Update README.md

README.md

@@ -1,212 +1,219 @@
-#  Python → Streamlit Converter Pro
-
-A powerful, production-ready tool that converts Python scripts and Jupyter Notebooks into fully functional Streamlit applications. Handles large files, preserves comments and markdown, and provides multiple conversion strategies.
-
-## ✨ Features
-
-### Core Capabilities
-- **Multi-Format Support**: Convert `.py` files and `.ipynb` notebooks
-- **Large File Handling**: Efficiently processes files up to 5MB+ with optimized algorithms
-- **Comment Preservation**: All comments, docstrings, and inline documentation are preserved
-- **Markdown Support**: Notebook markdown cells are converted to commented Python code
-- **Batch Processing**: Upload ZIP archives to convert multiple files at once
-
-### Conversion Strategies
-
-1. **Hybrid Mode (Recommended)**: Combines AST parsing with regex patterns
-   - Best balance of accuracy and performance
-   - Handles complex code structures
-   - Preserves formatting and comments
-
-2. **AST Mode (Precise)**: Pure abstract syntax tree transformation
-   - Deep understanding of code structure
-   - Best for complex transformations
-   - Preserves all code semantics
-
-3. **Regex Mode (Fast)**: Pattern-based matching
-   - Fastest for very large files
-   - Good for simple conversions
-   - Efficient memory usage
-
-4. **Auto Mode**: Automatically selects the best strategy based on file size
-
-### What Gets Converted?
-
-| Original Code | Streamlit Equivalent |
-|--------------|---------------------|
-| `print(x)` | `st.write(x)` |
-| `display(df)` | `st.dataframe(df)` |
-| `df.head()` / `df.tail()` | `st.dataframe(df.head())` |
-| `plt.show()` | `st.pyplot(plt.gcf())` |
-| `fig.show()` (Plotly) | `st.plotly_chart(fig)` |
-| Markdown cells | Commented markdown |
-| All comments | Preserved |
-
-## 🚀 Installation
-
-1. Clone or download this repository
-2. Install dependencies:
-
-```bash
-pip install -r Requirements.txt
-```
-
-## 📖 Usage
-
-### Running the Application
-
-```bash
-streamlit run app.py
-```
-
-The application will open in your default web browser.
-
-### Basic Workflow
-
-1. **Upload Files**: Use the sidebar to upload Python files or Jupyter notebooks
-   - Individual files: Upload one or more files directly
-   - ZIP archive: Upload a ZIP containing multiple files
-
-2. **Configure Settings** (Optional):
-   - Choose conversion strategy (Hybrid recommended)
-   - Set large file threshold
-   - Enable/disable main guard
-   - Toggle comment preservation
-
-3. **Review & Download**: 
-   - View original and converted code side-by-side
-   - Check conversion report for details
-   - Download the converted Streamlit app
-
-### Advanced Settings
-
-- **Conversion Strategy**: Choose how the code is analyzed and converted
-- **Large File Threshold**: Files above this size (in KB) use optimized processing
-- **Main Guard**: Adds `if __name__ == '__main__':` wrapper for safer execution
-- **Preserve Comments**: Keep all comments and docstrings in the output
-
-## 🎯 Use Cases
-
-- **Data Science Projects**: Convert Jupyter notebooks with visualizations to interactive Streamlit dashboards
-- **Script Migration**: Transform existing Python scripts into web applications
-- **Batch Conversion**: Process entire project folders at once
-- **Prototyping**: Quickly create Streamlit apps from existing code
-
-## 🔧 Technical Details
-
-### Architecture
-
-- **AST-Based Transformation**: Uses Python's `ast` module for structural analysis
-- **Regex Fallback**: Pattern matching for edge cases and large files
-- **Hybrid Approach**: Combines both methods for optimal results
-- **Error Recovery**: Graceful fallbacks when parsing fails
-
-### Performance
-
-- Handles files up to 5MB+ efficiently
-- Chunked processing for large files
-- Caching for repeated conversions
-- Memory-efficient algorithms
-
-### Limitations
-
-- Does not execute code (safe conversion only)
-- Complex interactive widgets (e.g., `ipywidgets`) require manual conversion
-- Some edge cases in very complex code may need manual adjustment
-- Manual review recommended before production deployment
-
-## 📝 Example
-
-### Input (Jupyter Notebook Cell):
-```python
-import pandas as pd
-import matplotlib.pyplot as plt
-
-df = pd.read_csv('data.csv')
-print(f"Dataset has {len(df)} rows")
-display(df.head())
-
-plt.figure(figsize=(10, 6))
-plt.plot(df['x'], df['y'])
-plt.show()
-```
-
-### Output (Streamlit App):
-```python
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-
-st.set_page_config(
-    page_title='Converted App',
-    layout='wide'
-)
-
-st.title(' Converted Streamlit App')
-
-df = pd.read_csv('data.csv')
-st.write(f"Dataset has {len(df)} rows")
-st.dataframe(df.head())
-
-plt.figure(figsize=(10, 6))
-plt.plot(df['x'], df['y'])
-st.pyplot(plt.gcf())
-```
-
-## 🛠️ Development
-
-### Project Structure
-```
-python_to_full_streamlit/
-├── app.py              # Main Streamlit application
-├── Requirements.txt    # Python dependencies
-└── README.md          # This file
-```
-
-### Key Components
-
-1. **HybridConverter**: Main conversion engine with multiple strategies
-2. **CommentPreservingTransformer**: AST transformer that preserves code structure
-3. **extract_code_from_notebook**: Enhanced notebook processing with markdown support
-4. **File Processing**: Cached, efficient file handling with error recovery
-
-## 🤝 Contributing
-
-This is a production-ready converter. Improvements welcome for:
-- Additional conversion patterns
-- Performance optimizations
-- Edge case handling
-- UI/UX enhancements
-
-## 📄 License
-
-This project is provided as-is for converting Python code to Streamlit applications.
-
-## 💡 Tips
-
-- Use **Hybrid mode** for best results on most files
-- Enable **comment preservation** to maintain documentation
-- For very large files (>1MB), consider using **Regex mode**
-- Always review converted code before deployment
-- Test the generated Streamlit app with sample data
-
-## 🐛 Troubleshooting
-
-**Issue**: Conversion fails on a file
-- **Solution**: Try a different conversion mode (AST vs Regex)
-- Check if the file has syntax errors
-- Verify the file is valid Python/Jupyter format
-
-**Issue**: Comments are missing
-- **Solution**: Enable "Preserve Comments" in advanced settings
-- Use AST or Hybrid mode instead of Regex
-
-**Issue**: Large file processing is slow
-- **Solution**: Increase the large file threshold
-- Use Regex mode for very large files
-- Process files individually instead of in ZIP
-
----
-
-**Made with ❤️ for the Streamlit community**
-
+---
+license: mit
+title: python_to_streamlit_convertor
+sdk: streamlit
+emoji: 📊
+colorFrom: green
+colorTo: gray
+---
+#  Python → Streamlit Converter Pro
+
+A powerful, production-ready tool that converts Python scripts and Jupyter Notebooks into fully functional Streamlit applications. Handles large files, preserves comments and markdown, and provides multiple conversion strategies.
+
+## ✨ Features
+
+### Core Capabilities
+- **Multi-Format Support**: Convert `.py` files and `.ipynb` notebooks
+- **Large File Handling**: Efficiently processes files up to 5MB+ with optimized algorithms
+- **Comment Preservation**: All comments, docstrings, and inline documentation are preserved
+- **Markdown Support**: Notebook markdown cells are converted to commented Python code
+- **Batch Processing**: Upload ZIP archives to convert multiple files at once
+
+### Conversion Strategies
+
+1. **Hybrid Mode (Recommended)**: Combines AST parsing with regex patterns
+   - Best balance of accuracy and performance
+   - Handles complex code structures
+   - Preserves formatting and comments
+
+2. **AST Mode (Precise)**: Pure abstract syntax tree transformation
+   - Deep understanding of code structure
+   - Best for complex transformations
+   - Preserves all code semantics
+
+3. **Regex Mode (Fast)**: Pattern-based matching
+   - Fastest for very large files
+   - Good for simple conversions
+   - Efficient memory usage
+
+4. **Auto Mode**: Automatically selects the best strategy based on file size
+
+### What Gets Converted?
+
+| Original Code | Streamlit Equivalent |
+|--------------|---------------------|
+| `print(x)` | `st.write(x)` |
+| `display(df)` | `st.dataframe(df)` |
+| `df.head()` / `df.tail()` | `st.dataframe(df.head())` |
+| `plt.show()` | `st.pyplot(plt.gcf())` |
+| `fig.show()` (Plotly) | `st.plotly_chart(fig)` |
+| Markdown cells | Commented markdown |
+| All comments | Preserved |
+
+## 🚀 Installation
+
+1. Clone or download this repository
+2. Install dependencies:
+
+```bash
+pip install -r Requirements.txt
+```
+
+## 📖 Usage
+
+### Running the Application
+
+```bash
+streamlit run app.py
+```
+
+The application will open in your default web browser.
+
+### Basic Workflow
+
+1. **Upload Files**: Use the sidebar to upload Python files or Jupyter notebooks
+   - Individual files: Upload one or more files directly
+   - ZIP archive: Upload a ZIP containing multiple files
+
+2. **Configure Settings** (Optional):
+   - Choose conversion strategy (Hybrid recommended)
+   - Set large file threshold
+   - Enable/disable main guard
+   - Toggle comment preservation
+
+3. **Review & Download**: 
+   - View original and converted code side-by-side
+   - Check conversion report for details
+   - Download the converted Streamlit app
+
+### Advanced Settings
+
+- **Conversion Strategy**: Choose how the code is analyzed and converted
+- **Large File Threshold**: Files above this size (in KB) use optimized processing
+- **Main Guard**: Adds `if __name__ == '__main__':` wrapper for safer execution
+- **Preserve Comments**: Keep all comments and docstrings in the output
+
+## 🎯 Use Cases
+
+- **Data Science Projects**: Convert Jupyter notebooks with visualizations to interactive Streamlit dashboards
+- **Script Migration**: Transform existing Python scripts into web applications
+- **Batch Conversion**: Process entire project folders at once
+- **Prototyping**: Quickly create Streamlit apps from existing code
+
+## 🔧 Technical Details
+
+### Architecture
+
+- **AST-Based Transformation**: Uses Python's `ast` module for structural analysis
+- **Regex Fallback**: Pattern matching for edge cases and large files
+- **Hybrid Approach**: Combines both methods for optimal results
+- **Error Recovery**: Graceful fallbacks when parsing fails
+
+### Performance
+
+- Handles files up to 5MB+ efficiently
+- Chunked processing for large files
+- Caching for repeated conversions
+- Memory-efficient algorithms
+
+### Limitations
+
+- Does not execute code (safe conversion only)
+- Complex interactive widgets (e.g., `ipywidgets`) require manual conversion
+- Some edge cases in very complex code may need manual adjustment
+- Manual review recommended before production deployment
+
+## 📝 Example
+
+### Input (Jupyter Notebook Cell):
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+
+df = pd.read_csv('data.csv')
+print(f"Dataset has {len(df)} rows")
+display(df.head())
+
+plt.figure(figsize=(10, 6))
+plt.plot(df['x'], df['y'])
+plt.show()
+```
+
+### Output (Streamlit App):
+```python
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+st.set_page_config(
+    page_title='Converted App',
+    layout='wide'
+)
+
+st.title(' Converted Streamlit App')
+
+df = pd.read_csv('data.csv')
+st.write(f"Dataset has {len(df)} rows")
+st.dataframe(df.head())
+
+plt.figure(figsize=(10, 6))
+plt.plot(df['x'], df['y'])
+st.pyplot(plt.gcf())
+```
+
+## 🛠️ Development
+
+### Project Structure
+```
+python_to_full_streamlit/
+├── app.py              # Main Streamlit application
+├── Requirements.txt    # Python dependencies
+└── README.md          # This file
+```
+
+### Key Components
+
+1. **HybridConverter**: Main conversion engine with multiple strategies
+2. **CommentPreservingTransformer**: AST transformer that preserves code structure
+3. **extract_code_from_notebook**: Enhanced notebook processing with markdown support
+4. **File Processing**: Cached, efficient file handling with error recovery
+
+## 🤝 Contributing
+
+This is a production-ready converter. Improvements welcome for:
+- Additional conversion patterns
+- Performance optimizations
+- Edge case handling
+- UI/UX enhancements
+
+## 📄 License
+
+This project is provided as-is for converting Python code to Streamlit applications.
+
+## 💡 Tips
+
+- Use **Hybrid mode** for best results on most files
+- Enable **comment preservation** to maintain documentation
+- For very large files (>1MB), consider using **Regex mode**
+- Always review converted code before deployment
+- Test the generated Streamlit app with sample data
+
+## 🐛 Troubleshooting
+
+**Issue**: Conversion fails on a file
+- **Solution**: Try a different conversion mode (AST vs Regex)
+- Check if the file has syntax errors
+- Verify the file is valid Python/Jupyter format
+
+**Issue**: Comments are missing
+- **Solution**: Enable "Preserve Comments" in advanced settings
+- Use AST or Hybrid mode instead of Regex
+
+**Issue**: Large file processing is slow
+- **Solution**: Increase the large file threshold
+- Use Regex mode for very large files
+- Process files individually instead of in ZIP
+
+---
+
+**Made with ❤️ for the Streamlit community**