Enhance README, add CI workflow, and initial tests

.github/workflows/main.yml

@@ -0,0 +1,41 @@
+name: Python Application CI
+
+on: [push, pull_request]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Check out code
+      uses: actions/checkout@v3
+
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.x' # Use a generic Python 3 version
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install nltk flake8
+        # The chatcoder.py script itself calls nltk.download().
+        # We need to ensure these resources are available for any import/run checks.
+        # Running this command in the workflow to pre-download.
+        python -c "import nltk; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger')"
+
+    - name: Lint with Flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
+    - name: Test basic script import
+      run: |
+        python -c "import chatcoder"
+
+    # Future step: Run unit tests (if they become viable)
+    # - name: Run Unit Tests
+    #   run: |
+    #     python -m unittest discover -s . -p "test_*.py"

README.md

@@ -1,3 +1,51 @@
-# NaturalLanguagecoder
-a small utility to take prompt strings and refine them into a structured format for better interpretation by LLM's. It's very raw and in need of detailing and cleanup work as well as interface enhancement etc.
+# NaturalLanguageCoder
+[![Python Application CI](https://github.com/YOUR_USERNAME/YOUR_REPOSITORY/actions/workflows/main.yml/badge.svg)](https://github.com/YOUR_USERNAME/YOUR_REPOSITORY/actions/workflows/main.yml)
 
+## Description
+A Python utility that converts natural language instructions into a basic code structure. It uses NLTK for tokenization and part-of-speech tagging to perform a rudimentary analysis and conversion. This tool is an initial exploration and has significant room for improvement in terms of accuracy, complexity of conversions, and user interface.
+
+## Current Features
+*   Accepts natural language input via a simple Tkinter GUI.
+*   Tokenizes and tags words using NLTK.
+*   Converts verbs into function calls (e.g., "print" -> "print()").
+*   Treats nouns as potential variables or objects.
+*   Allows saving the generated code snippet to a file named `output.py`.
+
+## Limitations
+*   The conversion logic is very basic and rule-based. It does not understand context, complex grammar, or specific programming language syntax beyond simple function calls and variable names.
+*   Error handling is minimal.
+*   The GUI is very basic.
+
+## Installation
+1.  Clone the repository: `git clone <repository-url>`
+2.  Navigate to the project directory: `cd NaturalLanguagecoder`
+3.  Install required libraries: `pip install nltk tkinter`
+4.  The first time you run the script, NLTK will download necessary resources ('punkt' and 'averaged_perceptron_tagger'). If this fails, you may need to run Python interactively and execute:
+    ```python
+    import nltk
+    nltk.download('punkt')
+    nltk.download('averaged_perceptron_tagger')
+    ```
+
+## Usage
+1.  Run the script: `python chatcoder.py`
+2.  Enter your natural language instruction in the input field (e.g., "create variable data and print hello").
+3.  Click "Convert to Code".
+4.  The generated code structure will appear in the text area below.
+5.  Click "Save" to save the output to `output.py` in the same directory.
+
+## Example Conversion
+*   Input: `define function process data`
+*   Output: `define() function() process() data ` (Illustrative of current simple logic)
+
+## Contributing
+Contributions are welcome! If you'd like to improve NaturalLanguageCoder, please consider:
+    *   Enhancing the parsing and code generation logic.
+    *   Adding support for more complex programming constructs.
+    *   Improving the user interface.
+    *   Adding comprehensive error handling.
+    *   Expanding test coverage.
+Please fork the repository, make your changes on a new branch, and submit a pull request.
+
+## License
+This project is licensed under the terms of the LICENSE file.

test_chatcoder.py

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+import unittest
+from unittest.mock import patch, MagicMock
+import sys
+
+# Define global mocks to be used by sys.modules patcher
+mock_tk_module_for_setup = MagicMock(name='setup_mock_tk')
+mock_ttk_module_for_setup = MagicMock(name='setup_mock_ttk')
+modules_to_mock_for_setup = {
+    'tkinter': mock_tk_module_for_setup,
+    'tkinter.ttk': mock_ttk_module_for_setup
+}
+
+# Placeholders for patchers and imported modules/classes
+sys_modules_patcher_module_level = None
+nltk_download_patcher_module_level = None
+CodeConverterApp_module_level = None
+nltk_module_for_patching = None # To store the imported nltk module
+
+def setUpModule():
+    """Called once, before any tests in this module run."""
+    global sys_modules_patcher_module_level, nltk_download_patcher_module_level
+    global CodeConverterApp_module_level, nltk_module_for_patching
+
+    sys_modules_patcher_module_level = patch.dict(sys.modules, modules_to_mock_for_setup)
+    sys_modules_patcher_module_level.start()
+
+    # Patch nltk.download before chatcoder (which imports nltk) is imported
+    nltk_download_patcher_module_level = patch('nltk.download', MagicMock(return_value=True))
+    nltk_download_patcher_module_level.start()
+
+    # Import nltk module itself into a global for direct patching later
+    import nltk as imported_nltk_module
+    nltk_module_for_patching = imported_nltk_module
+
+    # Import CodeConverterApp now. It will use the above imported_nltk_module if it also does 'import nltk'.
+    from chatcoder import CodeConverterApp as imported_app
+    CodeConverterApp_module_level = imported_app
+
+
+def tearDownModule():
+    """Called once, after all tests in this module have run."""
+    if nltk_download_patcher_module_level:
+        nltk_download_patcher_module_level.stop()
+    if sys_modules_patcher_module_level:
+        sys_modules_patcher_module_level.stop()
+
+class TestCodeConverterAppSimplified(unittest.TestCase):
+
+    def test_convert_to_code_logic(self):
+        # Use patch.object to patch word_tokenize and pos_tag directly on the
+        # nltk module object that was imported in setUpModule.
+        with patch.object(nltk_module_for_patching, 'word_tokenize', MagicMock(return_value=['test', 'function'])) as mock_word_tokenize_arg, \
+             patch.object(nltk_module_for_patching, 'pos_tag', MagicMock(return_value=[('test', 'VB'), ('function', 'NN')])) as mock_pos_tag_arg:
+
+            # Arrange
+            # NLTK mocks are set up by the with statement's patch.object
+
+            try:
+                app = CodeConverterApp_module_level()
+            except Exception as e:
+                self.fail(f"Failed to instantiate CodeConverterApp: {e}")
+
+            app.input_entry = MagicMock(name='mock_input_entry')
+            app.input_entry.get = MagicMock(return_value="test function")
+
+            app.output_text = MagicMock(name='mock_output_text')
+            app.output_text.delete = MagicMock()
+            app.output_text.insert = MagicMock()
+
+            # Act
+            app.convert_to_code()
+
+            # Assert
+            mock_word_tokenize_arg.assert_called_once_with("test function")
+            mock_pos_tag_arg.assert_called_once_with(['test', 'function'])
+            app.output_text.insert.assert_called_once_with(mock_tk_module_for_setup.END, "test() function ")
+
+if __name__ == '__main__':
+    unittest.main()