed IDs"))) + + shinyAce::updateAceEditor(session, "r_code_ace", value = paste(r_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "stata_code_ace", value = paste(stata_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "python_code_ace", value = paste(python_steps(), collapse = "\n\n")) + + showNotification("Identifiers suppressed & removed.", type = "message") + }) + + # ---------- QID picker & sync ------------------------------------------- + output$bucket_ui <- renderUI({ + req(data()) + avail <- setdiff(names(data()), safe_selected_qids()) + + sortable::bucket_list( + header = "Drag variables into QID bucket", + group_name = ns("qid_group"), + orientation = "horizontal", + sortable::add_rank_list("Available Variables", ns("available_vars"), labels = avail), + sortable::add_rank_list("Selected QIDs", ns("selected_qids"), labels = initial_qids()) + ) + }) + + observeEvent(input$selected_qids, { + initial_qids(input$selected_qids %||% character()) + }, ignoreNULL = FALSE) + + observe({ + req(initial_qids()) + if (!isTRUE(safe_method() == "Generalization")) return() + + choices <- initial_qids() + if (length(choices) == 0) return() + + current <- isolate(input$gen_var) + if (is.null(current) || !(current %in% choices)) { + updateSelectInput(session, "gen_var", choices = choices, selected = choices[1]) + } else { + updateSelectInput(session, "gen_var", choices = choices, selected = current) + } + }) + + # ---------- Technique parameters UI (server side) ----------------------- + output$k_num_picker <- renderUI({ + req(data(), initial_qids()) + nq <- initial_qids()[sapply(data()[, initial_qids(), drop = FALSE], is.numeric)] + if (length(nq) == 0) return(helpText("No numeric QIDs to bucket.")) + selectInput(ns("k_bucket_var"), "Numeric QID to bucket:", choices = nq, selected = nq[1]) + }) + + output$extra_input <- renderUI({ + req(data()) + cols <- names(data()) + + switch( + safe_method(), + + "Masking" = checkboxGroupInput(ns("mask_cols"), "Columns to mask:", choices = cols), + "Suppression" = checkboxGroupInput(ns("supp_cols"), "Columns to suppress:", choices = cols), + "Pseudonymization" = checkboxGroupInput(ns("ps_cols"), "Columns to pseudonymize:", choices = cols), + "Tokenization" = checkboxGroupInput(ns("tok_cols"), "Columns to tokenize:", choices = cols), + + "Bucketing" = tagList( + selectInput(ns("bucket_col"), "Column to bucket:", choices = cols), + numericInput(ns("bin_interval"), "Bin size:", value = 4, min = 1) + ), + + "K-Anonymity" = tagList( + uiOutput(ns("k_num_picker")), + numericInput(ns("k_bin_size"), "Bin size:", value = 5, min = 1), + numericInput(ns("k_value"), "k threshold:", value = 2, min = 2) + ), + + "L-Diversity" = tagList( + selectInput(ns("sensitive_attr"), "Sensitive attribute:", choices = cols), + numericInput(ns("l_value"), "l threshold:", value = 2, min = 2) + ), + + "T-Closeness" = tagList( + selectInput(ns("sensitive_attr_tc"), "Sensitive attribute:", choices = cols), + numericInput(ns("t_threshold"), "t threshold:", value = 0.1, min = 0, step = 0.01) + ), + + "Generalization" = tagList( + selectInput(ns("gen_var"), "Variable to generalize:", choices = initial_qids()), + uiOutput(ns("gen_groups_ui")) + ), + + "Anonymize Coordinates" = tagList( + selectInput(ns("geo_lat_col"), "Latitude column:", choices = cols), + selectInput(ns("geo_lon_col"), "Longitude column:", choices = cols), + radioButtons( + ns("geo_mode"), "Mode:", + choices = c("Truncate decimals" = "truncate", "Aggregate to polygons" = "aggregate"), + selected = "aggregate", inline = TRUE + ), + conditionalPanel( + condition = sprintf("input['%s'] == 'truncate'", ns("geo_mode")), + numericInput(ns("geo_decimals"), "Keep this many decimals:", value = 3, min = 0, step = 1), + helpText("Truncation (not rounding): reduces precision to the specified number of decimals.") + ), + conditionalPanel( + condition = sprintf("input['%s'] == 'aggregate'", ns("geo_mode")), + numericInput(ns("geo_grid_m"), "Grid cell size (meters):", value = 500, min = 10, step = 10), + helpText("Points are snapped to the grid cell centroid; polygons are drawn on the map.") + ), + helpText("Preview: original points + faint preview (for truncate). Apply: dataset updated; polygons drawn.") + ), + + NULL + ) + }) + + # ---------- Apply anonymization (incl. GEO) -------------------------------- + observeEvent(input$apply, { + req(data(), input$method) + if (input$method != "Anonymize Coordinates") req(initial_qids()) + + last_method(input$method) + + previous_data_stack(c(previous_data_stack(), list(if (is.null(anon_data())) data() else anon_data()))) + previous_r_stack(c(previous_r_stack(), list(r_steps()))) + previous_stata_stack(c(previous_stata_stack(), list(stata_steps()))) + previous_py_stack(c(previous_py_stack(), list(python_steps()))) + + df <- if (is.null(anon_data())) data() else anon_data() + code_r <- code_s <- code_py <- NULL + step_cols <- NULL + r_name <- r_obj_name() %||% "platform_dataset" + py_name <- py_obj_name() %||% "platform_dataset" + + if (input$method == "Masking") { + step_cols <- input$mask_cols + if (length(step_cols) == 0) { showNotification("Select columns to mask.", type="error"); return() } + + df[step_cols] <- lapply(df[step_cols], function(x) + ifelse(is.na(x), NA_character_, strrep("*", 10)) + ) + + code_r <- paste0( + "# Masking (strings & numerics)\nlibrary(dplyr)\n", + r_name, " <- ", r_name, " %>% dplyr::mutate(across(all_of(c(", + paste(shQuote(step_cols), collapse = ", "), + ")), ~ ifelse(is.na(.), NA_character_, strrep(\"*\", 10))))" + ) + + code_s <- paste0( + "// Masking (strings & numerics)\n", + "foreach v of varlist ", paste(step_cols, collapse = " "), " {\n", + " capture confirm numeric variable `v'\n", + " if !_rc { tostring `v', replace force }\n", + " replace `v' = \"**********\" if !missing(`v')\n", + "}" + ) + + code_py <- paste0( + "# Masking (strings & numerics)\n", + "import pandas as pd\n", + "for col in [", paste(shQuote(step_cols), collapse = ", "), "]:\n", + " ", py_name, "[col] = ", py_name, "[col].apply(lambda x: None if pd.isna(x) else '*'*10)" + ) + + } else if (input$method == "Suppression") { + step_cols <- input$supp_cols + if (length(step_cols) == 0) { showNotification("Select columns to suppress.", type="error"); return() } + + df <- df[, !names(df) %in% step_cols, drop = FALSE] + + code_r <- paste0( + "# Suppression\nlibrary(dplyr)\n", + r_name, " <- ", r_name, " %>% dplyr::select(-", paste(step_cols, collapse = ", -"), ")" + ) + code_s <- paste0("// Suppression\n", "drop ", paste(step_cols, collapse = " ")) + code_py <- paste0( + "# Suppression\nimport pandas as pd\n", + py_name, ".drop(columns=[", paste(shQuote(step_cols), collapse = ", "), "], inplace=True)" + ) + + } else if (input$method == "Bucketing") { + step_cols <- input$bucket_col; b <- input$bin_interval + if (is.null(step_cols) || is.null(b)) { showNotification("Choose a column and bin size.", type="error"); return() } + + vals <- df[[step_cols]] + if (!is.numeric(vals)) { showNotification("Bucketing requires numeric column.", type = "error"); return() } + + minv <- suppressWarnings(min(vals, na.rm = TRUE)); maxv <- suppressWarnings(max(vals, na.rm = TRUE)) + if (!is.finite(minv) || !is.finite(maxv)) { showNotification("No finite values to bucket.", type="error"); return() } + + start <- floor(minv / b) * b; end <- ceiling((maxv + 1) / b) * b + brks <- seq(start, end, by = b) + lbls <- paste0(head(brks, -1), "-", brks[-1] - 1) + + df[[step_cols]] <- cut(vals, breaks = brks, labels = lbls, include.lowest = TRUE, right = FALSE) + + code_r <- paste0( + "# Bucketing\nlibrary(dplyr)\n", + r_name, " <- ", r_name, " %>% dplyr::mutate(", step_cols, " = cut(", step_cols, ",\n", + " breaks = seq(", start, ", ", end, ", by=", b, "),\n", + " labels = c(", paste(shQuote(lbls), collapse = ", "), "), right=FALSE, include_lowest=TRUE))" + ) + + code_s <- paste0( + "// Bucketing\n", + "gen long __b = floor(", step_cols, "/", b, ")*", b, "\n", + "tostring __b, gen(__bstr)\n", + "gen long __e = __b + ", b, " - 1\n", + "tostring __e, gen(__estr)\n", + "replace ", step_cols, " = __bstr + \"-\" + __estr if !missing(", step_cols, ")\n", + "drop __b __e __bstr __estr" + ) + + code_py <- paste0( + "# Bucketing\nimport pandas as pd\nimport numpy as np\n", + "b = ", b, "\n", + "minv = int(np.floor(", py_name, "['", step_cols, "'].min()/b)*b)\n", + "maxv = int(np.ceil((", py_name, "['", step_cols, "'].max()+1)/b)*b)\n", + "bins = list(range(minv, maxv+1, b))\n", + "labels = [f\"{bins[i]}-{bins[i+1]-1}\" for i in range(len(bins)-1)]\n", + py_name, "['", step_cols, "'] = pd.cut(", py_name, "['", step_cols, "'], bins=bins, labels=labels, right=False, include_lowest=True)" + ) + + } else if (input$method == "Pseudonymization") { + step_cols <- input$ps_cols + if (length(step_cols) == 0) { showNotification("Select columns to pseudonymize.", type="error"); return() } + + salt_vec <- uuid::UUIDgenerate(n = nrow(df)) + hash_vec <- function(values, salts) { + mapply(function(v, s) digest::digest(paste0(as.character(v), s), algo = "sha256"), + values, salts, USE.NAMES = FALSE) + } + + for (col in step_cols) { + x <- df[[col]] + df[[col]] <- ifelse(is.na(x), NA_character_, hash_vec(x, salt_vec)) + } + + code_r <- "# Pseudonymization applied in-app" + code_s <- "// Pseudonymization\n// no direct Stata analogue" + code_py <- "# Pseudonymization applied in-app" + + } else if (input$method == "Tokenization") { + step_cols <- input$tok_cols + if (length(step_cols) == 0) { showNotification("Select columns to tokenize.", type="error"); return() } + + df <- dplyr::mutate( + df, + dplyr::across( + dplyr::all_of(step_cols), + ~ ifelse(is.na(.x), NA_character_, stringi::stri_rand_strings(length(.x), 10, pattern = "[A-Za-z0-9]")) + ) + ) + + code_r <- "# Tokenization applied in-app" + code_s <- "// Tokenization\n// no direct Stata analogue" + code_py <- "# Tokenization applied in-app" + + } else if (input$method == "K-Anonymity") { + qids_all <- initial_qids() + k_val <- as.integer(input$k_value) + var_now <- input$k_bucket_var + bin_size <- as.integer(input$k_bin_size) + + vld( + need(length(qids_all) > 0, "Select QIDs in step 2 first."), + need(!is.na(k_val) && k_val >= 2, "k must be ≥ 2") + ) + + bins <- k_bins() + if (!is.null(var_now) && !is.na(bin_size)) { bins[[var_now]] <- bin_size; k_bins(bins) } + + bucket_one <- function(x, b) { + if (!is.numeric(x)) return(x) + rng <- range(x, na.rm = TRUE) + if (!all(is.finite(rng))) return(x) + start <- floor(rng[1]/b)*b + end <- ceiling((rng[2]+1)/b)*b + cut(x, breaks = seq(start, end, by = b), right = FALSE, include.lowest = TRUE, + labels = sprintf("%d-%d", head(seq(start, end, by=b), -1), seq(start, end, by=b)[-1]-1)) + } + + if (length(bins)) for (nm in names(bins)) if (nm %in% names(df)) df[[nm]] <- bucket_one(df[[nm]], bins[[nm]]) + k_qids_use <- intersect(names(bins), qids_all) + vld(need(length(k_qids_use) > 0, "Bucket at least one numeric QID before applying k-anonymity.")) + + grouped_sizes <- df %>% + dplyr::group_by(dplyr::across(dplyr::all_of(k_qids_use))) %>% + dplyr::tally(name=".__size__") + + df_tagged <- df %>% + dplyr::left_join(grouped_sizes, by = k_qids_use) %>% + dplyr::mutate(.__ok__ = .__size__ >= k_val) + + df_ok <- df_tagged %>% dplyr::filter(.__ok__) %>% dplyr::select(-.__ok__, -.__size__) + df_bad <- df_tagged %>% dplyr::filter(!.__ok__) %>% dplyr::select(-.__ok__, -.__size__) + df <- dplyr::bind_rows(df_ok, df_bad) + + anon_data(df) + anonymized_cols(union(anonymized_cols(), k_qids_use)) + + code_r <- "# K-Anonymity applied in-app" + code_s <- "* no Stata analogue" + code_py <- "# no Python analogue" + + } else if (input$method == "T-Closeness") { + step_cols <- risk_qids(initial_qids()) + vld(need(length(step_cols) > 0, "Select non-coordinate QIDs.")) + + df <- apply_t_closeness(df, qids = step_cols, sensitive = input$sensitive_attr_tc, t = input$t_threshold) + + code_r <- "# T-Closeness applied in-app" + code_s <- "// T-Closeness\n// no direct Stata analogue" + code_py <- "# T-Closeness applied in-app" + + } else if (input$method == "Anonymize Coordinates") { + + latc <- input$geo_lat_col; lonc <- input$geo_lon_col + + vld( + need(!is.null(latc) && !is.null(lonc), "Pick latitude & longitude columns first."), + need(latc %in% names(df) && lonc %in% names(df), "Invalid coordinate columns."), + need(is.numeric(df[[latc]]), "Latitude must be numeric."), + need(is.numeric(df[[lonc]]), "Longitude must be numeric.") + ) + + pts_df <- df[, c(lonc, latc), drop = FALSE] + names(pts_df) <- c("lon","lat") + comp_mask_full <- stats::complete.cases(pts_df) + pts_df <- pts_df[comp_mask_full, , drop = FALSE] + + if (!nrow(pts_df)) { + showNotification("No valid coordinate rows to anonymize.", type="error"); return() + } + + pts_wgs <- sf::st_as_sf(pts_df, coords = c("lon","lat"), crs = 4326) + xy <- sf::st_coordinates(pts_wgs) + epsg <- guess_utm_epsg(xy[,1], xy[,2]) + pts_utm <- safe_transform(pts_wgs, epsg) + + mode <- input$geo_mode %||% "aggregate" + cell_wkt_full <- rep(NA_character_, nrow(df)) + + if (mode == "truncate") { + d <- max(0, suppressWarnings(as.integer(input$geo_decimals))); if (is.na(d)) d <- 3 + trunc_dec <- function(x, d) { f <- 10^d; sign(x) * floor(abs(x) * f) / f } + + xy_wgs <- sf::st_coordinates(safe_transform(pts_utm, 4326)) + lon_a <- trunc_dec(xy_wgs[,1], d) + lat_a <- trunc_dec(xy_wgs[,2], d) + + lat_mean <- mean(lat_a, na.rm = TRUE) + m_per_deg_lat <- 111320 + m_per_deg_lon <- 111320 * cos(lat_mean * pi/180) + cell_m <- nz_pos(min(m_per_deg_lat, m_per_deg_lon) / (10^d), 100) + + agg <- build_grid_agg(pts_utm, cell_m) + agg_wgs <- safe_transform(agg, 4326) + geo_after_layer(agg_wgs) + + if (nrow(agg)) { + join_ix <- sf::st_intersects(pts_utm, agg) + agg_wkt <- sf::st_as_text(sf::st_geometry(agg_wgs)) + + cell_wkt_new <- rep(NA_character_, nrow(pts_utm)) + for (i in seq_len(nrow(pts_utm))) { + cell_idx <- if (length(join_ix[[i]]) >= 1) join_ix[[i]][1] else NA_integer_ + if (is.finite(cell_idx)) cell_wkt_new[i] <- agg_wkt[cell_idx] + } + + df_rows_idx <- which(comp_mask_full) + df[[lonc]][df_rows_idx] <- lon_a + df[[latc]][df_rows_idx] <- lat_a + cell_wkt_full[df_rows_idx] <- cell_wkt_new + } else { + showNotification("No grid cells created (empty). Check decimals or data extent.", type="warning") + } + + anonymized_cols(union(anonymized_cols(), c(latc, lonc, "cell_wkt"))) + showNotification("Coordinate anonymization (truncate) applied.", type = "message") + + } else if (mode == "aggregate") { + gsize <- nz_pos(input$geo_grid_m, 500) + + agg <- build_grid_agg(pts_utm, gsize) + agg_wgs <- safe_transform(agg, 4326) + geo_after_layer(agg_wgs) + + if (nrow(agg)) { + join_ix <- sf::st_intersects(pts_utm, agg) + + cents <- sf::st_centroid(agg) + cents_wgs <- safe_transform(cents, 4326) + cxy <- sf::st_coordinates(cents_wgs) + + agg_wkt <- sf::st_as_text(sf::st_geometry(agg_wgs)) + + lon_new <- rep(NA_real_, nrow(pts_utm)) + lat_new <- rep(NA_real_, nrow(pts_utm)) + cell_wkt_new <- rep(NA_character_, nrow(pts_utm)) + + for (i in seq_len(nrow(pts_utm))) { + cell_idx <- if (length(join_ix[[i]]) >= 1) join_ix[[i]][1] else NA_integer_ + if (is.finite(cell_idx)) { + lon_new[i] <- cxy[cell_idx, 1] + lat_new[i] <- cxy[cell_idx, 2] + cell_wkt_new[i] <- agg_wkt[cell_idx] + } + } + + df_rows_idx <- which(comp_mask_full) + df[[lonc]][df_rows_idx] <- lon_new + df[[latc]][df_rows_idx] <- lat_new + cell_wkt_full[df_rows_idx] <- cell_wkt_new + + } else { + showNotification("No grid cells created (empty). Check grid size or data extent.", type="warning") + } + + anonymized_cols(union(anonymized_cols(), c(latc, lonc, "cell_wkt"))) + showNotification("Coordinate anonymization (aggregate to polygons) applied.", type = "message") + + } else { + showNotification("Unsupported mode selected.", type = "error"); return() + } + + df$cell_wkt <- cell_wkt_full + df <- df[, setdiff(names(df), c(lonc, latc)), drop = FALSE] + + anon_data(df) + log_steps(c(log_steps(), paste(Sys.time(), "- Applied Anonymize Coordinates (mode:", mode, ")"))) + geo_preview_layer(NULL) + + code_r <- "# Anonymize Coordinates applied in-app (cell_wkt added; lat/lon dropped)" + code_s <- "* no direct Stata analogue" + code_py <- "# Anonymize Coordinates applied (cell_wkt added; lat/lon dropped)" + } + + if (!is.null(step_cols) && input$method %in% c( + "Masking","Suppression","Bucketing","Pseudonymization","Tokenization","T-Closeness" + )) { + anon_data(df) + anonymized_cols(union(anonymized_cols(), step_cols)) + } + + if (!is.null(code_r)) r_steps( c(r_steps(), code_r)) + if (!is.null(code_s)) stata_steps(c(stata_steps(), code_s)) + if (!is.null(code_py)) python_steps(c(python_steps(), code_py)) + + if (input$method != "Anonymize Coordinates") { + log_steps(c(log_steps(), paste(Sys.time(), "- Applied", input$method))) + } + + shinyAce::updateAceEditor(session, "r_code_ace", value = paste(r_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "stata_code_ace", value = paste(stata_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "python_code_ace", value = paste(python_steps(), collapse = "\n\n")) + }) + + # ---------- K-Report -------------------------------------------------------- + output$k_report <- renderUI({ + req(last_method()) + if (last_method() == "K-Anonymity") { + bins <- k_bins(); k_val <- input$k_value + tags$div( + tags$h4("K-Anonymity Report"), + tags$p(paste0("Threshold k = ", k_val)), + if (length(bins) > 0) + tags$p(paste0("Bucket sizes: ", + paste(paste0(names(bins)," = ",bins), collapse=", "))) + ) + } + }) + + # ---------- Generalization UI & Logic -------------------------------------- + output$gen_groups_ui <- renderUI({ + req(input$gen_var) + working <- if (!is.null(anon_data()) && input$gen_var %in% names(anon_data())) anon_data() else data() + is_num <- is.numeric(working[[input$gen_var]]) + default_mode <- if (is_num) "numeric" else "categorical" + + tagList( + radioButtons( + ns("gen_mode"), "Generalization mode:", + choices = c("Categorical (drag & drop)" = "categorical", + "Custom numeric ranges" = if (is_num) "numeric" else NULL), + selected = default_mode, inline = TRUE + ), + + conditionalPanel( + condition = sprintf("input['%s'] == 'categorical'", ns("gen_mode")), + { + cats <- unique(as.character(working[[input$gen_var]])) + tagList( + sortable::bucket_list( + header = "Drag values to collapse", + group_name = ns("gen_group"), + orientation= "horizontal", + sortable::add_rank_list("Available categories", ns("gen_available"), labels = cats), + sortable::add_rank_list("Selected categories", ns("gen_selected"), labels = NULL) + ), + textInput(ns("gen_new_label"), "New label for selected categories:"), + actionButton(ns("apply_generalization"), "Apply Generalization", class = "btn btn-primary btn-block") + ) + } + ), + + conditionalPanel( + condition = sprintf("input['%s'] == 'numeric'", ns("gen_mode")), + tagList( + helpText("Define numeric buckets: e.g., '0-10:Group 1;11-40:Group 2;41-100:Group 3'"), + textInput(ns("gen_ranges_text"), "Ranges (lower-upper:label; semicolon separated):", + value = "0-10:Group 1;11-40:Group 2;41-100:Group 3"), + helpText("Use '-Inf' or 'Inf' for open bounds. Ranges must not overlap or touch."), + actionButton(ns("apply_generalization"), "Apply Generalization", class = "btn btn-primary btn-block") + ) + ) + ) + }) + + observeEvent(input$apply_generalization, { + req(data(), input$gen_var, input$gen_mode) + last_method("Generalization") + + previous_data_stack(c(previous_data_stack(), list(if (is.null(anon_data())) data() else anon_data()))) + previous_r_stack(c(previous_r_stack(), list(r_steps()))) + previous_stata_stack(c(previous_stata_stack(), list(stata_steps()))) + previous_py_stack(c(previous_py_stack(), list(python_steps()))) + previous_gen_assigned_stack(c(previous_gen_assigned_stack(), list(gen_assigned()))) + + df <- if (is.null(anon_data())) data() else anon_data() + var <- input$gen_var + code_r <- code_s <- code_py <- NULL + + if (input$gen_mode == "categorical") { + sel <- input$gen_selected; lab <- input$gen_new_label + if (is.null(sel) || length(sel) == 0) { showNotification("No categories selected to generalize.", type = "error"); return() } + if (is.null(lab) || trimws(lab) == "") { showNotification("Provide a new label.", type = "error"); return() } + + ga <- gen_assigned(); ga[[var]] <- unique(c(ga[[var]] %||% character(), sel)); gen_assigned(ga) + df[[var]] <- as.character(df[[var]]); df[[var]][ df[[var]] %in% sel ] <- lab + + anon_data(df) + anonymized_cols(union(anonymized_cols(), var)) + + code_r <- "# Generalization (categorical) applied in-app" + code_s <- "// Generalization\n// no direct Stata analogue" + code_py <- "# Generalization applied in-app" + + } else if (input$gen_mode == "numeric") { + req(input$gen_ranges_text); range_txt <- input$gen_ranges_text + ranges_df <- tryCatch({ parse_ranges_text(range_txt) }, error = function(e) { + showNotification(paste("Range parse error:", e$message), type = "error"); NULL + }) + if (is.null(ranges_df)) return() + if (!is.numeric(df[[var]])) { showNotification(sprintf("Variable '%s' must be numeric.", var), type = "error"); return() } + + x <- df[[var]] + generalized <- rep(NA_character_, length(x)) + + for (i in seq_len(nrow(ranges_df))) { + lower <- ranges_df$lower[i]; upper <- ranges_df$upper[i]; label <- as.character(ranges_df$label[i]) + in_range <- (x >= lower) & (x <= upper) + if (is.infinite(lower) && lower < 0) in_range <- x <= upper + if (is.infinite(upper) && upper > 0) in_range <- x >= lower + generalized[in_range] <- label + } + + df[[var]] <- generalized + anon_data(df) + anonymized_cols(union(anonymized_cols(), var)) + + code_r <- "# Generalization (numeric ranges) applied in-app" + code_s <- "// Generalization\n// no direct Stata analogue" + code_py <- "# Generalization applied in-app" + } + + if (!is.null(code_r)) r_steps(c(r_steps(), code_r)) + if (!is.null(code_s)) stata_steps(c(stata_steps(), code_s)) + if (!is.null(code_py)) python_steps(c(python_steps(), code_py)) + log_steps(c(log_steps(), paste(Sys.time(), "- Applied generalization (mode:", input$gen_mode, ") to", var))) + + shinyAce::updateAceEditor(session, "r_code_ace", value = paste(r_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "stata_code_ace", value = paste(stata_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session, "python_code_ace", value = paste(python_steps(), collapse = "\n\n")) + + showNotification("Generalization applied.", type = "message") + }) + + # ---------- Copy buttons ---------------------------------------------------- + observeEvent(input$copy_r, { + shinyjs::runjs(sprintf("copyAce('%s','%s')", ns("r_code_ace"), ns("copy_r"))) + }) + observeEvent(input$copy_stata, { + shinyjs::runjs(sprintf("copyAce('%s','%s')", ns("stata_code_ace"), ns("copy_stata"))) + }) + observeEvent(input$copy_py, { + shinyjs::runjs(sprintf("copyAce('%s','%s')", ns("python_code_ace"), ns("copy_py"))) + }) + + # ---------- Undo & Reset ---------------------------------------------------- + observeEvent(input$undo, { + prev <- previous_data_stack(); pr <- previous_r_stack() + ps <- previous_stata_stack(); pp <- previous_py_stack() + if (length(prev) > 0) { + idx <- length(prev) + anon_data(prev[[idx]]) + r_steps(pr[[idx]]); stata_steps(ps[[idx]]); python_steps(pp[[idx]]) + previous_data_stack(prev[-idx]); previous_r_stack(pr[-idx]) + previous_stata_stack(ps[-idx]); previous_py_stack(pp[-idx]) + log_steps(c(log_steps(), paste(Sys.time(), "- Undid last step"))) + + pgs <- previous_gen_assigned_stack() + if (length(pgs) > 0) { + gen_assigned(pgs[[length(pgs)]]) + previous_gen_assigned_stack(pgs[-length(pgs)]) + } + + geo_preview_layer(NULL); geo_after_layer(NULL) + + showNotification("Undo successful.", type = "message") + shinyAce::updateAceEditor(session,"r_code_ace", value = paste(r_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session,"stata_code_ace", value = paste(stata_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session,"python_code_ace", value = paste(python_steps(), collapse = "\n\n")) + } + }) + + observeEvent(input$reset, { + req(raw_data()) + data(raw_data()); anon_data(NULL) + anonymized_cols(character()); initial_qids(character()) + r_steps(import_snip()); stata_steps(import_stata_snip()); python_steps(import_py_snip()) + previous_data_stack(list()); previous_r_stack(list()) + previous_stata_stack(list()); previous_py_stack(list()) + log_steps(c(log_steps(), paste(Sys.time(),"- Reset complete"))) + k_bins(list()); gen_assigned(list()); previous_gen_assigned_stack(list()) + + geo_preview_layer(NULL); geo_after_layer(NULL) + geo_lat_col(NULL); geo_lon_col(NULL) + + risk_before_metrics(NULL) + risk_after_metrics(NULL) + + showNotification("Reset complete.", type = "warning") + shinyAce::updateAceEditor(session,"r_code_ace", value = paste(r_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session,"stata_code_ace", value = paste(stata_steps(), collapse = "\n\n")) + shinyAce::updateAceEditor(session,"python_code_ace", value = paste(python_steps(), collapse = "\n\n")) + }) + + # ---------- Preview merged table ------------------------------------------- + output$preview_merged <- renderTable({ + req(data()) + orig <- data() + cur <- if (is.null(anon_data())) orig else anon_data() + + pr <- list() + for (col in anonymized_cols()) { + if (col %in% names(orig) && col %in% names(cur)) { + pr[[col]] <- orig[[col]] + pr[[paste0(col, "_anon")]] <- cur[[col]] + } + } + for (col in setdiff(names(cur), anonymized_cols())) pr[[col]] <- cur[[col]] + + head(as.data.frame(pr, stringsAsFactors = FALSE), 10) + }, rownames = FALSE) + + # ---------- Map (safe basemap + module-safe leafletProxy) ------------------- + output$geo_map <- leaflet::renderLeaflet({ + req(input$method == "Anonymize Coordinates", data()) + + m <- leaflet::leaflet(options = leaflet::leafletOptions(minZoom = 1)) |> + leaflet::addTiles() |> + leaflet::addScaleBar(position = "bottomleft") + + latc <- input$geo_lat_col + lonc <- input$geo_lon_col + + if (!is.null(latc) && !is.null(lonc) && + latc %in% names(data()) && lonc %in% names(data()) && + is.numeric(data()[[latc]]) && is.numeric(data()[[lonc]])) { + + df0 <- data()[, c(lonc, latc), drop = FALSE] + names(df0) <- c("lon","lat") + df0 <- df0[stats::complete.cases(df0), , drop = FALSE] + + if (nrow(df0)) { + m <- m |> + leaflet::addCircleMarkers( + lng = df0$lon, lat = df0$lat, + radius = 3, stroke = FALSE, fillOpacity = 0.6, + clusterOptions = leaflet::markerClusterOptions(), + group = "Before (points)" + ) + + if (all(is.finite(range(df0$lon))) && all(is.finite(range(df0$lat)))) { + m <- m |> + leaflet::fitBounds( + lng1 = min(df0$lon), lat1 = min(df0$lat), + lng2 = max(df0$lon), lat2 = max(df0$lat) + ) + } + } + } + + m |> + leaflet::addLayersControl( + overlayGroups = c("Before (points)", "After (areas)"), + options = leaflet::layersControlOptions(collapsed = FALSE) + ) + }) + + observe({ + req(input$method == "Anonymize Coordinates") + pol <- geo_after_layer() + + prx <- leaflet::leafletProxy(session$ns("geo_map"), session = session) + + if (is.null(pol) || !inherits(pol, "sf") || nrow(pol) == 0) { + prx |> leaflet::clearGroup("After (areas)") + return() + } + + pol <- sf::st_make_valid(pol) + pol <- sf::st_zm(pol, drop = TRUE, what = "ZM") + pol <- pol[!sf::st_is_empty(pol), , drop = FALSE] + if (!nrow(pol)) { prx |> leaflet::clearGroup("After (areas)"); return() } + + prx |> + leaflet::clearGroup("After (areas)") |> + leaflet::addPolygons( + data = pol, + weight = 1, color = "#555555", fillOpacity = 0.5, fillColor = "#2A9D8F", + group = "After (areas)", + popup = ~paste0("n = ", n) + ) + }) + + # ---------- Risk BEFORE (coords excluded) ---------------------------------- + output$risk_before <- renderUI({ + req(data(), initial_qids()) + q <- risk_qids(initial_qids()) + vld(need(length(q) > 0, "Select non-coordinate QIDs.")) + + m <- risk_before_metrics() + req(m) + + tags$p(sprintf( + "Before: Average Risk: %.4f; Maximum Risk: %.4f; Percentage Unique: %.4f%%", + m$avg, m$max, m$pct_unique * 100 + )) + }) + + output$gauge_before <- flexdashboard::renderGauge({ + req(data(), initial_qids()) + q <- risk_qids(initial_qids()) + vld(need(length(q) > 0, "Select non-coordinate QIDs.")) + + m <- risk_before_metrics() + req(m) + + pct <- round(m$avg * 100, 2) + flexdashboard::gauge( + pct, min=0, max=100, symbol="%", + sectors = flexdashboard::gaugeSectors(success=c(0,20),warning=c(20,50),danger=c(50,100)), + label=sprintf("%.2f%%", pct), abbreviate=FALSE + ) + }) + + # ---------- Row counts / log ----------------------------------------------- + output$n_obs_text <- renderText({ + if (is.null(data())) return("") + after_txt <- if (!is.null(anon_data())) paste0(" | After: ", nrow(anon_data())) else "" + paste0("Rows: ", nrow(data()), after_txt) + }) + + output$step_log <- renderText({ paste(log_steps(), collapse = "\n") }) + + # ---------- Downloads ------------------------------------------------------- + output$download <- downloadHandler( + filename = function() paste0("anonymized_", Sys.Date(), ".csv"), + content = function(file) { + dat <- anon_data() + if (is.null(dat)) stop("No anonymized data available to download.") + utils::write.csv(drop_geo_cols(dat), file, row.names = FALSE) + } + ) + + output$download_excel <- downloadHandler( + filename = function() paste0("anonymized_", Sys.Date(), ".xlsx"), + content = function(file) { + dat <- anon_data() + if (is.null(dat)) stop("No anonymized data available to download.") + openxlsx::write.xlsx(drop_geo_cols(dat), file) + } + ) + + output$download_dta <- downloadHandler( + filename = function() paste0("anonymized_", Sys.Date(), ".dta"), + content = function(file) { + dat <- anon_data() + if (is.null(dat)) stop("No anonymized data available to download.") + haven::write_dta(drop_geo_cols(dat), file) + } + ) + + # ---------- Report preview/download ---------------------------------------- + render_html_report <- function() { + req(data(), anon_data(), initial_qids()) + + tmpl <- normalizePath( + file.path(shiny::getShinyOption("appDir") %||% getwd(), "server", "anon", "docs", "report_template.Rmd"), + mustWork = FALSE + ) + + if (!file.exists(tmpl)) { + showNotification(paste0("Missing report template: ", tmpl), type = "error") + return(NULL) + } + + + q_main <- risk_qids(initial_qids()) + vld(need(length(q_main) > 0, "Select non-coordinate QIDs.")) + + before_tbl <- data() %>% + dplyr::group_by(dplyr::across(dplyr::all_of(q_main))) %>% + dplyr::summarise(n = dplyr::n(), .groups = "drop") %>% + dplyr::mutate(r = round(1 / n, 4)) + + bm <- list( + avg = round(mean(before_tbl$r), 4), + max = round(max(before_tbl$r), 4), + pct_unique = paste0(round(mean(before_tbl$n == 1)*100,4), "%") + ) + + df_after <- anon_data() + after_cols_main <- sapply(q_main, get_after_col, df_after = df_after, USE.NAMES = FALSE) + + after_tbl <- df_after %>% + dplyr::group_by(dplyr::across(dplyr::all_of(after_cols_main))) %>% + dplyr::summarise(n = dplyr::n(), .groups = "drop") %>% + dplyr::mutate(r = round(1 / n, 4)) + + am <- list( + avg = round(mean(after_tbl$r), 4), + max = round(max(after_tbl$r), 4), + pct_unique = paste0(round(mean(after_tbl$n == 1)*100,4), "%") + ) + + all_subsets2 <- lapply(seq_along(q_main), function(k) combn(q_main, k, simplify = FALSE)) + subsets <- unlist(all_subsets2, recursive = FALSE) + + perm_tbl_before <- do.call(rbind, lapply(subsets, function(sub) { + df_sub <- data() %>% + dplyr::group_by(dplyr::across(dplyr::all_of(sub))) %>% + dplyr::summarise(n = dplyr::n(), .groups = "drop") %>% + dplyr::mutate(r = round(1 / n, 4)) + data.frame( + QIDs = paste(sub, collapse = ", "), + Average_Risk = round(mean(df_sub$r), 4), + Maximum_Risk = round(max(df_sub$r), 4), + Percent_Unique = paste0(round(mean(df_sub$n == 1)*100,4), "%"), + stringsAsFactors = FALSE + ) + })) + + perm_tbl_after <- do.call(rbind, lapply(subsets, function(sub) { + after_sub <- sapply(sub, get_after_col, df_after = df_after, USE.NAMES = FALSE) + df_sub <- df_after %>% + dplyr::group_by(dplyr::across(dplyr::all_of(after_sub))) %>% + dplyr::summarise(n = dplyr::n(), .groups = "drop") %>% + dplyr::mutate(r = round(1 / n, 4)) + data.frame( + QIDs = paste(sub, collapse = ", "), + Average_Risk = round(mean(df_sub$r), 4), + Maximum_Risk = round(max(df_sub$r), 4), + Percent_Unique = paste0(round(mean(df_sub$n == 1)*100,4), "%"), + stringsAsFactors = FALSE + ) + })) + + tmp_html <- tempfile(fileext = ".html") + rmarkdown::render( + input = tmpl, + output_format = "html_document", + output_file = basename(tmp_html), + output_dir = dirname(tmp_html), + params = list( + before_metrics = bm, + after_metrics = am, + perm_table_before = perm_tbl_before, + perm_table_after = perm_tbl_after + ), + envir = new.env(parent = globalenv()), + quiet = TRUE + ) + + tmp_html + } + + observeEvent(input$view_report, { + req(data(), anon_data(), initial_qids()) + + withProgress(message = "Building report...", value = 0.1, { + path <- render_html_report() + if (is.null(path)) return(NULL) + res_name <- paste0("reports_tmp_", gsub("[^A-Za-z0-9]", "_", landing_dom)) + addResourcePath(res_name, dirname(path)) + report_temp(path) + }) + + if (is.null(report_temp())) return(NULL) + + res_name <- paste0("reports_tmp_", gsub("[^A-Za-z0-9]", "_", landing_dom)) + showModal(modalDialog( + title = "Risk Report Preview", + size = "l", easyClose = TRUE, + tags$iframe( + src = paste0(res_name, "/", basename(report_temp())), + style = "width:100%; height:600px; border:none;" + ), + footer = modalButton("Close") + )) + }) + + output$download_report <- downloadHandler( + filename = function() paste0("risk_report_", Sys.Date(), ".pdf"), + content = function(file) { + req(report_temp()) + if (requireNamespace("pagedown", quietly = TRUE)) { + pagedown::chrome_print(input = report_temp(), output = file) + } else { + file.copy(report_temp(), file, overwrite = TRUE) + } + } + ) +} diff --git a/server/deep_learning.R b/server/deep_learning.R index ddb7824..e288e7f 100644 --- a/server/deep_learning.R +++ b/server/deep_learning.R @@ -4,1078 +4,1078 @@ options(shiny.maxRequestSize = 2000*1024^2) deep_learning = function() { + + api_url <- "http://23.135.236.5:3186" + + # ============================================================================== + # == 1. CORE REACTIVE VALUES + # ============================================================================== + + # --- For Live Job Polling --- + polled_data <- reactiveVal(list( + status = "Idle", task = "N/A", log = "", progress = list(percentage = 0, text = "Idle") + )) + polled_metrics <- reactiveVal(NULL) + active_job_id <- reactiveVal(NULL) + + # --- For Data Management --- + data_upload_status <- reactiveVal("") + processing_dataset_id <- reactiveVal(NULL) + refresh_data_trigger <- reactiveVal(0) # Triggers reload of dataset lists + + # --- For Model Registry --- + model_registry <- reactiveVal(NULL) + + # --- For History Tab --- + history_metrics <- reactiveVal(NULL) + history_jobs_df <- reactiveVal(NULL) + history_poller_active <- reactiveVal(FALSE) + + # --- For Inference Tab --- + obj_inference_result <- reactiveVal(list(status = "Ready", image_url = NULL, error = NULL)) + asr_inference_result <- reactiveVal(list(status = "Ready", transcription = NULL, error = NULL)) + img_class_inference_result <- reactiveVal(list(status = "Ready", prediction = NULL, error = NULL)) + seg_inference_result <- reactiveVal(list(status = "Ready", image_url = NULL, error = NULL)) + + # --- Table Proxies --- + proxy_eval_table <- dataTableProxy("eval_table") + proxy_dataset_table <- dataTableProxy("dataset_table") + proxy_history_eval_table <- dataTableProxy("history_eval_table") + + + # ============================================================================== + # == 2. INITIALIZATION & SIDEBAR LOGIC + # ============================================================================== + + # --- Fetch Model Registry on Startup --- + ## FIXME: This should only happen if deeplearning module is activated + observe({ + tryCatch({ + req <- request(paste0(api_url, "/models/list")) + resp <- req_perform(req) + model_registry(resp_body_json(resp)) + }, error = function(e) { + # print(paste("Failed to fetch model registry:", e$message)) + # TODO: Show a fatal error modal to the user + }) + }) + + # --- Task Panel Switching --- + # Show/hide the correct training UI based on the main task selector + observe({ + task <- input$task_selector + if (task == "object_detection") { + shinyjs::show("obj_panel"); shinyjs::hide("asr_panel"); shinyjs::hide("img_class_panel"); shinyjs::hide("seg_panel") + } else if (task == "asr") { + shinyjs::hide("obj_panel"); shinyjs::show("asr_panel"); shinyjs::hide("img_class_panel"); shinyjs::hide("seg_panel") + } else if (task == "image_classification") { + shinyjs::hide("obj_panel"); shinyjs::hide("asr_panel"); shinyjs::show("img_class_panel"); shinyjs::hide("seg_panel") + } else if (task == "image_segmentation") { + shinyjs::hide("obj_panel"); shinyjs::hide("asr_panel"); shinyjs::hide("img_class_panel"); shinyjs::show("seg_panel") + } + }) + + # --- Chained Dropdown Logic (Populate Architectures) --- + observeEvent(c(model_registry(), input$task_selector), { + req(model_registry()) - api_url <- "http://23.135.236.5:3186" - - # ============================================================================== - # == 1. CORE REACTIVE VALUES - # ============================================================================== - - # --- For Live Job Polling --- - polled_data <- reactiveVal(list( - status = "Idle", task = "N/A", log = "", progress = list(percentage = 0, text = "Idle") - )) - polled_metrics <- reactiveVal(NULL) - active_job_id <- reactiveVal(NULL) - - # --- For Data Management --- - data_upload_status <- reactiveVal("") - processing_dataset_id <- reactiveVal(NULL) - refresh_data_trigger <- reactiveVal(0) # Triggers reload of dataset lists - - # --- For Model Registry --- - model_registry <- reactiveVal(NULL) - - # --- For History Tab --- - history_metrics <- reactiveVal(NULL) - history_jobs_df <- reactiveVal(NULL) - history_poller_active <- reactiveVal(FALSE) - - # --- For Inference Tab --- - obj_inference_result <- reactiveVal(list(status = "Ready", image_url = NULL, error = NULL)) - asr_inference_result <- reactiveVal(list(status = "Ready", transcription = NULL, error = NULL)) - img_class_inference_result <- reactiveVal(list(status = "Ready", prediction = NULL, error = NULL)) - seg_inference_result <- reactiveVal(list(status = "Ready", image_url = NULL, error = NULL)) - - # --- Table Proxies --- - proxy_eval_table <- dataTableProxy("eval_table") - proxy_dataset_table <- dataTableProxy("dataset_table") - proxy_history_eval_table <- dataTableProxy("history_eval_table") - - - # ============================================================================== - # == 2. INITIALIZATION & SIDEBAR LOGIC - # ============================================================================== + task_slug <- input$task_selector + arch_choices <- c("Loading..." = "") - # --- Fetch Model Registry on Startup --- - ## FIXME: This should only happen if deeplearning module is activated - observe({ - tryCatch({ - req <- request(paste0(api_url, "/models/list")) - resp <- req_perform(req) - model_registry(resp_body_json(resp)) - }, error = function(e) { - # print(paste("Failed to fetch model registry:", e$message)) - # TODO: Show a fatal error modal to the user - }) + if (task_slug == "object_detection") { + arch_choices <- names(model_registry()$object_detection) + updateSelectInput(session, "obj_model_arch", choices = arch_choices) + } else if (task_slug == "asr") { + arch_choices <- names(model_registry()$asr) + updateSelectInput(session, "asr_model_arch", choices = arch_choices) + } else if (task_slug == "image_classification") { + arch_choices <- names(model_registry()$image_classification) + updateSelectInput(session, "img_class_model_arch", choices = arch_choices) + } else if (task_slug == "image_segmentation") { + arch_choices <- names(model_registry()$image_segmentation) + updateSelectInput(session, "seg_model_arch", choices = arch_choices) + } + }) + + # --- Chained Dropdown Logic (Populate Checkpoints) --- + observeEvent(input$obj_model_arch, { + req(model_registry(), input$obj_model_arch, input$obj_model_arch != "Loading...") + checkpoints <- model_registry()$object_detection[[input$obj_model_arch]] + updateSelectInput(session, "obj_model_checkpoint", choices = checkpoints) + }) + observeEvent(input$asr_model_arch, { + req(model_registry(), input$asr_model_arch, input$asr_model_arch != "Loading...") + checkpoints <- model_registry()$asr[[input$asr_model_arch]] + updateSelectInput(session, "asr_model_checkpoint", choices = checkpoints) + }) + observeEvent(input$img_class_model_arch, { + req(model_registry(), input$img_class_model_arch, input$img_class_model_arch != "Loading...") + checkpoints <- model_registry()$image_classification[[input$img_class_model_arch]] + updateSelectInput(session, "img_class_model_checkpoint", choices = checkpoints) + }) + observeEvent(input$seg_model_arch, { + req(model_registry(), input$seg_model_arch, input$seg_model_arch != "Loading...") + checkpoints <- model_registry()$image_segmentation[[input$seg_model_arch]] + updateSelectInput(session, "seg_model_checkpoint", choices = checkpoints) + }) + + + # ============================================================================== + # == 3. "DATA MANAGEMENT" TAB LOGIC + # ============================================================================== + + # --- Helper: Load Datasets for a specific task --- + load_datasets_for_task <- function(task_slug) { + tryCatch({ + req <- request(paste0(api_url, "/data/list/", task_slug)) + resp_data <- resp_body_json(req_perform(req), simplifyVector = TRUE) + if (length(resp_data) > 0 && nrow(resp_data) > 0) { + setNames(resp_data$id, resp_data$name) + } else { + c("No datasets found" = "") + } + }, error = function(e) { + c("Error loading datasets" = "") }) - - # --- Task Panel Switching --- - # Show/hide the correct training UI based on the main task selector - observe({ - task <- input$task_selector - if (task == "object_detection") { - shinyjs::show("obj_panel"); shinyjs::hide("asr_panel"); shinyjs::hide("img_class_panel"); shinyjs::hide("seg_panel") - } else if (task == "asr") { - shinyjs::hide("obj_panel"); shinyjs::show("asr_panel"); shinyjs::hide("img_class_panel"); shinyjs::hide("seg_panel") - } else if (task == "image_classification") { - shinyjs::hide("obj_panel"); shinyjs::hide("asr_panel"); shinyjs::show("img_class_panel"); shinyjs::hide("seg_panel") - } else if (task == "image_segmentation") { - shinyjs::hide("obj_panel"); shinyjs::hide("asr_panel"); shinyjs::hide("img_class_panel"); shinyjs::show("seg_panel") - } + } + + # --- Auto-refresh Dataset Dropdowns --- + # Triggered by: 1. Task selector change, 2. Data refresh trigger + observeEvent(c(input$task_selector, refresh_data_trigger()), { + task_slug <- input$task_selector + if (task_slug == "object_detection") { + updateSelectInput(session, "obj_dataset_id", choices = load_datasets_for_task("object_detection")) + } else if (task_slug == "asr") { + updateSelectInput(session, "asr_dataset_id", choices = load_datasets_for_task("asr")) + } else if (task_slug == "image_classification") { + updateSelectInput(session, "img_class_dataset_id", choices = load_datasets_for_task("image_classification")) + } else if (task_slug == "image_segmentation") { + updateSelectInput(session, "seg_dataset_id", choices = load_datasets_for_task("image_segmentation")) + } + }, ignoreNULL = TRUE, ignoreInit = TRUE) + + # Manually trigger first data load on startup (after registry is loaded) + observeEvent(model_registry(), { + req(model_registry()) + refresh_data_trigger(refresh_data_trigger() + 1) + }, once = TRUE) + + + # --- Handle Dataset Upload Button --- + observeEvent(input$start_data_upload, { + req(input$new_data_zip, input$new_data_name, input$new_data_task_type) + data_upload_status("Uploading...") + tryCatch({ + req <- request(paste0(api_url, "/data/upload/", input$new_data_task_type)) %>% + req_body_multipart( + data_name = input$new_data_name, + data_zip = curl::form_file(input$new_data_zip$datapath, type = "application/zip") + ) + resp <- req_perform(req) + resp_data <- resp_body_json(resp) + # Start the poller + processing_dataset_id(resp_data$dataset_id) + data_upload_status(paste("Success! Dataset", input$new_data_name, "is processing...")) + + }, error = function(e) { + error_message <- as.character(e$message) + if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } + data_upload_status(paste("Error:", error_message)) }) + }) + + # --- Poller for Data Processing Status --- + observe({ + ds_id <- processing_dataset_id() + req(ds_id) # Only run if we are processing a dataset - # --- Chained Dropdown Logic (Populate Architectures) --- - observeEvent(c(model_registry(), input$task_selector), { - req(model_registry()) + invalidateLater(2000, session) # Poll every 2 seconds + + tryCatch({ + req_status <- request(paste0(api_url, "/data/status/", ds_id)) + resp <- req_perform(req_status) + status_data <- resp_body_json(resp) + + if (status_data$status == "ready" || status_data$status == "failed") { + processing_dataset_id(NULL) # Stop polling - task_slug <- input$task_selector - arch_choices <- c("Loading..." = "") + # --- AUTO-REFRESH --- + refresh_data_trigger(refresh_data_trigger() + 1) - if (task_slug == "object_detection") { - arch_choices <- names(model_registry()$object_detection) - updateSelectInput(session, "obj_model_arch", choices = arch_choices) - } else if (task_slug == "asr") { - arch_choices <- names(model_registry()$asr) - updateSelectInput(session, "asr_model_arch", choices = arch_choices) - } else if (task_slug == "image_classification") { - arch_choices <- names(model_registry()$image_classification) - updateSelectInput(session, "img_class_model_arch", choices = arch_choices) - } else if (task_slug == "image_segmentation") { - arch_choices <- names(model_registry()$image_segmentation) - updateSelectInput(session, "seg_model_arch", choices = arch_choices) + if(status_data$status == "ready") { + data_upload_status(paste("Dataset processing complete!")) + } else { + data_upload_status(paste("Dataset processing failed:", status_data$error)) } + + } else { + data_upload_status(paste("Processing dataset...", status_data$status)) + } + }, error = function(e) { + data_upload_status("Error polling data status.") + processing_dataset_id(NULL) # Stop polling on error }) + }) + + # --- Data Management UI Outputs --- + output$data_upload_status <- renderText({ data_upload_status() }) + + output$dataset_table <- renderDT({ + refresh_data_trigger() # React to the trigger - # --- Chained Dropdown Logic (Populate Checkpoints) --- - observeEvent(input$obj_model_arch, { - req(model_registry(), input$obj_model_arch, input$obj_model_arch != "Loading...") - checkpoints <- model_registry()$object_detection[[input$obj_model_arch]] - updateSelectInput(session, "obj_model_checkpoint", choices = checkpoints) - }) - observeEvent(input$asr_model_arch, { - req(model_registry(), input$asr_model_arch, input$asr_model_arch != "Loading...") - checkpoints <- model_registry()$asr[[input$asr_model_arch]] - updateSelectInput(session, "asr_model_checkpoint", choices = checkpoints) - }) - observeEvent(input$img_class_model_arch, { - req(model_registry(), input$img_class_model_arch, input$img_class_model_arch != "Loading...") - checkpoints <- model_registry()$image_classification[[input$img_class_model_arch]] - updateSelectInput(session, "img_class_model_checkpoint", choices = checkpoints) + tryCatch({ + tasks <- c("object_detection", "asr", "image_classification", "image_segmentation") + all_datasets <- lapply(tasks, function(task) { + req <- request(paste0(api_url, "/data/list/", task)) + resp_data <- resp_body_json(req_perform(req), simplifyVector = TRUE) + if (length(resp_data) > 0 && nrow(resp_data) > 0) { + resp_data$task_type <- task + return(resp_data) + } + return(NULL) + }) + bind_rows(all_datasets) + }, error = function(e) { + data.frame(name = "Error loading dataset list.", task_type = e$message) }) - observeEvent(input$seg_model_arch, { - req(model_registry(), input$seg_model_arch, input$seg_model_arch != "Loading...") - checkpoints <- model_registry()$image_segmentation[[input$seg_model_arch]] - updateSelectInput(session, "seg_model_checkpoint", choices = checkpoints) + }) + + + # ============================================================================== + # == 4. TRAINING JOB SUBMISSION (One per task) + # ============================================================================== + + # --- Helper: Resets UI before starting a job --- + reset_live_training_ui <- function(task_name) { + replaceData(proxy_eval_table, NULL, resetPaging = TRUE, clearSelection = TRUE) + active_job_id(NULL) + polled_metrics(NULL) + polled_data(list(status = "Submitting...", task = task_name, log = "Submitting job...")) + updateTabsetPanel(session, "main_tabs", selected = "Live Training") + } + + # --- 4.1: Object Detection Job --- + observeEvent(input$start_obj_job, { + req(input$obj_dataset_id, input$obj_model_checkpoint) + reset_live_training_ui("Object Detection") + + tryCatch({ + req <- request(paste0(api_url, "/train/object-detection")) %>% + req_body_multipart( + # Common Params + dataset_id = as.character(input$obj_dataset_id), + model_checkpoint = as.character(input$obj_model_checkpoint), + run_name = as.character(input$obj_run_name), + version = as.character(input$obj_version), + epochs = as.character(input$obj_epochs), + train_batch_size = as.character(input$obj_train_batch_size), + eval_batch_size = as.character(input$obj_eval_batch_size), + seed = as.character(input$obj_seed), + num_proc = as.character(input$obj_num_proc), + early_stopping_patience = as.character(input$obj_early_stopping_patience), + push_to_hub = as.character(input$obj_push_to_hub), + hub_user_id = as.character(input$obj_hub_user_id), + log_to_wandb = as.character(input$obj_log_to_wandb), + wandb_project = as.character(input$obj_wandb_project), + wandb_entity = as.character(input$obj_wandb_entity), + max_image_size = as.character(input$obj_max_image_size), + + # HF-Specific Params + learning_rate = as.character(input$obj_learning_rate), + weight_decay = as.character(input$obj_weight_decay), + gradient_accumulation_steps = as.character(input$obj_gradient_accumulation_steps), + gradient_checkpointing = as.character(input$obj_gradient_checkpointing), + max_grad_norm = as.character(input$obj_max_grad_norm), + fp16 = as.character(input$obj_fp16), + force_preprocess = as.character(input$obj_force_preprocess), + early_stopping_threshold = as.character(input$obj_early_stopping_threshold), + + # YOLO-Specific Params + warmup_epochs = as.character(input$obj_yolo_warmup_epochs), + lr0 = as.character(input$obj_yolo_lr0), + momentum = as.character(input$obj_yolo_momentum), + optimizer = as.character(input$obj_yolo_optimizer), + weight_decay_yolo = as.character(input$obj_yolo_weight_decay) + ) + + resp <- req_perform(req) + resp_data <- resp_body_json(resp) + active_job_id(resp_data$job_id) + polled_data(list(status = "Queued", task = "Object Detection", log = "Job is queued.")) + + }, error = function(e) { + error_message <- as.character(e$message) + if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } + polled_data(list(status = "Error", task = "Object Detection", log = error_message)) }) - + }) + + # --- 4.2: ASR Job --- + observeEvent(input$start_asr_job, { + req(input$asr_dataset_id, input$asr_model_checkpoint) + reset_live_training_ui("ASR") - # ============================================================================== - # == 3. "DATA MANAGEMENT" TAB LOGIC - # ============================================================================== - - # --- Helper: Load Datasets for a specific task --- - load_datasets_for_task <- function(task_slug) { - tryCatch({ - req <- request(paste0(api_url, "/data/list/", task_slug)) - resp_data <- resp_body_json(req_perform(req), simplifyVector = TRUE) - if (length(resp_data) > 0 && nrow(resp_data) > 0) { - setNames(resp_data$id, resp_data$name) - } else { - c("No datasets found" = "") - } - }, error = function(e) { - c("Error loading datasets" = "") - }) + outlier_val <- input$outlier_std_devs + if (is.null(outlier_val) || is.na(outlier_val) || !is.numeric(outlier_val) || !input$asr_apply_outlier_filtering) { + outlier_val <- 2.0 } - # --- Auto-refresh Dataset Dropdowns --- - # Triggered by: 1. Task selector change, 2. Data refresh trigger - observeEvent(c(input$task_selector, refresh_data_trigger()), { - task_slug <- input$task_selector - if (task_slug == "object_detection") { - updateSelectInput(session, "obj_dataset_id", choices = load_datasets_for_task("object_detection")) - } else if (task_slug == "asr") { - updateSelectInput(session, "asr_dataset_id", choices = load_datasets_for_task("asr")) - } else if (task_slug == "image_classification") { - updateSelectInput(session, "img_class_dataset_id", choices = load_datasets_for_task("image_classification")) - } else if (task_slug == "image_segmentation") { - updateSelectInput(session, "seg_dataset_id", choices = load_datasets_for_task("image_segmentation")) - } - }, ignoreNULL = TRUE, ignoreInit = TRUE) - - # Manually trigger first data load on startup (after registry is loaded) - observeEvent(model_registry(), { - req(model_registry()) - refresh_data_trigger(refresh_data_trigger() + 1) - }, once = TRUE) - + max_hours <- if (is.na(input$asr_max_train_hours) || is.null(input$asr_max_train_hours)) NULL else as.character(input$asr_max_train_hours) - # --- Handle Dataset Upload Button --- - observeEvent(input$start_data_upload, { - req(input$new_data_zip, input$new_data_name, input$new_data_task_type) - data_upload_status("Uploading...") - tryCatch({ - req <- request(paste0(api_url, "/data/upload/", input$new_data_task_type)) %>% - req_body_multipart( - data_name = input$new_data_name, - data_zip = curl::form_file(input$new_data_zip$datapath, type = "application/zip") - ) - resp <- req_perform(req) - resp_data <- resp_body_json(resp) - # Start the poller - processing_dataset_id(resp_data$dataset_id) - data_upload_status(paste("Success! Dataset", input$new_data_name, "is processing...")) - - }, error = function(e) { - error_message <- as.character(e$message) - if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } - data_upload_status(paste("Error:", error_message)) - }) + tryCatch({ + req_list <- list( + dataset_id = as.character(input$asr_dataset_id), + model_checkpoint = as.character(input$asr_model_checkpoint), + run_name = as.character(input$asr_run_name), + version = as.character(input$asr_version), + language = as.character(input$asr_language), + language_code = as.character(input$asr_language_code), + speaker_id_column = as.character(input$asr_speaker_id_column), + text_column = as.character(input$asr_text_column), + target_sampling_rate = as.character(input$asr_target_sampling_rate), + min_duration_s = as.character(input$asr_min_duration_s), + max_duration_s = as.character(input$asr_max_duration_s), + min_transcript_len = as.character(input$asr_min_transcript_len), + max_transcript_len = as.character(input$asr_max_transcript_len), + apply_outlier_filtering = as.character(input$asr_apply_outlier_filtering), + outlier_std_devs = as.character(outlier_val), + is_presplit = as.character(input$asr_is_presplit), + speaker_disjointness = as.character(input$asr_speaker_disjointness), + train_ratio = as.character(input$asr_train_ratio), + dev_ratio = as.character(input$asr_dev_ratio), + test_ratio = as.character(input$asr_test_ratio), + epochs = as.character(input$asr_epochs), + learning_rate = as.character(input$asr_learning_rate), + lr_scheduler_type = as.character(input$asr_lr_scheduler_type), + warmup_ratio = as.character(input$asr_warmup_ratio), + train_batch_size = as.character(input$asr_train_batch_size), + eval_batch_size = as.character(input$asr_eval_batch_size), + gradient_accumulation_steps = as.character(input$asr_gradient_accumulation_steps), + gradient_checkpointing = as.character(input$asr_gradient_checkpointing), + optimizer = as.character(input$asr_optimizer), + early_stopping_patience = as.character(input$asr_early_stopping_patience), + early_stopping_threshold = as.character(input$asr_early_stopping_threshold), + push_to_hub = as.character(input$asr_push_to_hub), + hub_user_id = as.character(input$asr_hub_user_id), + hub_private_repo = as.character(input$asr_hub_private_repo), + log_to_wandb = as.character(input$asr_log_to_wandb), + wandb_project = as.character(input$asr_wandb_project), + wandb_entity = as.character(input$asr_wandb_entity), + seed = as.character(input$asr_seed), + num_proc = as.character(input$asr_num_proc), + max_train_hours = max_hours + ) + + req_list <- req_list[!sapply(req_list, is.null)] + + req <- request(paste0(api_url, "/train/asr")) %>% + req_body_multipart(!!!req_list) + + resp <- req_perform(req) + resp_data <- resp_body_json(resp) + active_job_id(resp_data$job_id) + polled_data(list(status = "Queued", task = "ASR", log = "Job is queued.")) + + }, error = function(e) { + error_message <- as.character(e$message) + if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } + polled_data(list(status = "Error", task = "ASR", log = error_message)) }) + }) + + # --- 4.3: Image Classification Job --- + observeEvent(input$start_img_class_job, { + req(input$img_class_dataset_id, input$img_class_model_checkpoint) + reset_live_training_ui("Image Classification") - # --- Poller for Data Processing Status --- - observe({ - ds_id <- processing_dataset_id() - req(ds_id) # Only run if we are processing a dataset - - invalidateLater(2000, session) # Poll every 2 seconds - - tryCatch({ - req_status <- request(paste0(api_url, "/data/status/", ds_id)) - resp <- req_perform(req_status) - status_data <- resp_body_json(resp) - - if (status_data$status == "ready" || status_data$status == "failed") { - processing_dataset_id(NULL) # Stop polling - - # --- AUTO-REFRESH --- - refresh_data_trigger(refresh_data_trigger() + 1) - - if(status_data$status == "ready") { - data_upload_status(paste("Dataset processing complete!")) - } else { - data_upload_status(paste("Dataset processing failed:", status_data$error)) - } - - } else { - data_upload_status(paste("Processing dataset...", status_data$status)) - } - }, error = function(e) { - data_upload_status("Error polling data status.") - processing_dataset_id(NULL) # Stop polling on error - }) + tryCatch({ + req <- request(paste0(api_url, "/train/image-classification")) %>% + req_body_multipart( + dataset_id = as.character(input$img_class_dataset_id), + model_checkpoint = as.character(input$img_class_model_checkpoint), + run_name = as.character(input$img_class_run_name), + version = as.character(input$img_class_version), + epochs = as.character(input$img_class_epochs), + learning_rate = as.character(input$img_class_learning_rate), + weight_decay = as.character(input$img_class_weight_decay), + train_batch_size = as.character(input$img_class_train_batch_size), + eval_batch_size = as.character(input$img_class_eval_batch_size), + max_image_size = as.character(input$img_class_max_image_size), + gradient_accumulation_steps = as.character(input$img_class_grad_accum), + gradient_checkpointing = as.character(input$img_class_grad_check), + fp16 = as.character(input$img_class_fp16), + seed = as.character(input$img_class_seed), + early_stopping_patience = as.character(input$img_class_early_stop), + push_to_hub = as.character(input$img_class_push_to_hub), + hub_user_id = as.character(input$img_class_hub_user_id), + log_to_wandb = as.character(input$img_class_log_to_wandb), + wandb_project = as.character(input$img_class_wandb_project), + wandb_entity = as.character(input$img_class_wandb_entity), + num_proc = as.character(input$img_class_num_proc), + is_presplit = as.character(input$img_class_is_presplit), + train_ratio = as.character(input$img_class_train_ratio), + dev_ratio = as.character(input$img_class_dev_ratio) + ) + + resp <- req_perform(req) + active_job_id(resp_body_json(resp)$job_id) + }, error = function(e) { + polled_data(list(status = "Error", task = "Image Classification", log = as.character(e))) }) + }) + + # --- 4.4: Image Segmentation Job --- + observeEvent(input$start_seg_job, { + req(input$seg_dataset_id, input$seg_model_checkpoint) + reset_live_training_ui("Image Segmentation") - # --- Data Management UI Outputs --- - output$data_upload_status <- renderText({ data_upload_status() }) - - output$dataset_table <- renderDT({ - refresh_data_trigger() # React to the trigger - - tryCatch({ - tasks <- c("object_detection", "asr", "image_classification", "image_segmentation") - all_datasets <- lapply(tasks, function(task) { - req <- request(paste0(api_url, "/data/list/", task)) - resp_data <- resp_body_json(req_perform(req), simplifyVector = TRUE) - if (length(resp_data) > 0 && nrow(resp_data) > 0) { - resp_data$task_type <- task - return(resp_data) - } - return(NULL) - }) - bind_rows(all_datasets) - }, error = function(e) { - data.frame(name = "Error loading dataset list.", task_type = e$message) - }) + tryCatch({ + req <- request(paste0(api_url, "/train/image-segmentation")) %>% + req_body_multipart( + dataset_id = as.character(input$seg_dataset_id), + model_checkpoint = as.character(input$seg_model_checkpoint), + run_name = as.character(input$seg_run_name), + version = as.character(input$seg_version), + epochs = as.character(input$seg_epochs), + learning_rate = as.character(input$seg_learning_rate), + weight_decay = as.character(input$seg_weight_decay), + train_batch_size = as.character(input$seg_train_batch_size), + eval_batch_size = as.character(input$seg_eval_batch_size), + max_image_size = as.character(input$seg_max_image_size), + gradient_accumulation_steps = as.character(input$seg_grad_accum), + gradient_checkpointing = as.character(input$seg_grad_check), + fp16 = as.character(input$seg_fp16), + seed = as.character(input$seg_seed), + early_stopping_patience = as.character(input$seg_early_stop), + push_to_hub = as.character(input$seg_push_to_hub), + hub_user_id = as.character(input$seg_hub_user_id), + log_to_wandb = as.character(input$seg_log_to_wandb), + wandb_project = as.character(input$seg_wandb_project), + wandb_entity = as.character(input$seg_wandb_entity), + num_proc = as.character(input$seg_num_proc), + is_presplit = as.character(input$seg_is_presplit), + train_ratio = as.character(input$seg_train_ratio), + dev_ratio = as.character(input$seg_dev_ratio) + ) + + resp <- req_perform(req) + active_job_id(resp_body_json(resp)$job_id) + }, error = function(e) { + error_message <- as.character(e$message) + if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } + polled_data(list(status = "Error", task = "Image Segmentation", log = error_message)) }) - + }) + + + # ============================================================================== + # == 5. "LIVE TRAINING" TAB LOGIC + # ============================================================================== + + # --- 5.1: Job Polling & Status Display --- + observe({ + job_id <- active_job_id() + current_status <- polled_data()$status - # ============================================================================== - # == 4. TRAINING JOB SUBMISSION (One per task) - # ============================================================================== + # Only poll if we have an active job that isn't finished + if (!is.null(job_id) && !(current_status %in% c("completed", "failed", "Error", "Polling Error"))) { + invalidateLater(2000, session) + + # Poll for Status & Log + tryCatch({ + req_status <- request(paste0(api_url, "/status/", job_id)) + resp_status <- req_perform(req_status) + if (resp_status(resp_status) == 200) { + polled_data(resp_body_json(resp_status)) + } + }, error = function(e) { + current_data <- polled_data() + current_data$status <- "Polling Error" + polled_data(current_data) + }) + + # Poll for Metrics + tryCatch({ + req_metrics <- request(paste0(api_url, "/metrics/", job_id)) + resp_metrics <- req_perform(req_metrics) + if (resp_status(resp_metrics) == 200) { + polled_metrics(resp_body_json(resp_metrics)) + } + }, error = function(e) { + polled_metrics(NULL) + }) + } + }) + + # --- 5.2: Job Status Panel Outputs --- + output$job_task_display <- renderText({ polled_data()$task }) + output$job_id_display <- renderText({ ifelse(is.null(active_job_id()), "None", active_job_id()) }) + output$job_status_display <- renderText({ polled_data()$status }) + + # --- 5.3: Metrics Table Panel Output --- + output$eval_table <- renderDT({ + metrics_list <- polled_metrics() + req(metrics_list, length(metrics_list) > 0) + + metrics_df <- bind_rows(metrics_list) - # --- Helper: Resets UI before starting a job --- - reset_live_training_ui <- function(task_name) { - replaceData(proxy_eval_table, NULL, resetPaging = TRUE, clearSelection = TRUE) - active_job_id(NULL) - polled_metrics(NULL) - polled_data(list(status = "Submitting...", task = task_name, log = "Submitting job...")) - updateTabsetPanel(session, "main_tabs", selected = "Live Training") + if (nrow(metrics_df) > 0) { + display_df <- metrics_df %>% + pivot_longer( + cols = starts_with("eval_") | starts_with("test_"), + names_to = "metric_name", + values_to = "value", + values_drop_na = TRUE + ) %>% + separate(metric_name, into = c("step", "metric"), sep = "_", extra = "merge") %>% + pivot_wider( + names_from = metric, + values_from = value, + values_fn = first + ) %>% + select(any_of(c("step", "epoch", "loss", "map", "wer", "cer", "mean_iou")), everything()) + + datatable( + display_df, + options = list( + pageLength = 5, + scrollX = TRUE, + searching = FALSE, + autoWidth = TRUE, + class = 'cell-border stripe' + ), + rownames = FALSE + ) + } else { + return(NULL) } - - # --- 4.1: Object Detection Job --- - observeEvent(input$start_obj_job, { - req(input$obj_dataset_id, input$obj_model_checkpoint) - reset_live_training_ui("Object Detection") - - tryCatch({ - req <- request(paste0(api_url, "/train/object-detection")) %>% - req_body_multipart( - # Common Params - dataset_id = as.character(input$obj_dataset_id), - model_checkpoint = as.character(input$obj_model_checkpoint), - run_name = as.character(input$obj_run_name), - version = as.character(input$obj_version), - epochs = as.character(input$obj_epochs), - train_batch_size = as.character(input$obj_train_batch_size), - eval_batch_size = as.character(input$obj_eval_batch_size), - seed = as.character(input$obj_seed), - num_proc = as.character(input$obj_num_proc), - early_stopping_patience = as.character(input$obj_early_stopping_patience), - push_to_hub = as.character(input$obj_push_to_hub), - hub_user_id = as.character(input$obj_hub_user_id), - log_to_wandb = as.character(input$obj_log_to_wandb), - wandb_project = as.character(input$obj_wandb_project), - wandb_entity = as.character(input$obj_wandb_entity), - max_image_size = as.character(input$obj_max_image_size), - - # HF-Specific Params - learning_rate = as.character(input$obj_learning_rate), - weight_decay = as.character(input$obj_weight_decay), - gradient_accumulation_steps = as.character(input$obj_gradient_accumulation_steps), - gradient_checkpointing = as.character(input$obj_gradient_checkpointing), - max_grad_norm = as.character(input$obj_max_grad_norm), - fp16 = as.character(input$obj_fp16), - force_preprocess = as.character(input$obj_force_preprocess), - early_stopping_threshold = as.character(input$obj_early_stopping_threshold), - - # YOLO-Specific Params - warmup_epochs = as.character(input$obj_yolo_warmup_epochs), - lr0 = as.character(input$obj_yolo_lr0), - momentum = as.character(input$obj_yolo_momentum), - optimizer = as.character(input$obj_yolo_optimizer), - weight_decay_yolo = as.character(input$obj_yolo_weight_decay) - ) - - resp <- req_perform(req) - resp_data <- resp_body_json(resp) - active_job_id(resp_data$job_id) - polled_data(list(status = "Queued", task = "Object Detection", log = "Job is queued.")) - - }, error = function(e) { - error_message <- as.character(e$message) - if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } - polled_data(list(status = "Error", task = "Object Detection", log = error_message)) - }) - }) + }) + + # --- 5.4: Full Log Panel Output --- + output$log_output <- renderText({ + log_text <- polled_data()$log + if (is.null(log_text) || nchar(log_text) == 0) { + return("No log output...") + } + return(log_text) + }) + + # --- 5.5: Plotting Logic for "Live Training" Tab --- + metrics_for_plotting <- reactive({ + metrics_list <- polled_metrics() + req(metrics_list, length(metrics_list) > 0) - # --- 4.2: ASR Job --- - observeEvent(input$start_asr_job, { - req(input$asr_dataset_id, input$asr_model_checkpoint) - reset_live_training_ui("ASR") - - outlier_val <- input$outlier_std_devs - if (is.null(outlier_val) || is.na(outlier_val) || !is.numeric(outlier_val) || !input$asr_apply_outlier_filtering) { - outlier_val <- 2.0 - } - - max_hours <- if (is.na(input$asr_max_train_hours) || is.null(input$asr_max_train_hours)) NULL else as.character(input$asr_max_train_hours) - - tryCatch({ - req_list <- list( - dataset_id = as.character(input$asr_dataset_id), - model_checkpoint = as.character(input$asr_model_checkpoint), - run_name = as.character(input$asr_run_name), - version = as.character(input$asr_version), - language = as.character(input$asr_language), - language_code = as.character(input$asr_language_code), - speaker_id_column = as.character(input$asr_speaker_id_column), - text_column = as.character(input$asr_text_column), - target_sampling_rate = as.character(input$asr_target_sampling_rate), - min_duration_s = as.character(input$asr_min_duration_s), - max_duration_s = as.character(input$asr_max_duration_s), - min_transcript_len = as.character(input$asr_min_transcript_len), - max_transcript_len = as.character(input$asr_max_transcript_len), - apply_outlier_filtering = as.character(input$asr_apply_outlier_filtering), - outlier_std_devs = as.character(outlier_val), - is_presplit = as.character(input$asr_is_presplit), - speaker_disjointness = as.character(input$asr_speaker_disjointness), - train_ratio = as.character(input$asr_train_ratio), - dev_ratio = as.character(input$asr_dev_ratio), - test_ratio = as.character(input$asr_test_ratio), - epochs = as.character(input$asr_epochs), - learning_rate = as.character(input$asr_learning_rate), - lr_scheduler_type = as.character(input$asr_lr_scheduler_type), - warmup_ratio = as.character(input$asr_warmup_ratio), - train_batch_size = as.character(input$asr_train_batch_size), - eval_batch_size = as.character(input$asr_eval_batch_size), - gradient_accumulation_steps = as.character(input$asr_gradient_accumulation_steps), - gradient_checkpointing = as.character(input$asr_gradient_checkpointing), - optimizer = as.character(input$asr_optimizer), - early_stopping_patience = as.character(input$asr_early_stopping_patience), - early_stopping_threshold = as.character(input$asr_early_stopping_threshold), - push_to_hub = as.character(input$asr_push_to_hub), - hub_user_id = as.character(input$asr_hub_user_id), - hub_private_repo = as.character(input$asr_hub_private_repo), - log_to_wandb = as.character(input$asr_log_to_wandb), - wandb_project = as.character(input$asr_wandb_project), - wandb_entity = as.character(input$asr_wandb_entity), - seed = as.character(input$asr_seed), - num_proc = as.character(input$asr_num_proc), - max_train_hours = max_hours - ) - - req_list <- req_list[!sapply(req_list, is.null)] - - req <- request(paste0(api_url, "/train/asr")) %>% - req_body_multipart(!!!req_list) - - resp <- req_perform(req) - resp_data <- resp_body_json(resp) - active_job_id(resp_data$job_id) - polled_data(list(status = "Queued", task = "ASR", log = "Job is queued.")) - - }, error = function(e) { - error_message <- as.character(e$message) - if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } - polled_data(list(status = "Error", task = "ASR", log = error_message)) - }) - }) - - # --- 4.3: Image Classification Job --- - observeEvent(input$start_img_class_job, { - req(input$img_class_dataset_id, input$img_class_model_checkpoint) - reset_live_training_ui("Image Classification") - - tryCatch({ - req <- request(paste0(api_url, "/train/image-classification")) %>% - req_body_multipart( - dataset_id = as.character(input$img_class_dataset_id), - model_checkpoint = as.character(input$img_class_model_checkpoint), - run_name = as.character(input$img_class_run_name), - version = as.character(input$img_class_version), - epochs = as.character(input$img_class_epochs), - learning_rate = as.character(input$img_class_learning_rate), - weight_decay = as.character(input$img_class_weight_decay), - train_batch_size = as.character(input$img_class_train_batch_size), - eval_batch_size = as.character(input$img_class_eval_batch_size), - max_image_size = as.character(input$img_class_max_image_size), - gradient_accumulation_steps = as.character(input$img_class_grad_accum), - gradient_checkpointing = as.character(input$img_class_grad_check), - fp16 = as.character(input$img_class_fp16), - seed = as.character(input$img_class_seed), - early_stopping_patience = as.character(input$img_class_early_stop), - push_to_hub = as.character(input$img_class_push_to_hub), - hub_user_id = as.character(input$img_class_hub_user_id), - log_to_wandb = as.character(input$img_class_log_to_wandb), - wandb_project = as.character(input$img_class_wandb_project), - wandb_entity = as.character(input$img_class_wandb_entity), - num_proc = as.character(input$img_class_num_proc), - is_presplit = as.character(input$img_class_is_presplit), - train_ratio = as.character(input$img_class_train_ratio), - dev_ratio = as.character(input$img_class_dev_ratio) - ) - - resp <- req_perform(req) - active_job_id(resp_body_json(resp)$job_id) - }, error = function(e) { - polled_data(list(status = "Error", task = "Image Classification", log = as.character(e))) - }) - }) + bind_rows(metrics_list) %>% + filter(!is.na(epoch)) %>% + filter(if_any(everything(), ~ !is.na(.))) %>% + select(starts_with("eval_"), epoch) %>% + arrange(epoch) %>% + distinct(epoch, .keep_all = TRUE) + }) + + output$metric_selector_ui <- renderUI({ + df <- tryCatch(metrics_for_plotting(), error = function(e) NULL) - # --- 4.4: Image Segmentation Job --- - observeEvent(input$start_seg_job, { - req(input$seg_dataset_id, input$seg_model_checkpoint) - reset_live_training_ui("Image Segmentation") - - tryCatch({ - req <- request(paste0(api_url, "/train/image-segmentation")) %>% - req_body_multipart( - dataset_id = as.character(input$seg_dataset_id), - model_checkpoint = as.character(input$seg_model_checkpoint), - run_name = as.character(input$seg_run_name), - version = as.character(input$seg_version), - epochs = as.character(input$seg_epochs), - learning_rate = as.character(input$seg_learning_rate), - weight_decay = as.character(input$seg_weight_decay), - train_batch_size = as.character(input$seg_train_batch_size), - eval_batch_size = as.character(input$seg_eval_batch_size), - max_image_size = as.character(input$seg_max_image_size), - gradient_accumulation_steps = as.character(input$seg_grad_accum), - gradient_checkpointing = as.character(input$seg_grad_check), - fp16 = as.character(input$seg_fp16), - seed = as.character(input$seg_seed), - early_stopping_patience = as.character(input$seg_early_stop), - push_to_hub = as.character(input$seg_push_to_hub), - hub_user_id = as.character(input$seg_hub_user_id), - log_to_wandb = as.character(input$seg_log_to_wandb), - wandb_project = as.character(input$seg_wandb_project), - wandb_entity = as.character(input$seg_wandb_entity), - num_proc = as.character(input$seg_num_proc), - is_presplit = as.character(input$seg_is_presplit), - train_ratio = as.character(input$seg_train_ratio), - dev_ratio = as.character(input$seg_dev_ratio) - ) - - resp <- req_perform(req) - active_job_id(resp_body_json(resp)$job_id) - }, error = function(e) { - error_message <- as.character(e$message) - if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } - polled_data(list(status = "Error", task = "Image Segmentation", log = error_message)) - }) - }) - - - # ============================================================================== - # == 5. "LIVE TRAINING" TAB LOGIC - # ============================================================================== - - # --- 5.1: Job Polling & Status Display --- - observe({ - job_id <- active_job_id() - current_status <- polled_data()$status - - # Only poll if we have an active job that isn't finished - if (!is.null(job_id) && !(current_status %in% c("completed", "failed", "Error", "Polling Error"))) { - invalidateLater(2000, session) - - # Poll for Status & Log - tryCatch({ - req_status <- request(paste0(api_url, "/status/", job_id)) - resp_status <- req_perform(req_status) - if (resp_status(resp_status) == 200) { - polled_data(resp_body_json(resp_status)) - } - }, error = function(e) { - current_data <- polled_data() - current_data$status <- "Polling Error" - polled_data(current_data) - }) - - # Poll for Metrics - tryCatch({ - req_metrics <- request(paste0(api_url, "/metrics/", job_id)) - resp_metrics <- req_perform(req_metrics) - if (resp_status(resp_metrics) == 200) { - polled_metrics(resp_body_json(resp_metrics)) - } - }, error = function(e) { - polled_metrics(NULL) - }) - } - }) + if (is.null(df) || nrow(df) == 0) { + return(p("Waiting for first evaluation epoch to complete...")) + } - # --- 5.2: Job Status Panel Outputs --- - output$job_task_display <- renderText({ polled_data()$task }) - output$job_id_display <- renderText({ ifelse(is.null(active_job_id()), "None", active_job_id()) }) - output$job_status_display <- renderText({ polled_data()$status }) + metric_names <- names(df)[sapply(df, is.numeric) & !names(df) %in% c("epoch", "step", "runtime", "samples_per_second", "steps_per_second")] - # --- 5.3: Metrics Table Panel Output --- - output$eval_table <- renderDT({ - metrics_list <- polled_metrics() - req(metrics_list, length(metrics_list) > 0) - - metrics_df <- bind_rows(metrics_list) - - if (nrow(metrics_df) > 0) { - display_df <- metrics_df %>% - pivot_longer( - cols = starts_with("eval_") | starts_with("test_"), - names_to = "metric_name", - values_to = "value", - values_drop_na = TRUE - ) %>% - separate(metric_name, into = c("step", "metric"), sep = "_", extra = "merge") %>% - pivot_wider( - names_from = metric, - values_from = value, - values_fn = first - ) %>% - select(any_of(c("step", "epoch", "loss", "map", "wer", "cer", "mean_iou")), everything()) - - datatable( - display_df, - options = list( - pageLength = 5, - scrollX = TRUE, - searching = FALSE, - autoWidth = TRUE, - class = 'cell-border stripe' - ), - rownames = FALSE - ) - } else { - return(NULL) - } - }) - - # --- 5.4: Full Log Panel Output --- - output$log_output <- renderText({ - log_text <- polled_data()$log - if (is.null(log_text) || nchar(log_text) == 0) { - return("No log output...") - } - return(log_text) - }) - - # --- 5.5: Plotting Logic for "Live Training" Tab --- - metrics_for_plotting <- reactive({ - metrics_list <- polled_metrics() - req(metrics_list, length(metrics_list) > 0) - - bind_rows(metrics_list) %>% - filter(!is.na(epoch)) %>% - filter(if_any(everything(), ~ !is.na(.))) %>% - select(starts_with("eval_"), epoch) %>% - arrange(epoch) %>% - distinct(epoch, .keep_all = TRUE) - }) + default_metric <- "eval_loss" + if (!"eval_loss" %in% metric_names && length(metric_names) > 0) { + default_metric <- metric_names[1] + } - output$metric_selector_ui <- renderUI({ - df <- tryCatch(metrics_for_plotting(), error = function(e) NULL) - - if (is.null(df) || nrow(df) == 0) { - return(p("Waiting for first evaluation epoch to complete...")) - } - - metric_names <- names(df)[sapply(df, is.numeric) & !names(df) %in% c("epoch", "step", "runtime", "samples_per_second", "steps_per_second")] - - default_metric <- "eval_loss" - if (!"eval_loss" %in% metric_names && length(metric_names) > 0) { - default_metric <- metric_names[1] - } - - selected_val <- input$selected_metric - if (is.null(selected_val) || !selected_val %in% metric_names) { - selected_val <- default_metric - } - - selectInput("selected_metric", "Select Metric to Plot:", - choices = metric_names, - selected = selected_val) - }) + selected_val <- input$selected_metric + if (is.null(selected_val) || !selected_val %in% metric_names) { + selected_val <- default_metric + } - output$dynamic_metric_plot <- renderDygraph({ - df <- tryCatch(metrics_for_plotting(), error = function(e) NULL) - - if (is.null(df) || nrow(df) == 0) { - return(dygraph(data.frame(x=c(0), y=c(0)), main = "Waiting for Epoch 1") %>% + selectInput("selected_metric", "Select Metric to Plot:", + choices = metric_names, + selected = selected_val) + }) + + output$dynamic_metric_plot <- renderDygraph({ + df <- tryCatch(metrics_for_plotting(), error = function(e) NULL) + + if (is.null(df) || nrow(df) == 0) { + return(dygraph(data.frame(x=c(0), y=c(0)), main = "Waiting for Epoch 1") %>% dyOptions(drawGrid = FALSE, drawAxes = FALSE, drawYAxis = FALSE, drawXAxis = FALSE) %>% dyAxis("x", label = "Epoch")) - } - - req(input$selected_metric) - req(input$selected_metric %in% names(df)) - - metric_to_plot <- input$selected_metric - - if (!"epoch" %in% names(df) || !metric_to_plot %in% names(df)) { - return(dygraph(data.frame(x=c(0), y=c(0)), main = "Metric data not yet available") %>% + } + + req(input$selected_metric) + req(input$selected_metric %in% names(df)) + + metric_to_plot <- input$selected_metric + + if (!"epoch" %in% names(df) || !metric_to_plot %in% names(df)) { + return(dygraph(data.frame(x=c(0), y=c(0)), main = "Metric data not yet available") %>% dyOptions(drawGrid = FALSE, drawAxes = FALSE, drawYAxis = FALSE, drawXAxis = FALSE)) - } - - plot_data <- df[, c("epoch", metric_to_plot)] - - dygraph(plot_data, main = paste(metric_to_plot, "vs. Epoch")) %>% - dySeries(metric_to_plot, label = metric_to_plot) %>% - dyAxis("x", label = "Epoch", valueRange = c(0, max(df$epoch, na.rm = TRUE) + 1)) %>% - dyRangeSelector() %>% - dyOptions(stackedGraph = FALSE, - fillGraph = FALSE, - stepPlot = FALSE, - drawPoints = TRUE, - pointSize = 4) %>% - dyLegend(show = "always", width = 200) - }) + } + plot_data <- df[, c("epoch", metric_to_plot)] - # ============================================================================== - # == 6. "TRAINING HISTORY" TAB LOGIC - # ============================================================================== - - # --- 6.1: Populate the Job Selector Dropdown (with Filters) --- - observe({ - # React to tab switching, job completion, and filter changes - input$main_tabs - polled_data() - input$history_task_filter - input$history_status_filter - - tryCatch({ - - query_params <- list() - if (input$history_task_filter != "all") { - query_params$task_type <- input$history_task_filter - } - if (input$history_status_filter != "all") { - query_params$status <- input$history_status_filter - } - - req <- request(paste0(api_url, "/jobs/list")) - if (length(query_params) > 0) { - req <- req_url_query(req, !!!query_params) - } - - resp <- req_perform(req) - jobs_list_raw <- resp_body_json(resp, simplifyVector = FALSE) - - if (length(jobs_list_raw) > 0) { - - jobs_df <- bind_rows(lapply(jobs_list_raw, function(job) { - data.frame( - id = job$id, - task_type = job$task_type, - status = job$status, - run_name = ifelse(is.null(job$details$run_name), "N/A", job$details$run_name) - ) - })) - - history_jobs_df(jobs_df) - - job_names <- paste0( - jobs_df$run_name, - " (", jobs_df$task_type, " | ID: ", substr(jobs_df$id, 1, 8), ") - ", - jobs_df$status - ) - job_choices <- setNames(jobs_df$id, job_names) - - updateSelectInput(session, "history_job_selector", choices = job_choices) - - } else { - updateSelectInput(session, "history_job_selector", choices = c("No jobs found" = "")) - history_jobs_df(NULL) - } - }, error = function(e) { - updateSelectInput(session, "history_job_selector", choices = c("Error loading jobs" = "")) - history_jobs_df(NULL) - }) - }) - - # --- 6.2: Fetch Metrics when user selects a historical job --- - observeEvent(input$history_job_selector, { - job_id <- input$history_job_selector - - history_poller_active(FALSE) # Deactivate poller by default - - if (!is.null(job_id) && nchar(job_id) > 0) { - tryCatch({ - req <- request(paste0(api_url, "/metrics/", job_id)) - resp <- req_perform(req) - history_metrics(resp_body_json(resp)) - }, error = function(e) { - history_metrics(NULL) - }) - - # --- Poller Activation Logic --- - req(history_jobs_df()) - job_info <- history_jobs_df() %>% filter(id == job_id) - if (nrow(job_info) > 0 && job_info$status == "running") { - print(paste("Activating poller for running job:", job_id)) - history_poller_active(TRUE) # Activate poller - } - # --- End Poller Logic --- - - } else { - # If job_id is "" or NULL (e.g., "No jobs found"), clear the metrics. - history_metrics(NULL) - } - }) - - # --- 6.3: Reactive for Historical Plot Data --- - history_metrics_for_plotting <- reactive({ - metrics_list <- history_metrics() - req(metrics_list, length(metrics_list) > 0) - - bind_rows(metrics_list) %>% - filter(!is.na(epoch)) %>% # <--- THIS IS THE FIX - filter(if_any(everything(), ~ !is.na(.))) %>% - select(starts_with("eval_"), epoch) %>% - arrange(epoch) %>% - distinct(epoch, .keep_all = TRUE) - }) - - # --- 6.4: Render Historical Metrics Table --- - output$history_eval_table <- renderDT({ - metrics_list <- history_metrics() - req(metrics_list, length(metrics_list) > 0) - - metrics_df <- bind_rows(metrics_list) - - if (nrow(metrics_df) > 0) { - display_df <- metrics_df %>% - pivot_longer( - cols = starts_with("eval_") | starts_with("test_"), - names_to = "metric_name", - values_to = "value", - values_drop_na = TRUE - ) %>% - separate(metric_name, into = c("step", "metric"), sep = "_", extra = "merge") %>% - pivot_wider( - names_from = metric, - values_from = value, - values_fn = first - ) %>% - select(any_of(c("step", "epoch", "loss", "map", "wer", "cer", "mean_iou")), everything()) - - datatable( - display_df, - options = list(pageLength = 5, scrollX = TRUE, searching = FALSE, autoWidth = TRUE), - rownames = FALSE - ) - } else { - return(NULL) - } - }) - - # --- 6.5: Render Historical Plot UI (Dropdown) --- - output$history_metric_selector_ui <- renderUI({ - df <- tryCatch(history_metrics_for_plotting(), error = function(e) NULL) - - if (is.null(df) || nrow(df) == 0) { - return(p("No evaluation metrics found for this job.")) - } - - metric_names <- names(df)[sapply(df, is.numeric) & !names(df) %in% c("epoch", "step", "runtime", "samples_per_second", "steps_per_second")] - - default_metric <- "eval_loss" - if (!"eval_loss" %in% metric_names && length(metric_names) > 0) { - default_metric <- metric_names[1] - } - - selected_val <- input$history_selected_metric - if (is.null(selected_val) || !selected_val %in% metric_names) { - selected_val <- default_metric - } - - selectInput("history_selected_metric", "Select Metric to Plot:", - choices = metric_names, - selected = selected_val) - }) - - # --- 6.6: Render Historical Plot --- - output$history_metric_plot <- renderDygraph({ - df <- tryCatch(history_metrics_for_plotting(), error = function(e) NULL) - - if (is.null(df) || nrow(df) == 0) { - return(dygraph(data.frame(x=c(0), y=c(0)), main = "No Metric Data") %>% - dyOptions(drawGrid = FALSE, drawAxes = FALSE, drawYAxis = FALSE, drawXAxis = FALSE) %>% - dyAxis("x", label = "Epoch")) - } - - req(input$history_selected_metric) - req(input$history_selected_metric %in% names(df)) + dygraph(plot_data, main = paste(metric_to_plot, "vs. Epoch")) %>% + dySeries(metric_to_plot, label = metric_to_plot) %>% + dyAxis("x", label = "Epoch", valueRange = c(0, max(df$epoch, na.rm = TRUE) + 1)) %>% + dyRangeSelector() %>% + dyOptions(stackedGraph = FALSE, + fillGraph = FALSE, + stepPlot = FALSE, + drawPoints = TRUE, + pointSize = 4) %>% + dyLegend(show = "always", width = 200) + }) + + + # ============================================================================== + # == 6. "TRAINING HISTORY" TAB LOGIC + # ============================================================================== + + # --- 6.1: Populate the Job Selector Dropdown (with Filters) --- + observe({ + # React to tab switching, job completion, and filter changes + input$main_tabs + polled_data() + input$history_task_filter + input$history_status_filter + + tryCatch({ + + query_params <- list() + if (input$history_task_filter != "all") { + query_params$task_type <- input$history_task_filter + } + if (input$history_status_filter != "all") { + query_params$status <- input$history_status_filter + } + + req <- request(paste0(api_url, "/jobs/list")) + if (length(query_params) > 0) { + req <- req_url_query(req, !!!query_params) + } + + resp <- req_perform(req) + jobs_list_raw <- resp_body_json(resp, simplifyVector = FALSE) + + if (length(jobs_list_raw) > 0) { - metric_to_plot <- input$history_selected_metric + jobs_df <- bind_rows(lapply(jobs_list_raw, function(job) { + data.frame( + id = job$id, + task_type = job$task_type, + status = job$status, + run_name = ifelse(is.null(job$details$run_name), "N/A", job$details$run_name) + ) + })) - if (!"epoch" %in% names(df) || !metric_to_plot %in% names(df)) { - return(dygraph(data.frame(x=c(0), y=c(0)), main = "Metric data not yet available") %>% - dyOptions(drawGrid = FALSE, drawAxes = FALSE, drawYAxis = FALSE, drawXAxis = FALSE)) - } - - plot_data <- df[, c("epoch", metric_to_plot)] + history_jobs_df(jobs_df) - dygraph(plot_data, main = paste(metric_to_plot, "vs. Epoch")) %>% - dySeries(metric_to_plot, label = metric_to_plot) %>% - dyAxis("x", label = "Epoch", valueRange = c(0, max(df$epoch, na.rm = TRUE) + 1)) %>% - dyRangeSelector() %>% - dyOptions(stackedGraph = FALSE, - fillGraph = FALSE, - stepPlot = FALSE, - drawPoints = TRUE, - pointSize = 4) %>% - dyLegend(show = "always", width = 200) - }) - - # --- 6.7: Poller for selected running job in History tab --- - observe({ - # Only run if: - req( - history_poller_active() == TRUE, - input$main_tabs == "Training History", - !is.null(input$history_job_selector), - nchar(input$history_job_selector) > 0 + job_names <- paste0( + jobs_df$run_name, + " (", jobs_df$task_type, " | ID: ", substr(jobs_df$id, 1, 8), ") - ", + jobs_df$status ) + job_choices <- setNames(jobs_df$id, job_names) - # Poll every 3 seconds - invalidateLater(3000, session) - - job_id <- input$history_job_selector - print(paste("History Poller: Fetching metrics for", job_id)) + updateSelectInput(session, "history_job_selector", choices = job_choices) - tryCatch({ - req <- request(paste0(api_url, "/metrics/", job_id)) - resp <- req_perform(req) - if (resp_status(resp) == 200) { - history_metrics(resp_body_json(resp)) - } - - # Check if job is still running - req_status <- request(paste0(api_url, "/status/", job_id)) - resp_status <- req_perform(req_status) - if (resp_status(resp_status) == 200) { - status_data <- resp_body_json(resp_status) - if (status_data$status != "running") { - print(paste("History Poller: Job", job_id, "is no longer running. Deactivating poller.")) - history_poller_active(FALSE) - # Refresh the job list dropdown to show "completed" - observeEvent(model_registry(), { - req(model_registry()) - refresh_data_trigger(refresh_data_trigger() + 1) - }, once = TRUE) - } - } - - }, error = function(e) { - print(paste("History Poller Error:", e$message)) - history_poller_active(FALSE) # Stop polling on error - }) + } else { + updateSelectInput(session, "history_job_selector", choices = c("No jobs found" = "")) + history_jobs_df(NULL) + } + }, error = function(e) { + updateSelectInput(session, "history_job_selector", choices = c("Error loading jobs" = "")) + history_jobs_df(NULL) }) + }) + + # --- 6.2: Fetch Metrics when user selects a historical job --- + observeEvent(input$history_job_selector, { + job_id <- input$history_job_selector + history_poller_active(FALSE) # Deactivate poller by default - # ============================================================================== - # == 7. "INFERENCE" TAB LOGIC - # ============================================================================== + if (!is.null(job_id) && nchar(job_id) > 0) { + tryCatch({ + req <- request(paste0(api_url, "/metrics/", job_id)) + resp <- req_perform(req) + history_metrics(resp_body_json(resp)) + }, error = function(e) { + history_metrics(NULL) + }) + + # --- Poller Activation Logic --- + req(history_jobs_df()) + job_info <- history_jobs_df() %>% filter(id == job_id) + if (nrow(job_info) > 0 && job_info$status == "running") { + print(paste("Activating poller for running job:", job_id)) + history_poller_active(TRUE) # Activate poller + } + # --- End Poller Logic --- + + } else { + # If job_id is "" or NULL (e.g., "No jobs found"), clear the metrics. + history_metrics(NULL) + } + }) + + # --- 6.3: Reactive for Historical Plot Data --- + history_metrics_for_plotting <- reactive({ + metrics_list <- history_metrics() + req(metrics_list, length(metrics_list) > 0) - # --- 7.1: Inference Checkpoint Finders --- - observeEvent(input$infer_run_name, { - run_name <- input$infer_run_name - if (nchar(run_name) > 2) { - tryCatch({ - req <- request(paste0(api_url, "/checkpoints")) %>% - req_url_query(run_name = run_name, task_type = "object_detection") - resp <- req_perform(req) - if (resp_status(resp) == 200) { - checkpoints <- resp_body_json(resp, simplifyVector = TRUE) - updateSelectInput(session, "infer_checkpoint_dropdown", choices = checkpoints) - } - }, error = function(e) { - updateSelectInput(session, "infer_checkpoint_dropdown", choices = c("Error finding checkpoints")) - }) - } - }) + bind_rows(metrics_list) %>% + filter(!is.na(epoch)) %>% # <--- THIS IS THE FIX + filter(if_any(everything(), ~ !is.na(.))) %>% + select(starts_with("eval_"), epoch) %>% + arrange(epoch) %>% + distinct(epoch, .keep_all = TRUE) + }) + + # --- 6.4: Render Historical Metrics Table --- + output$history_eval_table <- renderDT({ + metrics_list <- history_metrics() + req(metrics_list, length(metrics_list) > 0) - observeEvent(input$infer_asr_run_name, { - run_name <- input$infer_asr_run_name - if (nchar(run_name) > 2) { - tryCatch({ - req <- request(paste0(api_url, "/checkpoints")) %>% - req_url_query(run_name = run_name, task_type = "asr") - resp <- req_perform(req) - if (resp_status(resp) == 200) { - checkpoints <- resp_body_json(resp, simplifyVector = TRUE) - updateSelectInput(session, "infer_asr_checkpoint_dropdown", choices = checkpoints) - } - }, error = function(e) { - updateSelectInput(session, "infer_asr_checkpoint_dropdown", choices = c("Error finding checkpoints")) - }) - } - }) - - observeEvent(input$infer_img_class_run_name, { - run_name <- input$infer_img_class_run_name - if (nchar(run_name) > 2) { - tryCatch({ - req <- request(paste0(api_url, "/checkpoints")) %>% - req_url_query(run_name = run_name, task_type = "image_classification") - checkpoints <- resp_body_json(req_perform(req), simplifyVector = TRUE) - updateSelectInput(session, "infer_img_class_checkpoint_dropdown", choices = checkpoints) - }, error = function(e) { }) - } - }) + metrics_df <- bind_rows(metrics_list) - observeEvent(input$infer_seg_run_name, { - run_name <- input$infer_seg_run_name - if (nchar(run_name) > 2) { - tryCatch({ - req <- request(paste0(api_url, "/checkpoints")) %>% - req_url_query(run_name = run_name, task_type = "image_segmentation") - checkpoints <- resp_body_json(req_perform(req), simplifyVector = TRUE) - updateSelectInput(session, "infer_seg_checkpoint_dropdown", choices = checkpoints) - }, error = function(e) { }) - } - }) - - # --- 7.2: Inference Job Submission (One per task) --- + if (nrow(metrics_df) > 0) { + display_df <- metrics_df %>% + pivot_longer( + cols = starts_with("eval_") | starts_with("test_"), + names_to = "metric_name", + values_to = "value", + values_drop_na = TRUE + ) %>% + separate(metric_name, into = c("step", "metric"), sep = "_", extra = "merge") %>% + pivot_wider( + names_from = metric, + values_from = value, + values_fn = first + ) %>% + select(any_of(c("step", "epoch", "loss", "map", "wer", "cer", "mean_iou")), everything()) + + datatable( + display_df, + options = list(pageLength = 5, scrollX = TRUE, searching = FALSE, autoWidth = TRUE), + rownames = FALSE + ) + } else { + return(NULL) + } + }) + + # --- 6.5: Render Historical Plot UI (Dropdown) --- + output$history_metric_selector_ui <- renderUI({ + df <- tryCatch(history_metrics_for_plotting(), error = function(e) NULL) - observeEvent(input$start_obj_inference, { - req(input$infer_obj_image_upload); req(input$infer_checkpoint_dropdown) - obj_inference_result(list(status = "Running...", image_url = NULL, error = NULL)) - tryCatch({ - req <- request(paste0(api_url, "/inference/object-detection")) %>% - req_body_multipart( - image = curl::form_file(input$infer_obj_image_upload$datapath), - model_checkpoint = input$infer_checkpoint_dropdown, - threshold = as.character(input$infer_obj_threshold), - iou = as.character(input$infer_obj_iou), - max_det = as.character(input$infer_obj_max_det) - ) - resp <- req_perform(req) - resp_data <- resp_body_json(resp) - obj_inference_result(list(status = "Success", image_url = resp_data$output_url, error = NULL)) - }, error = function(e) { - error_message <- as.character(e$message) - if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } - obj_inference_result(list(status = "Error", image_url = NULL, error = error_message)) - }) - }) - - observeEvent(input$start_asr_inference, { - req(input$infer_asr_audio_upload) - req(input$infer_asr_checkpoint_dropdown) - asr_inference_result(list(status = "Running...", transcription = "Processing...", error = NULL)) - tryCatch({ - req <- request(paste0(api_url, "/inference/asr")) %>% - req_body_multipart( - audio = curl::form_file(input$infer_asr_audio_upload$datapath), - model_checkpoint = input$infer_asr_checkpoint_dropdown - ) - resp <- req_perform(req) - resp_data <- resp_body_json(resp) - asr_inference_result(list(status = "Success", transcription = resp_data$transcription, error = NULL)) - }, error = function(e) { - error_message <- as.character(e$message) - if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } - asr_inference_result(list(status = "Error", transcription = NULL, error = error_message)) - }) - }) + if (is.null(df) || nrow(df) == 0) { + return(p("No evaluation metrics found for this job.")) + } - observeEvent(input$start_img_class_inference, { - req(input$infer_img_class_upload, input$infer_img_class_checkpoint_dropdown) - img_class_inference_result(list(status = "Running...", prediction = "Processing...", error = NULL)) - tryCatch({ - req <- request(paste0(api_url, "/inference/image-classification")) %>% - req_body_multipart( - image = curl::form_file(input$infer_img_class_upload$datapath), - model_checkpoint = input$infer_img_class_checkpoint_dropdown - ) - resp_data <- resp_body_json(req_perform(req)) - img_class_inference_result(list(status = "Success", prediction = resp_data$prediction, error = NULL)) - }, error = function(e) { - img_class_inference_result(list(status = "Error", prediction = NULL, error = as.character(e))) - }) - }) + metric_names <- names(df)[sapply(df, is.numeric) & !names(df) %in% c("epoch", "step", "runtime", "samples_per_second", "steps_per_second")] - observeEvent(input$start_seg_inference, { - req(input$infer_seg_image_upload); req(input$infer_seg_checkpoint_dropdown) - seg_inference_result(list(status = "Running...", image_url = NULL, error = NULL)) - tryCatch({ - req <- request(paste0(api_url, "/inference/image-segmentation")) %>% - req_body_multipart( - image = curl::form_file(input$infer_seg_image_upload$datapath), - model_checkpoint = input$infer_seg_checkpoint_dropdown - ) - resp <- req_perform(req) - resp_data <- resp_body_json(resp) - seg_inference_result(list(status = "Success", image_url = resp_data$output_url, error = NULL)) - }, error = function(e) { - error_message <- as.character(e$message) - if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } - seg_inference_result(list(status = "Error", image_url = NULL, error = error_message)) - }) - }) + default_metric <- "eval_loss" + if (!"eval_loss" %in% metric_names && length(metric_names) > 0) { + default_metric <- metric_names[1] + } + + selected_val <- input$history_selected_metric + if (is.null(selected_val) || !selected_val %in% metric_names) { + selected_val <- default_metric + } + + selectInput("history_selected_metric", "Select Metric to Plot:", + choices = metric_names, + selected = selected_val) + }) + + # --- 6.6: Render Historical Plot --- + output$history_metric_plot <- renderDygraph({ + df <- tryCatch(history_metrics_for_plotting(), error = function(e) NULL) + + if (is.null(df) || nrow(df) == 0) { + return(dygraph(data.frame(x=c(0), y=c(0)), main = "No Metric Data") %>% + dyOptions(drawGrid = FALSE, drawAxes = FALSE, drawYAxis = FALSE, drawXAxis = FALSE) %>% + dyAxis("x", label = "Epoch")) + } - # --- 7.3: Inference UI Outputs --- + req(input$history_selected_metric) + req(input$history_selected_metric %in% names(df)) - output$inference_status_ui <- renderUI({ - res <- obj_inference_result() - if (res$status == "Running...") { - tags$div(class = "alert alert-info", "Running inference...") - } else if (res$status == "Error") { - tags$div(class = "alert alert-danger", HTML(paste("Error:", res$error))) + metric_to_plot <- input$history_selected_metric + + if (!"epoch" %in% names(df) || !metric_to_plot %in% names(df)) { + return(dygraph(data.frame(x=c(0), y=c(0)), main = "Metric data not yet available") %>% + dyOptions(drawGrid = FALSE, drawAxes = FALSE, drawYAxis = FALSE, drawXAxis = FALSE)) + } + + plot_data <- df[, c("epoch", metric_to_plot)] + + dygraph(plot_data, main = paste(metric_to_plot, "vs. Epoch")) %>% + dySeries(metric_to_plot, label = metric_to_plot) %>% + dyAxis("x", label = "Epoch", valueRange = c(0, max(df$epoch, na.rm = TRUE) + 1)) %>% + dyRangeSelector() %>% + dyOptions(stackedGraph = FALSE, + fillGraph = FALSE, + stepPlot = FALSE, + drawPoints = TRUE, + pointSize = 4) %>% + dyLegend(show = "always", width = 200) + }) + + # --- 6.7: Poller for selected running job in History tab --- + observe({ + # Only run if: + req( + history_poller_active() == TRUE, + input$main_tabs == "Training History", + !is.null(input$history_job_selector), + nchar(input$history_job_selector) > 0 + ) + + # Poll every 3 seconds + invalidateLater(3000, session) + + job_id <- input$history_job_selector + print(paste("History Poller: Fetching metrics for", job_id)) + + tryCatch({ + req <- request(paste0(api_url, "/metrics/", job_id)) + resp <- req_perform(req) + if (resp_status(resp) == 200) { + history_metrics(resp_body_json(resp)) + } + + # Check if job is still running + req_status <- request(paste0(api_url, "/status/", job_id)) + resp_status <- req_perform(req_status) + if (resp_status(resp_status) == 200) { + status_data <- resp_body_json(resp_status) + if (status_data$status != "running") { + print(paste("History Poller: Job", job_id, "is no longer running. Deactivating poller.")) + history_poller_active(FALSE) + # Refresh the job list dropdown to show "completed" + observeEvent(model_registry(), { + req(model_registry()) + refresh_data_trigger(refresh_data_trigger() + 1) + }, once = TRUE) } + } + + }, error = function(e) { + print(paste("History Poller Error:", e$message)) + history_poller_active(FALSE) # Stop polling on error }) - output$inference_image_output <- renderImage({ - res <- obj_inference_result() - req(res$status == "Success", res$image_url) - image_url <- paste0(api_url, res$image_url) - temp_file <- tempfile(fileext = ".jpg") - download.file(image_url, temp_file, mode = "wb") - list(src = temp_file, contentType = 'image/jpeg', alt = "Inference Result") - }, deleteFile = TRUE) - - output$asr_inference_status_ui <- renderUI({ - res <- asr_inference_result() - if (res$status == "Running...") { - tags$div(class = "alert alert-info", "Running inference...") - } else if (res$status == "Error") { - tags$div(class = "alert alert-danger", HTML(paste("Error:", res$error))) + }) + + + # ============================================================================== + # == 7. "INFERENCE" TAB LOGIC + # ============================================================================== + + # --- 7.1: Inference Checkpoint Finders --- + observeEvent(input$infer_run_name, { + run_name <- input$infer_run_name + if (nchar(run_name) > 2) { + tryCatch({ + req <- request(paste0(api_url, "/checkpoints")) %>% + req_url_query(run_name = run_name, task_type = "object_detection") + resp <- req_perform(req) + if (resp_status(resp) == 200) { + checkpoints <- resp_body_json(resp, simplifyVector = TRUE) + updateSelectInput(session, "infer_checkpoint_dropdown", choices = checkpoints) } - }) - output$asr_transcription_output <- renderText({ - res <- asr_inference_result() - if (is.null(res$transcription)) { - "Upload an audio file and click 'Run Inference' to see the transcription here." - } else { - res$transcription + }, error = function(e) { + updateSelectInput(session, "infer_checkpoint_dropdown", choices = c("Error finding checkpoints")) + }) + } + }) + + observeEvent(input$infer_asr_run_name, { + run_name <- input$infer_asr_run_name + if (nchar(run_name) > 2) { + tryCatch({ + req <- request(paste0(api_url, "/checkpoints")) %>% + req_url_query(run_name = run_name, task_type = "asr") + resp <- req_perform(req) + if (resp_status(resp) == 200) { + checkpoints <- resp_body_json(resp, simplifyVector = TRUE) + updateSelectInput(session, "infer_asr_checkpoint_dropdown", choices = checkpoints) } + }, error = function(e) { + updateSelectInput(session, "infer_asr_checkpoint_dropdown", choices = c("Error finding checkpoints")) + }) + } + }) + + observeEvent(input$infer_img_class_run_name, { + run_name <- input$infer_img_class_run_name + if (nchar(run_name) > 2) { + tryCatch({ + req <- request(paste0(api_url, "/checkpoints")) %>% + req_url_query(run_name = run_name, task_type = "image_classification") + checkpoints <- resp_body_json(req_perform(req), simplifyVector = TRUE) + updateSelectInput(session, "infer_img_class_checkpoint_dropdown", choices = checkpoints) + }, error = function(e) { }) + } + }) + + observeEvent(input$infer_seg_run_name, { + run_name <- input$infer_seg_run_name + if (nchar(run_name) > 2) { + tryCatch({ + req <- request(paste0(api_url, "/checkpoints")) %>% + req_url_query(run_name = run_name, task_type = "image_segmentation") + checkpoints <- resp_body_json(req_perform(req), simplifyVector = TRUE) + updateSelectInput(session, "infer_seg_checkpoint_dropdown", choices = checkpoints) + }, error = function(e) { }) + } + }) + + # --- 7.2: Inference Job Submission (One per task) --- + + observeEvent(input$start_obj_inference, { + req(input$infer_obj_image_upload); req(input$infer_checkpoint_dropdown) + obj_inference_result(list(status = "Running...", image_url = NULL, error = NULL)) + tryCatch({ + req <- request(paste0(api_url, "/inference/object-detection")) %>% + req_body_multipart( + image = curl::form_file(input$infer_obj_image_upload$datapath), + model_checkpoint = input$infer_checkpoint_dropdown, + threshold = as.character(input$infer_obj_threshold), + iou = as.character(input$infer_obj_iou), + max_det = as.character(input$infer_obj_max_det) + ) + resp <- req_perform(req) + resp_data <- resp_body_json(resp) + obj_inference_result(list(status = "Success", image_url = resp_data$output_url, error = NULL)) + }, error = function(e) { + error_message <- as.character(e$message) + if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } + obj_inference_result(list(status = "Error", image_url = NULL, error = error_message)) }) - - output$img_class_inference_status_ui <- renderUI({ - res <- img_class_inference_result() - if (res$status == "Running...") tags$div(class = "alert alert-info", "Running inference...") - else if (res$status == "Error") tags$div(class = "alert alert-danger", HTML(paste("Error:", res$error))) + }) + + observeEvent(input$start_asr_inference, { + req(input$infer_asr_audio_upload) + req(input$infer_asr_checkpoint_dropdown) + asr_inference_result(list(status = "Running...", transcription = "Processing...", error = NULL)) + tryCatch({ + req <- request(paste0(api_url, "/inference/asr")) %>% + req_body_multipart( + audio = curl::form_file(input$infer_asr_audio_upload$datapath), + model_checkpoint = input$infer_asr_checkpoint_dropdown + ) + resp <- req_perform(req) + resp_data <- resp_body_json(resp) + asr_inference_result(list(status = "Success", transcription = resp_data$transcription, error = NULL)) + }, error = function(e) { + error_message <- as.character(e$message) + if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } + asr_inference_result(list(status = "Error", transcription = NULL, error = error_message)) }) - output$img_class_prediction_output <- renderText({ - img_class_inference_result()$prediction + }) + + observeEvent(input$start_img_class_inference, { + req(input$infer_img_class_upload, input$infer_img_class_checkpoint_dropdown) + img_class_inference_result(list(status = "Running...", prediction = "Processing...", error = NULL)) + tryCatch({ + req <- request(paste0(api_url, "/inference/image-classification")) %>% + req_body_multipart( + image = curl::form_file(input$infer_img_class_upload$datapath), + model_checkpoint = input$infer_img_class_checkpoint_dropdown + ) + resp_data <- resp_body_json(req_perform(req)) + img_class_inference_result(list(status = "Success", prediction = resp_data$prediction, error = NULL)) + }, error = function(e) { + img_class_inference_result(list(status = "Error", prediction = NULL, error = as.character(e))) }) - - output$seg_inference_status_ui <- renderUI({ - res <- seg_inference_result() - if (res$status == "Running...") { - tags$div(class = "alert alert-info", "Running inference...") - } else if (res$status == "Error") { - tags$div(class = "alert alert-danger", HTML(paste("Error:", res$error))) - } + }) + + observeEvent(input$start_seg_inference, { + req(input$infer_seg_image_upload); req(input$infer_seg_checkpoint_dropdown) + seg_inference_result(list(status = "Running...", image_url = NULL, error = NULL)) + tryCatch({ + req <- request(paste0(api_url, "/inference/image-segmentation")) %>% + req_body_multipart( + image = curl::form_file(input$infer_seg_image_upload$datapath), + model_checkpoint = input$infer_seg_checkpoint_dropdown + ) + resp <- req_perform(req) + resp_data <- resp_body_json(resp) + seg_inference_result(list(status = "Success", image_url = resp_data$output_url, error = NULL)) + }, error = function(e) { + error_message <- as.character(e$message) + if(!is.null(e$body)) { error_message <- paste("API Error:", e$body) } + seg_inference_result(list(status = "Error", image_url = NULL, error = error_message)) }) - output$seg_inference_image_output <- renderImage({ - res <- seg_inference_result() - req(res$status == "Success", res$image_url) - image_url <- paste0(api_url, res$image_url) - temp_file <- tempfile(fileext = ".jpg") - download.file(image_url, temp_file, mode = "wb") - list(src = temp_file, contentType = 'image/jpeg', alt = "Inference Result") - }, deleteFile = TRUE) - + }) + + # --- 7.3: Inference UI Outputs --- + + output$inference_status_ui <- renderUI({ + res <- obj_inference_result() + if (res$status == "Running...") { + tags$div(class = "alert alert-info", "Running inference...") + } else if (res$status == "Error") { + tags$div(class = "alert alert-danger", HTML(paste("Error:", res$error))) + } + }) + output$inference_image_output <- renderImage({ + res <- obj_inference_result() + req(res$status == "Success", res$image_url) + image_url <- paste0(api_url, res$image_url) + temp_file <- tempfile(fileext = ".jpg") + download.file(image_url, temp_file, mode = "wb") + list(src = temp_file, contentType = 'image/jpeg', alt = "Inference Result") + }, deleteFile = TRUE) + + output$asr_inference_status_ui <- renderUI({ + res <- asr_inference_result() + if (res$status == "Running...") { + tags$div(class = "alert alert-info", "Running inference...") + } else if (res$status == "Error") { + tags$div(class = "alert alert-danger", HTML(paste("Error:", res$error))) + } + }) + output$asr_transcription_output <- renderText({ + res <- asr_inference_result() + if (is.null(res$transcription)) { + "Upload an audio file and click 'Run Inference' to see the transcription here." + } else { + res$transcription + } + }) + + output$img_class_inference_status_ui <- renderUI({ + res <- img_class_inference_result() + if (res$status == "Running...") tags$div(class = "alert alert-info", "Running inference...") + else if (res$status == "Error") tags$div(class = "alert alert-danger", HTML(paste("Error:", res$error))) + }) + output$img_class_prediction_output <- renderText({ + img_class_inference_result()$prediction + }) + + output$seg_inference_status_ui <- renderUI({ + res <- seg_inference_result() + if (res$status == "Running...") { + tags$div(class = "alert alert-info", "Running inference...") + } else if (res$status == "Error") { + tags$div(class = "alert alert-danger", HTML(paste("Error:", res$error))) + } + }) + output$seg_inference_image_output <- renderImage({ + res <- seg_inference_result() + req(res$status == "Success", res$image_url) + image_url <- paste0(api_url, res$image_url) + temp_file <- tempfile(fileext = ".jpg") + download.file(image_url, temp_file, mode = "wb") + list(src = temp_file, contentType = 'image/jpeg', alt = "Inference Result") + }, deleteFile = TRUE) + } diff --git a/server/header_footer_configs.R b/server/header_footer_configs.R index 9bc42a7..73b1ef9 100644 --- a/server/header_footer_configs.R +++ b/server/header_footer_configs.R @@ -1,25 +1,24 @@ app_title = function() { - output$app_title = renderUI({ - h3(get_rv_labels("app_title"), style = "text-align: center;") - }) + output$app_title = renderUI({ + h3(get_rv_labels("app_title"), style = "text-align: center;") + }) } #Footer Language convertion footer_language_translation = function() { output$app_footer_title = renderUI({ - # h4(get_rv_labels("footer_org_name")) - h4(get_rv_labels("app_version")) + h4(get_rv_labels("footer_org_name")) }) - + output$app_footer_contact = renderUI({ h4(HTML(paste0('',get_rv_labels("footer_contact"), ': example@aphrc.org'))) }) - + output$app_footer_all_rights = renderUI({ h4(get_rv_labels("footer_org_name")) }) - + output$app_footer_all_rights = renderUI({ h4(paste0(get_rv_labels("footer_copyright"), " © ", format(Sys.Date(), "%Y"), ", ", get_rv_labels("footer_all_rights")), style = "text-align: right;") }) @@ -31,85 +30,100 @@ footer_language_translation = function() { menu_translation = function(){ output$dynamic_meinu_aphrc <- renderMenu({ sidebarMenu(id = "tabs", - menuItem(text = HTML(paste0("", get_rv_labels("menu_home"), "")), tabName = "homePage", icon = icon("house"), selected = TRUE), - menuItem(text = HTML(paste0("", get_rv_labels("menu_source_data"), "")), tabName = "sourcedata", icon = icon("file-import", lib = "font-awesome")), - menuItem( - text = HTML(paste0("", get_rv_labels("menu_manage_data"), "")), tabName = "manageData", icon = icon("glyphicon glyphicon-tasks", lib = "glyphicon"), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_overview"), "")), tabName = "Overview", icon = icon("table-columns", lib = "font-awesome")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_explore"), "")), tabName = "Explore", icon = icon("object-ungroup", lib = "font-awesome")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_transform"), "")), tabName = "Transform", icon = icon("table-columns", lib = "font-awesome")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_combine_data"), "")), tabName = "combineData", icon = icon("table-columns", lib = "font-awesome")) - ), - menuItem(text = HTML(paste0("", get_rv_labels("menu_visualize_data"), "")), tabName = "visualizeData", icon = icon("glyphicon glyphicon-stats", lib = "glyphicon"), - menuItem(text = HTML(paste0("", get_rv_labels("menu_summarizeAutomatic"), "")), tabName = "summarizeAutomatic", icon = icon("glyphicon glyphicon-stats", lib = "glyphicon") - - ), - menuItem(text = HTML(paste0("", get_rv_labels("menu_summarizeCustom"), "")), tabName = "summarizeCustom", icon = icon("chart-line"))), - - - menuItem(text = HTML(paste0("", get_rv_labels("menu_research_question"), "")), tabName = "researchQuestions", icon = icon("file-import", lib = "font-awesome"), selected = FALSE), - - - menuItem( - text = HTML(paste0("", get_rv_labels("menu_machine_learning"), "")), tabName = "machineLearning", icon = icon("code-merge", lib = "font-awesome"), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_setup_models"), "")), tabName = "setupModels", icon = icon("arrows-split-up-and-left", lib = "font-awesome")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_feature_engineering"), "")), tabName = "featureEngineering", icon = icon("sitemap", lib = "font-awesome")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_train_model"), "")), tabName = "trainModel", icon = icon("gear", lib = "font-awesome")), - menuSubItem(text =HTML(paste0("", get_rv_labels("menu_validate_model"), "")), tabName = "validateDeployModel", icon = icon("server", lib = "font-awesome")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_predict"), "")), tabName = "predictClassify", icon = icon("layer-group", lib = "font-awesome")) - ), - menuItem(text = HTML(paste0("", get_rv_labels("menu_omop_data"), "")), tabName = "omopAnalysis", icon = icon("magnifying-glass-chart", lib = "font-awesome"), - menuSubItem(text =HTML(paste0("", get_rv_labels("evidenceQuality"), "")), tabName = "evidenceQuality", icon = icon("server", lib = "font-awesome")), - #menuSubItem(text = HTML(paste0("", get_rv_labels("cohortConstruction"), "")), tabName = "cohortConstruction", icon = icon("layer-group", lib = "font-awesome")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_achilles"), "")), tabName = "achilles", icon = icon("glyphicon glyphicon-stats", lib = "glyphicon")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_omop"), "")), tabName = "omop_visualizations", icon = icon("glyphicon glyphicon-stats", lib = "glyphicon")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_Cohort_Constructor"), "")), tabName = "CohortConstructor", icon = icon("arrows-split-up-and-left", lib = "font-awesome")), - menuSubItem(text = HTML(paste0("", get_rv_labels("menu_Feature_Extraction"), "")), tabName = "FeatureExtraction", icon = icon("arrows-split-up-and-left", lib = "font-awesome")) - - - ), - menuItem( - HTML(paste0("", get_rv_labels("deeplearning"), "")), - tabName = "deeplearning", - icon = icon("gear"), - - # # --- CNN Deep section with its own submenus --- - # menuItem( - # HTML(paste0("", get_rv_labels("cnn_deep"), "")), - # tabName = "cnntransformers", - # icon = icon("gear", lib = "font-awesome"), - # - # menuSubItem(get_rv_labels("dashboard"), tabName = "dashboard", icon = icon("dashboard")), - # menuSubItem(get_rv_labels("c_pipeline"), tabName = "create", icon = icon("plus-circle")), - # menuSubItem(get_rv_labels("t_model"), tabName = "train", icon = icon("cog")), - # menuSubItem(get_rv_labels("m_predictions"), tabName = "predict", icon = icon("eye")), - # menuSubItem(get_rv_labels("v_jobs"), tabName = "jobs", icon = icon("list")), - # menuSubItem(get_rv_labels("v_datasets"), tabName = "datasets", icon = icon("database")), - # menuSubItem(get_rv_labels("d_jobs"), tabName = "delete", icon = icon("trash")) - #), - - # --- Transformers Deep --- - menuItem( - HTML(paste0("", get_rv_labels("transformers_deep"), "")), - tabName = "cnndeep", - icon = icon("server", lib = "font-awesome") - ) - ) - , - - - menuItem(HTML(paste0("", get_rv_labels("menu_additional_resources"), "")), tabName = "addResources", icon = icon("book")), - br(), - div( - style="display: flex; justify-content: flex-start; gap: 10px; margin-top: 5px;", - actionBttn("logoutID", label = get_rv_labels("logoutID"), class = "btn") - #is_logged_in( - # id = app_login_config$APP_ID,login::logout_button(id = "logout", label = "Sign out")) - ) - - ) + menuItem(text = HTML(paste0("", get_rv_labels("menu_home"), "")), tabName = "homePage", icon = icon("house"), selected = TRUE), + menuItem(text = HTML(paste0("", get_rv_labels("menu_source_data"), "")), tabName = "sourcedata", icon = icon("file-import", lib = "font-awesome")), + menuItem( + text = HTML(paste0("", get_rv_labels("menu_manage_data"), "")), tabName = "manageData", icon = icon("glyphicon glyphicon-tasks", lib = "glyphicon"), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_overview"), "")), tabName = "Overview", icon = icon("table-columns", lib = "font-awesome")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_explore"), "")), tabName = "Explore", icon = icon("object-ungroup", lib = "font-awesome")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_transform"), "")), tabName = "Transform", icon = icon("table-columns", lib = "font-awesome")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_combine_data"), "")), tabName = "combineData", icon = icon("table-columns", lib = "font-awesome")) + ), + menuItem(text = HTML(paste0("", get_rv_labels("menu_visualize_data"), "")), tabName = "visualizeData", icon = icon("glyphicon glyphicon-stats", lib = "glyphicon"), + menuItem(text = HTML(paste0("", get_rv_labels("menu_summarizeAutomatic"), "")), tabName = "summarizeAutomatic", icon = icon("glyphicon glyphicon-stats", lib = "glyphicon") + + ), + menuItem(text = HTML(paste0("", get_rv_labels("menu_summarizeCustom"), "")), tabName = "summarizeCustom", icon = icon("chart-line"))), + + + menuItem(text = HTML(paste0("", get_rv_labels("menu_research_question"), "")), tabName = "researchQuestions", icon = icon("file-import", lib = "font-awesome"), selected = FALSE), + + + menuItem( + text = HTML(paste0("", get_rv_labels("menu_machine_learning"), "")), tabName = "machineLearning", icon = icon("code-merge", lib = "font-awesome"), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_setup_models"), "")), tabName = "setupModels", icon = icon("arrows-split-up-and-left", lib = "font-awesome")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_feature_engineering"), "")), tabName = "featureEngineering", icon = icon("sitemap", lib = "font-awesome")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_train_model"), "")), tabName = "trainModel", icon = icon("gear", lib = "font-awesome")), + menuSubItem(text =HTML(paste0("", get_rv_labels("menu_validate_model"), "")), tabName = "validateDeployModel", icon = icon("server", lib = "font-awesome")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_predict"), "")), tabName = "predictClassify", icon = icon("layer-group", lib = "font-awesome")) + ), + menuItem(text = HTML(paste0("", get_rv_labels("menu_omop_data"), "")), tabName = "omopAnalysis", icon = icon("magnifying-glass-chart", lib = "font-awesome"), + menuSubItem(text =HTML(paste0("", get_rv_labels("evidenceQuality"), "")), tabName = "evidenceQuality", icon = icon("server", lib = "font-awesome")), + #menuSubItem(text = HTML(paste0("", get_rv_labels("cohortConstruction"), "")), tabName = "cohortConstruction", icon = icon("layer-group", lib = "font-awesome")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_achilles"), "")), tabName = "achilles", icon = icon("glyphicon glyphicon-stats", lib = "glyphicon")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_omop"), "")), tabName = "omop_visualizations", icon = icon("glyphicon glyphicon-stats", lib = "glyphicon")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_Cohort_Constructor"), "")), tabName = "CohortConstructor", icon = icon("arrows-split-up-and-left", lib = "font-awesome")), + menuSubItem(text = HTML(paste0("", get_rv_labels("menu_Feature_Extraction"), "")), tabName = "FeatureExtraction", icon = icon("arrows-split-up-and-left", lib = "font-awesome")) + + + ), + menuItem( + HTML(paste0("", get_rv_labels("deeplearning"), "")), + tabName = "deeplearning", + icon = icon("gear"), + + # # --- CNN Deep section with its own submenus --- + # menuItem( + # HTML(paste0("", get_rv_labels("cnn_deep"), "")), + # tabName = "cnntransformers", + # icon = icon("gear", lib = "font-awesome"), + # + # menuSubItem(get_rv_labels("dashboard"), tabName = "dashboard", icon = icon("dashboard")), + # menuSubItem(get_rv_labels("c_pipeline"), tabName = "create", icon = icon("plus-circle")), + # menuSubItem(get_rv_labels("t_model"), tabName = "train", icon = icon("cog")), + # menuSubItem(get_rv_labels("m_predictions"), tabName = "predict", icon = icon("eye")), + # menuSubItem(get_rv_labels("v_jobs"), tabName = "jobs", icon = icon("list")), + # menuSubItem(get_rv_labels("v_datasets"), tabName = "datasets", icon = icon("database")), + # menuSubItem(get_rv_labels("d_jobs"), tabName = "delete", icon = icon("trash")) + #), + + + # --- Transformers Deep --- + menuItem( + HTML(paste0("", get_rv_labels("transformers_deep"), "")), + tabName = "cnndeep", + icon = icon("server", lib = "font-awesome") + ) + ) + , + menuItem( + text = HTML(paste0("", get_rv_labels("menu_data_anonymization"), "")), + icon = icon("user-shield", lib = "font-awesome"), + menuSubItem( + text = HTML(paste0("", get_rv_labels("menu_quant_anonymization"), "")), + tabName = "anonymization_quant", + icon = icon("calculator") + ), + menuSubItem( + text = HTML(paste0("", get_rv_labels("menu_qual_anonymization"), "")), + tabName = "anonymization_qual", + icon = icon("align-left") + ) + ), + + + + menuItem(HTML(paste0("", get_rv_labels("menu_additional_resources"), "")), tabName = "addResources", icon = icon("book")), + br(), + div( + style="display: flex; justify-content: flex-start; gap: 10px; margin-top: 5px;", + actionBttn("logoutID", label = get_rv_labels("logoutID"), class = "btn") + #is_logged_in( + # id = app_login_config$APP_ID,login::logout_button(id = "logout", label = "Sign out")) + ) + + ) }) -} - +} \ No newline at end of file diff --git a/server/load_libraries.R b/server/load_libraries.R new file mode 100644 index 0000000..5517f39 --- /dev/null +++ b/server/load_libraries.R @@ -0,0 +1,19 @@ +library(shiny) +library(shinyjs) +library(sortable) # drag-and-drop QID picker +library(shinyWidgets) # gauges & widgets +library(flexdashboard) # gaugeOutput() +library(shinyAce) # Ace editors +library(leaflet) # map preview for coordinates +library(dplyr) +library(readr) +library(readxl) +library(openxlsx) +library(haven) +library(shinyjs) +library(shiny) +library(digest) +library(sdcMicro) +library(rmarkdown) +library(pagedown) +library(data.table) diff --git a/static_files/labelling_file.xlsx b/static_files/labelling_file.xlsx index cf4ef8ddbbb002a89132602bd112cbc615c9c7f1..4d2eabc915d1734d91ea3426c6f4c3cc68a5c490 100644 GIT binary patch literal 1347905 zcmeFZWk6irmY@xR;1D!ug1fuB7G7v@hakaS6CikS4Vnt>!JXg`90I{1xV!6|%G-VW 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and R packages should be included -#Load R packages -source(paste0(getwd(), "/ui/load_r_packages.R")) +.app_root <- normalizePath(getwd(), mustWork = TRUE) -## Language change utilities -source("R/shinyutilities.R") +# ============================================================================= +# Static assets +# ============================================================================= +.anon_dir <- file.path(.app_root, "www", "anon_assets") -source(paste0(getwd(), "/server/maskedpassinput.R")) -# U1 Add this line of code to call automl_UI from UI folder -source("ui/automl_ui.R") -# Automl controls -source("ui/automl_controls_ui.R") -source("ui/train_model_ui.R") +if (dir.exists(.anon_dir)) { + shiny::addResourcePath("anon_assets", .anon_dir) +} -# Load UI function before deploy_model_ui() -source("ui/deploy_model_ui.R") -#Load Headertag -source(paste0(getwd(), "/ui/login_credentials.R")) +# ============================================================================= +# Load packages FIRST (this should load shiny, shinydashboard, shinyjs, waiter, login, etc) +# ============================================================================= +source(file.path(.app_root, "ui", "load_r_packages.R")) -source(paste0(getwd(), "/ui/headertag.R")) -#Load App Theme -source(paste0(getwd(), "/ui/appTheme.R")) -#Load Header -source(paste0(getwd(), "/ui/header.R")) -#Load Footer -source(paste0(getwd(), "/ui/footer.R")) -source(paste0(getwd(), "/ui/homepage.R")) +# ============================================================================= +# UI helpers / assets +# ============================================================================= +source(file.path(.app_root, "ui", "sidebar_hover_collapse.R")) +source(file.path(.app_root, "R", "shinyutilities.R")) +source(file.path(.app_root, "server", "maskedpassinput.R")) -source("ui/dashboard_body.R") +# ============================================================================= +# App UI modules +# ============================================================================= +source(file.path(.app_root, "ui", "automl_ui.R")) +source(file.path(.app_root, "ui", "automl_controls_ui.R")) +source(file.path(.app_root, "ui", "train_model_ui.R")) +source(file.path(.app_root, "ui", "deploy_model_ui.R")) +# ============================================================================= +# Login / header / theme / footer / homepage +# ============================================================================= +source(file.path(.app_root, "ui", "login_credentials.R")) +source(file.path(.app_root, "ui", "headertag.R")) +source(file.path(.app_root, "ui", "appTheme.R")) +source(file.path(.app_root, "ui", "header.R")) +source(file.path(.app_root, "ui", "footer.R")) +source(file.path(.app_root, "ui", "homepage.R")) -#Sidebar -aphrcSiderbar <- dashboardSidebar( - width = "20%", - #menuItemOutput("dynamic_meinu_aphrc"), - sidebarMenuOutput("dynamic_meinu_aphrc") - #menuItem("AutoML", tabName = "automl_tab", icon = icon("robot")) - +# ============================================================================= +# Anonymization UI (module + tab UIs) +# ============================================================================= +# NOTE: local=FALSE so module functions exist at global scope +.mod_quant_path <- file.path(.app_root, "modules", "mod_quant_anonymization.R") +if (file.exists(.mod_quant_path)) { + source(.mod_quant_path, local = FALSE) +} + +.anon_ui_path <- file.path(.app_root, "ui", "anonymization_ui.R") +if (file.exists(.anon_ui_path)) { + source(.anon_ui_path, local = FALSE) +} + +# ============================================================================= +# Dashboard body must be last (it uses functions above) +# ============================================================================= +source(file.path(.app_root, "ui", "dashboard_body.R")) + +# ============================================================================= +# Shinydashboard shell +# ============================================================================= + +aphrcSiderbar <- shinydashboard::dashboardSidebar( + width = "240%", + shinydashboard::sidebarMenuOutput("dynamic_meinu_aphrc") ) -#Body -fluidPage( - useShinyjs(), - useWaiter(), - - waiterShowOnLoad( - color = "#FFF", - html = spin_loaders(id = 2, style="width:56px;height:56px;color:#7BC148;"), - logo= "WWW/aphrc.png"), +# IMPORTANT: +# - disable=TRUE removes the header bar entirely (no hamburger) +# - disable=FALSE keeps header bar + hamburger toggle available +aphrcHeader <- shinydashboard::dashboardHeader(disable = FALSE) + +# ============================================================================= +# UI object (THIS MUST BE NAMED `ui` if using shinyApp(ui=ui,...)) +# ============================================================================= - div( - id = "auth_wrapper1", # <– will be shown after spinner - login::is_logged_in( - id = app_login_config$APP_ID, header - ), - - aphrcHeader <- dashboardHeader(disable = TRUE), - - login::is_not_logged_in( - id = app_login_config$APP_ID, - div( - class = "auth-container", - br(), - div( - class = "auth-title text-center", - tags$img(src = "aphrc.png", height = "80px", style = "margin-bottom: 15px;"), - h3("Welcome to Nocode Platform") - ), - div( - class = "toggle-buttons", - actionButton("show_login", "Login", class = "btn btn-outline-success"), - actionButton("show_signup", "Sign Up", class = "btn btn-outline-success"), - actionButton("show_reset", "Reset Password", class = "btn btn-outline-success") - ), - div(id = "login_form", - login::login_ui(id = app_login_config$APP_ID) - ), - div(id = "signup_form", style = "display:none;", - login::new_user_ui(id = app_login_config$APP_ID) - ), - div(id = "reset_form", style = "display:none;", - login::reset_password_ui(id = app_login_config$APP_ID) - ) +ui <- shiny::fluidPage( + shinyjs::useShinyjs(), + waiter::useWaiter(), + + shiny::tags$head( + # anonymization CSS in WWW/anon_assets + shiny::tags$link(rel = "stylesheet", type = "text/css", href = "anon_assets/custom.css?v=20260129"), + # Font Awesome (needed by anonymization module icons and your hover assets) + shiny::tags$link(rel = "stylesheet", + href = "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.0/css/all.min.css"), + + # hover-collapse assets (your helper) + sidebar_hover_collapse_assets(default_topbar_px = 110) + ), + + shiny::div( + id = "auth_wrapper1", + + # Logged-in header (your existing `header` object from ui/header.R) + login::is_logged_in( + id = app_login_config$APP_ID, + header + ), + + # Not logged-in auth UI + login::is_not_logged_in( + id = app_login_config$APP_ID, + shiny::div( + class = "auth-container", + shiny::br(), + + shiny::div( + class = "auth-title text-center", + shiny::tags$img(src = "aphrc.png", height = "80px", + style = "margin-bottom: 15px;"), + shiny::tags$h3("Welcome to Nocode Platform") + ), + + shiny::div( + class = "toggle-buttons", + shiny::actionButton("show_login", "Login", class = "btn btn-outline-success"), + shiny::actionButton("show_signup", "Sign Up", class = "btn btn-outline-success"), + shiny::actionButton("show_reset", "Reset Password", class = "btn btn-outline-success") + ), + + shiny::div( + id = "login_form", + login::login_ui(id = app_login_config$APP_ID) + ), + + shiny::div( + id = "signup_form", style = "display:none;", + login::new_user_ui(id = app_login_config$APP_ID) + ), + + shiny::div( + id = "reset_form", style = "display:none;", + login::reset_password_ui(id = app_login_config$APP_ID) ) - ), - - login::is_logged_in( - id = app_login_config$APP_ID, - div(dashboardPage(aphrcHeader, aphrcSiderbar, aphrcBody, skin = "green")) - ), - - login::is_logged_in( - id = app_login_config$APP_ID, - div(footer) ) - )) - - + ), + + # Logged-in dashboard + login::is_logged_in( + id = app_login_config$APP_ID, + shinydashboard::dashboardPage( + header = aphrcHeader, + sidebar = aphrcSiderbar, + body = aphrcBody, + skin = "green" + ) + ), + + # Logged-in footer + login::is_logged_in( + id = app_login_config$APP_ID, + footer + ) + ) +) diff --git a/ui/anonymization_ui.R b/ui/anonymization_ui.R new file mode 100644 index 0000000..14f16b4 --- /dev/null +++ b/ui/anonymization_ui.R @@ -0,0 +1,14 @@ +anonymization_quant_ui <- function() { + shinydashboard::tabItem( + tabName = "anonymization_quant", + mod_quant_anon_ui("quant_anon") + ) +} + +anonymization_qual_ui <- function() { + shinydashboard::tabItem( + tabName = "anonymization_qual", + shiny::h3("Qualitative Anonymization"), + shiny::p("Coming soon / integrate later.") + ) +} diff --git a/ui/dashboard_body.R b/ui/dashboard_body.R index a38606d..0010713 100644 --- a/ui/dashboard_body.R +++ b/ui/dashboard_body.R @@ -22,125 +22,130 @@ source("ui/add_resources_ui.R") source("ui/deploy_model_ui.R", local=TRUE) source("ui/predict_pycaret_ui.R", local = TRUE) + #### ---- Change language -------------------------------------------- source("server/change_language.R", local = TRUE) #### Extracts language specific labels get_rv_labels = function(var) { - get_rv_labels_base(rv_lang$labelling_file_df, var) + get_rv_labels_base(rv_lang$labelling_file_df, var) } - aphrcBody <- dashboardBody( - headertag, - useShinyjs(), - useAttendant(), - # useWaiter(), #FIXME: Use better one - theme = appTheme, - # -- Handler JS pour capter les clics 'Deploy' dans la DataTable du module -- - # dashboard_body (une seule fois, avant tabItems) - tags$head( - tags$script(HTML(" - Shiny.addCustomMessageHandler('bindDeployBtn', function(msg) { - var ns = msg.ns; - - // Nettoie d'abord les handlers (évite doublons) - $(document).off('click', '.action-deploy'); - $(document).off('click', '.action-stop'); - - // ---- Deploy ---- - $(document).on('click', '.action-deploy', function(){ - var $btn = $(this); - var mid = $btn.data('model'); - - // Feedback visuel immédiat - $btn.prop('disabled', true).text('Deploying...'); - - // Envoi à Shiny - Shiny.setInputValue(ns + 'deploy_model_id', mid, {priority: 'event'}); - }); - - // ---- Stop ---- - $(document).on('click', '.action-stop', function(){ - var $btn = $(this); - var mid = $btn.data('model'); - - $btn.prop('disabled', true).text('Stopping...'); - - Shiny.setInputValue(ns + 'stop_model_id', mid, {priority: 'event'}); - }); - }); - Shiny.addCustomMessageHandler('openSwagger', function(msg) { - if (msg && msg.url) { window.open(msg.url, '_blank'); } - }); - ")) - ), - - tabItems( - tabItem(tabName = "homePage" - , class = "active" - , homepage() - ) - - ## Source data - , sourcedata_ui() - - ## Data overview - , overview_ui() - - ## Explore - , explore_ui() - - ## Transform data - , transform_ui() - - ## Combine data - , combinedata_ui() - - ## Summarize data automatic - , summarize_automatic_ui() - - ## Summarize data customize - , summarize_custom_ui() - - ## Research questions - , research_questions_ui() - - ## Setup models - , setup_models_ui() - - ## Feature engineering - , feature_engineering_ui() - - ## Evidence quality - , evidence_quality_ui() - - ## Train models: FIXME: NOW - Remove text in the train_model_ui - , train_all_model_ui() - - ## Validate and deploy models - , validate_deploy_model_ui() - - ## Prediction UI - , predict_classify_ui() - - ## Deep learning UI - , deeplearning_ui() - - ## Cohort constructor - , cohort_constructor_ui() - - ## Achilles - , achilles_ui() - - ## Feature extraction - , feature_extraction_ui() - - ## OMOP visualization - , omop_visualizations_ui() - - ## OMOP resources - , add_resources_ui() - - ) + headertag, + useShinyjs(), + useAttendant(), + # useWaiter(), #FIXME: Use better one + theme = appTheme, + + # -- Handler JS pour capter les clics 'Deploy' dans la DataTable du module -- + # dashboard_body (une seule fois, avant tabItems) + tags$head( + tags$script(HTML(" + Shiny.addCustomMessageHandler('bindDeployBtn', function(msg) { + var ns = msg.ns; + + // Nettoie d'abord les handlers (évite doublons) + $(document).off('click', '.action-deploy'); + $(document).off('click', '.action-stop'); + + // ---- Deploy ---- + $(document).on('click', '.action-deploy', function(){ + var $btn = $(this); + var mid = $btn.data('model'); + + // Feedback visuel immédiat + $btn.prop('disabled', true).text('Deploying...'); + + // Envoi à Shiny + Shiny.setInputValue(ns + 'deploy_model_id', mid, {priority: 'event'}); + }); + + // ---- Stop ---- + $(document).on('click', '.action-stop', function(){ + var $btn = $(this); + var mid = $btn.data('model'); + + $btn.prop('disabled', true).text('Stopping...'); + + Shiny.setInputValue(ns + 'stop_model_id', mid, {priority: 'event'}); + }); + }); + + Shiny.addCustomMessageHandler('openSwagger', function(msg) { + if (msg && msg.url) { window.open(msg.url, '_blank'); } + }); + ")) + ), + + tabItems( + tabItem(tabName = "homePage", + class = "active", + homepage() + ) + + ## Source data + , sourcedata_ui() + + ## Data overview + , overview_ui() + + ## Explore + , explore_ui() + + ## Transform data + , transform_ui() + + ## Combine data + , combinedata_ui() + + ## Summarize data automatic + , summarize_automatic_ui() + + ## Summarize data customize + , summarize_custom_ui() + + ## Research questions + , research_questions_ui() + + ## Setup models + , setup_models_ui() + + ## Feature engineering + , feature_engineering_ui() + + ## Evidence quality + , evidence_quality_ui() + + ## Train models: FIXME: NOW - Remove text in the train_model_ui + , train_all_model_ui() + + ## Validate and deploy models + , validate_deploy_model_ui() + + ## Prediction UI + , predict_classify_ui() + + ## Deep learning UI + , deeplearning_ui() + + ## Cohort constructor + , cohort_constructor_ui() + + ## Achilles + , achilles_ui() + + ## Feature extraction + , feature_extraction_ui() + + ## OMOP visualization + , omop_visualizations_ui() + + ## OMOP resources + , add_resources_ui() + + ## Anonymization + , anonymization_quant_ui() + , anonymization_qual_ui() + ) ) diff --git a/ui/load_r_packages.R b/ui/load_r_packages.R index d45c665..377b1fe 100644 --- a/ui/load_r_packages.R +++ b/ui/load_r_packages.R @@ -92,7 +92,25 @@ libraries <- c( "MLmetrics", "fs" ) - +library(shiny) +library(shinyjs) +library(sortable) +library(shinyWidgets) +library(flexdashboard) +library(shinyAce) +library(leaflet) +library(dplyr) +library(readr) +library(readxl) +library(openxlsx) +library(haven) +library(shinyjs) +library(shiny) +library(digest) +library(sdcMicro) +library(rmarkdown) +library(pagedown) +library(data.table) # Install missing CRAN packages missing <- setdiff(libraries, rownames(installed.packages())) diff --git a/ui/sidebar_hover_collapse.R b/ui/sidebar_hover_collapse.R new file mode 100644 index 0000000..0969ba5 --- /dev/null +++ b/ui/sidebar_hover_collapse.R @@ -0,0 +1,175 @@ +# ui/sidebar_hover_collapse.R + +sidebar_hover_collapse_assets <- function( + collapsed_width = 56, # px + expanded_width = 240, # px + transition_ms = 320, # smooth + default_topbar_px = 110 # <- IMPORTANT: fallback if JS can't detect header +) { + shiny::tags$script(shiny::HTML(" +(function () { + + function killAdminLTEHeaderAndToggle() { + // Remove the hamburger toggle wherever it exists + document.querySelectorAll('.sidebar-toggle, a.sidebar-toggle').forEach(function(el){ + if (el && el.parentNode) el.parentNode.removeChild(el); + }); + + // Remove the AdminLTE header bar container if it exists + var mh = document.querySelector('.main-header'); + if (mh && mh.parentNode) mh.parentNode.removeChild(mh); + } + + // Run now + killAdminLTEHeaderAndToggle(); + + // Run again after Shiny attaches UI + document.addEventListener('shiny:connected', function(){ + setTimeout(killAdminLTEHeaderAndToggle, 50); + setTimeout(killAdminLTEHeaderAndToggle, 250); + setTimeout(killAdminLTEHeaderAndToggle, 800); + }); + + // Observe DOM changes (login / dynamic UI) + var obs = new MutationObserver(function(){ killAdminLTEHeaderAndToggle(); }); + if (document.body) obs.observe(document.body, { childList: true, subtree: true }); + +})(); +")) + + css <- sprintf(" + :root{ + /* Fallback so sidebar never goes behind the green header */ + --aphrc-topbar-height: %dpx; + } + + /* Sidebar position */ + .main-sidebar{ + position: fixed !important; + left: 0 !important; + top: var(--aphrc-topbar-height) !important; + height: calc(100vh - var(--aphrc-topbar-height)) !important; + + overflow-x: hidden; + overflow-y: auto; + + width: %dpx !important; + transition: width %dms cubic-bezier(0.22, 1, 0.36, 1); + will-change: width; + z-index: 2000; + } + + /* Fix weird top spacing inside sidebar (THIS fixes the 'home icon too high') */ + .main-sidebar .sidebar{ + padding-top: 10px !important; + } + .main-sidebar .sidebar-menu{ + margin-top: 0 !important; + } + + /* Make menu items consistent height + vertically centered */ + .main-sidebar .sidebar-menu > li > a{ + height: 44px !important; + display: flex !important; + align-items: center !important; + box-sizing: border-box; + } + + /* Keep content aligned to collapsed sidebar (no jump) */ + .content-wrapper, .right-side, .main-footer{ + margin-left: %dpx !important; + transition: margin-left %dms cubic-bezier(0.22, 1, 0.36, 1); + } + + /* Expand on hover */ + .main-sidebar:hover{ + width: %dpx !important; + } + + /* Hide text when collapsed */ + .main-sidebar .sidebar-menu > li > a > span, + .main-sidebar .sidebar-menu > li > a > .pull-right-container{ + display: none !important; + } + + /* Collapsed: icon centered */ + .main-sidebar .sidebar-menu > li > a{ + justify-content: center !important; + padding-left: 0 !important; + padding-right: 0 !important; + } + .main-sidebar .sidebar-menu > li > a > i{ + font-size: 16px; + margin: 0 !important; + width: auto !important; + } + + /* On hover: show text and align left */ + .main-sidebar:hover .sidebar-menu > li > a{ + justify-content: flex-start !important; + padding-left: 15px !important; + padding-right: 15px !important; + } + .main-sidebar:hover .sidebar-menu > li > a > i{ + margin-right: 8px !important; + } + .main-sidebar:hover .sidebar-menu > li > a > span, + .main-sidebar:hover .sidebar-menu > li > a > .pull-right-container{ + display: inline-block !important; + } + + /* Keep shinydashboard header disabled (do NOT force-hide main-header aggressively) */ + .content-wrapper{ padding-top: 0 !important; } + ", + default_topbar_px, + collapsed_width, transition_ms, + collapsed_width, transition_ms, + expanded_width + ) + + shiny::tagList( + shiny::tags$style(shiny::HTML(css)), + shiny::tags$script(shiny::HTML(" + (function () { + + function pickHeader() { + // Try common candidates; we want the GREEN header region container + return document.querySelector('#auth_wrapper1 .header') + || document.querySelector('.header') + || document.querySelector('#auth_wrapper1 header') + || document.querySelector('header'); + } + + function setTopbarHeight() { + var el = pickHeader(); + if (!el) return; + + // Use bottom edge: exactly where green block ends + var r = el.getBoundingClientRect(); + var h = Math.max(0, Math.round(r.bottom)); + if (h > 20) { + document.documentElement.style.setProperty('--aphrc-topbar-height', h + 'px'); + } + } + + // MutationObserver: catches Shiny/login UI changes reliably + var obs = new MutationObserver(function(){ setTopbarHeight(); }); + + function startObserver() { + if (!document.body) return; + obs.observe(document.body, { childList: true, subtree: true }); + setTopbarHeight(); + } + + if (document.readyState === 'loading') { + document.addEventListener('DOMContentLoaded', startObserver); + } else { + startObserver(); + } + + window.addEventListener('resize', setTopbarHeight); + + })(); + ")) + ) +} diff --git a/users_db/users.sqlite b/users_db/users.sqlite index 3019f3891dc68af6646173ed3631914f4c68f0c6..68509dcf74899e51792455ef187b344148046f86 100644 GIT binary patch literal 20480 zcmeI&dsGuw9tZF|Chx~kt7b)8XRC09xl$kr2_P8ISn&ZO(Mr|gBqSIO2_*@Ft7y>f 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