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README.md

@@ -1,19 +1,212 @@
----
-title: Python To Streamlit Convertor
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
----
-
-# Welcome to Streamlit!
-
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
-
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+#  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**
+

Requirements.txt

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+streamlit>=1.28.0
+nbformat>=5.9.0
+astor>=0.8.1

app.py

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+import streamlit as st
+import nbformat
+import ast
+import re
+import zipfile
+import io
+import hashlib
+from pathlib import Path
+from typing import List, Tuple, Dict, Any, Optional, Set
+from datetime import datetime
+
+# ==============================
+# SESSION STATE INITIALIZATION
+# ==============================
+def init_session_state():
+    """Initialize all session state variables."""
+    defaults = {
+        'conversion_results': {},
+        'uploaded_files': [],
+        'uploaded_zip': None,
+        'theme': "light",
+        'conversion_mode': "Hybrid (Recommended)",
+        'large_file_threshold': 200,
+        'add_main_guard': False,
+        'preserve_comments': True,
+        'conversion_stats': {
+            'total_files': 0,
+            'successful': 0,
+            'failed': 0,
+            'total_conversions': 0
+        }
+    }
+    for key, value in defaults.items():
+        if key not in st.session_state:
+            st.session_state[key] = value
+
+init_session_state()
+
+# ==============================
+# PAGE CONFIG & ENHANCED THEME
+# ==============================
+st.set_page_config(
+    page_title="📊 Python → Streamlit Converter Pro",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded",
+    menu_items={
+        'Get Help': None,
+        'Report a bug': None,
+        'About': "Python to Streamlit Converter Pro - Transform your code into beautiful Streamlit apps!"
+    }
+)
+
+def apply_enhanced_theme():
+    """Apply enhanced theme with modern styling."""
+    is_dark = st.session_state.theme == "dark"
+    
+    # Color scheme
+    if is_dark:
+        bg_primary = "#0e1117"
+        bg_secondary = "#1e2127"
+        bg_tertiary = "#262730"
+        text_primary = "#fafafa"
+        text_secondary = "#d0d0d0"
+        accent = "#ff4b4b"
+        accent_hover = "#ff6b6b"
+        border = "#3a3d47"
+        success = "#00d4aa"
+        warning = "#ffa726"
+        card_bg = "#1a1d24"
+    else:
+        bg_primary = "#ffffff"
+        bg_secondary = "#f8f9fa"
+        bg_tertiary = "#e9ecef"
+        text_primary = "#1a1a1a"
+        text_secondary = "#4a4a4a"
+        accent = "#ff4b4b"
+        accent_hover = "#ff6b6b"
+        border = "#dee2e6"
+        success = "#00d4aa"
+        warning = "#ffa726"
+        card_bg = "#ffffff"
+    
+    st.markdown(f"""
+    <style>
+    /* Main App Styling */
+    .stApp {{
+        background: linear-gradient(135deg, {bg_primary} 0%, {bg_secondary} 100%);
+        color: {text_primary};
+    }}
+    
+    /* Typography */
+    h1, h2, h3, h4, h5, h6 {{
+        color: {text_primary} !important;
+        font-weight: 700;
+    }}
+    
+    /* Sidebar Styling */
+    [data-testid="stSidebar"] {{
+        background: {bg_secondary} !important;
+        border-right: 1px solid {border};
+    }}
+    
+    /* Cards and Containers */
+    .main-card {{
+        background: {card_bg};
+        border-radius: 12px;
+        padding: 24px;
+        margin: 16px 0;
+        border: 1px solid {border};
+        box-shadow: 0 2px 8px rgba(0,0,0,0.1);
+    }}
+    
+    .stat-card {{
+        background: linear-gradient(135deg, {accent}15 0%, {accent}05 100%);
+        border-radius: 10px;
+        padding: 20px;
+        border: 1px solid {accent}30;
+        text-align: center;
+    }}
+    
+    /* Buttons */
+    .stButton > button {{
+        border-radius: 8px;
+        font-weight: 600;
+        transition: all 0.3s ease;
+    }}
+    
+    .stButton > button:hover {{
+        transform: translateY(-2px);
+        box-shadow: 0 4px 12px rgba(0,0,0,0.15);
+    }}
+    
+    /* Expanders */
+    .streamlit-expanderHeader {{
+        background: {bg_tertiary} !important;
+        border-radius: 8px !important;
+        font-weight: 600;
+    }}
+    
+    /* Code Blocks */
+    .stCodeBlock {{
+        border-radius: 8px;
+        border: 1px solid {border};
+    }}
+    
+    /* Metrics */
+    [data-testid="stMetricValue"] {{
+        color: {accent} !important;
+        font-weight: 700;
+    }}
+    
+    /* Tabs */
+    .stTabs [data-baseweb="tab-list"] {{
+        gap: 8px;
+    }}
+    
+    .stTabs [data-baseweb="tab"] {{
+        border-radius: 8px 8px 0 0;
+        padding: 12px 24px;
+        font-weight: 600;
+    }}
+    
+    /* File Uploader */
+    [data-testid="stFileUploader"] {{
+        border: 2px dashed {border};
+        border-radius: 12px;
+        padding: 20px;
+        background: {bg_tertiary};
+    }}
+    
+    /* Progress Bar */
+    .stProgress > div > div > div {{
+        background: linear-gradient(90deg, {accent}, {accent_hover});
+    }}
+    
+    /* Custom Badge */
+    .custom-badge {{
+        display: inline-block;
+        padding: 4px 12px;
+        border-radius: 12px;
+        font-size: 0.85em;
+        font-weight: 600;
+        background: {accent}20;
+        color: {accent};
+        border: 1px solid {accent}40;
+    }}
+    
+    /* Hero Section */
+    .hero-section {{
+        background: linear-gradient(135deg, {accent}15 0%, {accent}05 100%);
+        border-radius: 16px;
+        padding: 40px;
+        margin: 20px 0;
+        text-align: center;
+        border: 1px solid {accent}30;
+    }}
+    
+    /* Info Boxes */
+    .info-box {{
+        background: {bg_tertiary};
+        border-left: 4px solid {accent};
+        border-radius: 4px;
+        padding: 16px;
+        margin: 12px 0;
+    }}
+    
+    /* Success Message */
+    .success-box {{
+        background: {success}15;
+        border-left: 4px solid {success};
+        border-radius: 4px;
+        padding: 16px;
+        margin: 12px 0;
+    }}
+    </style>
+    """, unsafe_allow_html=True)
+
+apply_enhanced_theme()
+
+# ==============================
+# UI COMPONENTS
+# ==============================
+
+def render_stat_card(title: str, value: str, icon: str = "📊"):
+    """Render a statistics card."""
+    st.markdown(f"""
+    <div class="stat-card">
+        <div style="font-size: 2em; margin-bottom: 8px;">{icon}</div>
+        <div style="font-size: 1.8em; font-weight: 700; color: {st.session_state.get('accent', '#ff4b4b')}; margin-bottom: 4px;">{value}</div>
+        <div style="color: {st.session_state.get('text_secondary', '#4a4a4a')}; font-size: 0.9em;">{title}</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+def render_badge(text: str, color: str = "#ff4b4b"):
+    """Render a custom badge."""
+    st.markdown(f'<span class="custom-badge" style="background: {color}20; color: {color}; border-color: {color}40;">{text}</span>', unsafe_allow_html=True)
+
+def render_hero_section():
+    """Render the hero section."""
+    st.markdown("""
+    <div class="hero-section">
+        <h1 style="font-size: 3em; margin-bottom: 16px;">🐍 Python → Streamlit Converter Pro</h1>
+        <p style="font-size: 1.2em; color: #666; margin-bottom: 24px;">
+            Transform your <strong>Jupyter Notebooks</strong> and <strong>Python scripts</strong> into beautiful, 
+            interactive <strong>Streamlit apps</strong> in seconds!
+        </p>
+        <div style="display: flex; gap: 12px; justify-content: center; flex-wrap: wrap;">
+            <span class="custom-badge">✨ Auto-Conversion</span>
+            <span class="custom-badge">📊 Handles Large Files</span>
+            <span class="custom-badge">💬 Preserves Comments</span>
+            <span class="custom-badge">📦 Batch Processing</span>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+# ==============================
+# ENHANCED SIDEBAR
+# ==============================
+with st.sidebar:
+    # Logo and Title
+    st.markdown("""
+    <div style="text-align: center; padding: 20px 0;">
+        <h1 style="font-size: 2.5em; margin: 0;">🐍</h1>
+        <h2 style="margin-top: 8px; font-size: 1.2em;">Converter Pro</h2>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    st.divider()
+    
+    # Quick Actions
+    st.markdown("### ⚡ Quick Actions")
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        theme_icon = "🌙" if st.session_state.theme == "light" else "☀️"
+        if st.button(theme_icon, use_container_width=True, help="Toggle theme"):
+            st.session_state.theme = "dark" if st.session_state.theme == "light" else "light"
+            st.rerun()
+    
+    with col2:
+        if (st.session_state.conversion_results or 
+            st.session_state.uploaded_files or 
+            st.session_state.uploaded_zip):
+            if st.button("🔄", use_container_width=True, help="Clear all"):
+                for key in list(st.session_state.keys()):
+                    del st.session_state[key]
+                init_session_state()
+                st.rerun()
+    
+    st.divider()
+    
+    # Upload Section
+    st.markdown("### 📥 Upload Files")
+    upload_method = st.radio(
+        "Upload Method",
+        ["📁 Individual Files", "📦 ZIP Archive"],
+        index=0,
+        help="Choose how to upload your files",
+        label_visibility="collapsed"
+    )
+    
+    if upload_method == "📁 Individual Files":
+        uploaded_files = st.file_uploader(
+            "Select Python or Notebook files",
+            type=["py", "ipynb"],
+            accept_multiple_files=True,
+            key="file_uploader",
+            help="Upload one or more .py or .ipynb files"
+        )
+        st.session_state.uploaded_files = uploaded_files if uploaded_files else []
+        st.session_state.uploaded_zip = None
+    else:
+        uploaded_zip = st.file_uploader(
+            "Upload ZIP archive",
+            type=["zip"],
+            key="zip_uploader",
+            help="Upload a ZIP file containing multiple Python/Notebook files"
+        )
+        st.session_state.uploaded_zip = uploaded_zip
+        st.session_state.uploaded_files = []
+    
+    st.divider()
+    
+    # Advanced Settings
+    with st.expander("🔧 Advanced Settings", expanded=False):
+        st.session_state.conversion_mode = st.selectbox(
+            "🧠 Conversion Strategy",
+            ["Hybrid (Recommended)", "Auto", "AST (Precise)", "Regex (Fast)"],
+            index=["Hybrid (Recommended)", "Auto", "AST (Precise)", "Regex (Fast)"].index(
+                st.session_state.conversion_mode
+            ) if st.session_state.conversion_mode in ["Hybrid (Recommended)", "Auto", "AST (Precise)", "Regex (Fast)"] else 0,
+            help="Hybrid combines AST and regex for best results"
+        )
+        
+        st.session_state.large_file_threshold = st.slider(
+            "📏 Large File Threshold (KB)",
+            min_value=50,
+            max_value=5000,
+            value=st.session_state.large_file_threshold,
+            help="Files larger than this use optimized processing",
+            step=50
+        )
+        
+        st.session_state.add_main_guard = st.checkbox(
+            "🛡️ Add `if __name__ == '__main__':` guard",
+            value=st.session_state.add_main_guard,
+            help="Prevents execution issues when imported"
+        )
+        
+        st.session_state.preserve_comments = st.checkbox(
+            "💬 Preserve Comments & Docstrings",
+            value=st.session_state.preserve_comments,
+            help="Keep all comments and documentation"
+        )
+    
+    st.divider()
+    
+    # Sample Notebook Button
+    if st.button("🧪 Load Sample Notebook", use_container_width=True, type="secondary"):
+        sample_nb = get_sample_notebook()
+        sample_bytes = nbformat.writes(sample_nb).encode('utf-8')
+        st.session_state.uploaded_files = [io.BytesIO(sample_bytes)]
+        st.session_state.uploaded_files[0].name = "sample_notebook.ipynb"
+        st.rerun()
+    
+    st.divider()
+    
+    # Statistics
+    if st.session_state.conversion_stats['total_files'] > 0:
+        st.markdown("### 📊 Statistics")
+        stats = st.session_state.conversion_stats
+        st.metric("Total Files", stats['total_files'])
+        st.metric("Successful", stats['successful'], delta=f"{stats['total_conversions']} conversions")
+        if stats['failed'] > 0:
+            st.metric("Failed", stats['failed'], delta_color="inverse")
+
+# ==============================
+# SAMPLE FILE GENERATOR
+# ==============================
+@st.cache_data
+def get_sample_notebook():
+    """Generate a sample notebook for testing."""
+    nb = nbformat.v4.new_notebook()
+    nb.cells = [
+        nbformat.v4.new_markdown_cell("# Data Analysis Sample\n\nThis notebook demonstrates data visualization."),
+        nbformat.v4.new_code_cell("import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\n\n# Load sample data\ndf = pd.DataFrame({'x': np.random.randn(100), 'y': np.random.randn(100)})"),
+        nbformat.v4.new_code_cell("print('Dataset shape:', df.shape)\nprint(f'Total rows: {len(df)}')"),
+        nbformat.v4.new_code_cell("display(df.head())\ndisplay(df.describe())"),
+        nbformat.v4.new_code_cell("plt.figure(figsize=(8,5))\nsns.scatterplot(data=df, x='x', y='y')\nplt.title('Scatter Plot')\nplt.show()"),
+        nbformat.v4.new_code_cell("import plotly.express as px\nfig = px.scatter(df, x='x', y='y', title='Plotly Scatter')\nfig.show()")
+    ]
+    return nb
+
+# ==============================
+# ENHANCED CONVERTER CORE
+# ==============================
+
+class CommentPreservingTransformer(ast.NodeTransformer):
+    """Enhanced AST transformer that preserves comments and handles more patterns."""
+    def __init__(self, source_lines: List[str]):
+        self.source_lines = source_lines
+        self.conversion_log = []
+        self.imports_needed = set()
+        self.line_comments = {}
+        
+    def visit_Expr(self, node):
+        """Transform expression statements like print, display, plt.show, etc."""
+        if isinstance(node.value, ast.Call):
+            call = node.value
+            
+            if self._is_print_call(call):
+                self.conversion_log.append("Converted print() → st.write()")
+                self.imports_needed.add("import streamlit as st")
+                call.func = ast.Attribute(
+                    value=ast.Name(id='st', ctx=ast.Load()),
+                    attr='write',
+                    ctx=ast.Load()
+                )
+                return node
+            
+            elif self._is_display_call(call):
+                self.conversion_log.append("Converted display() → st.dataframe()")
+                self.imports_needed.add("import streamlit as st")
+                call.func = ast.Attribute(
+                    value=ast.Name(id='st', ctx=ast.Load()),
+                    attr='dataframe',
+                    ctx=ast.Load()
+                )
+                return node
+            
+            elif self._is_plt_show_call(call):
+                self.conversion_log.append("Converted plt.show() → st.pyplot()")
+                self.imports_needed.add("import streamlit as st")
+                self.imports_needed.add("import matplotlib.pyplot as plt")
+                return ast.Expr(
+                    value=ast.Call(
+                        func=ast.Attribute(
+                            value=ast.Name(id='st', ctx=ast.Load()),
+                            attr='pyplot',
+                            ctx=ast.Load()
+                        ),
+                        args=[ast.Call(
+                            func=ast.Attribute(
+                                value=ast.Name(id='plt', ctx=ast.Load()),
+                                attr='gcf',
+                                ctx=ast.Load()
+                            ),
+                            args=[], keywords=[]
+                        )],
+                        keywords=[]
+                    )
+                )
+            
+            elif self._is_plotly_show_call(call):
+                var_name = self._get_call_attr_name(call)
+                if var_name:
+                    self.conversion_log.append(f"Converted {var_name}.show() → st.plotly_chart()")
+                    self.imports_needed.add("import streamlit as st")
+                    return ast.Expr(
+                        value=ast.Call(
+                            func=ast.Attribute(
+                                value=ast.Name(id='st', ctx=ast.Load()),
+                                attr='plotly_chart',
+                                ctx=ast.Load()
+                            ),
+                            args=[ast.Name(id=var_name, ctx=ast.Load())],
+                            keywords=[]
+                        )
+                    )
+        
+        return self.generic_visit(node)
+    
+    def visit_Call(self, node):
+        """Handle method calls like df.head(), df.tail(), etc."""
+        if isinstance(node.func, ast.Attribute):
+            attr_name = node.func.attr
+            
+            if attr_name in ('head', 'tail') and isinstance(node.func.value, (ast.Name, ast.Attribute)):
+                parent = getattr(node, '_parent', None)
+                if parent is None or isinstance(parent, ast.Expr):
+                    self.conversion_log.append(f"Wrapped {attr_name}() → st.dataframe()")
+                    self.imports_needed.add("import streamlit as st")
+                    return ast.Call(
+                        func=ast.Attribute(
+                            value=ast.Name(id='st', ctx=ast.Load()),
+                            attr='dataframe',
+                            ctx=ast.Load()
+                        ),
+                        args=[node],
+                        keywords=[]
+                    )
+        
+        return self.generic_visit(node)
+    
+    def _is_print_call(self, call):
+        return (isinstance(call.func, ast.Name) and call.func.id == 'print')
+    
+    def _is_display_call(self, call):
+        return (isinstance(call.func, ast.Name) and call.func.id == 'display')
+    
+    def _is_plt_show_call(self, call):
+        return (isinstance(call.func, ast.Attribute) and
+                isinstance(call.func.value, ast.Name) and
+                call.func.value.id == 'plt' and
+                call.func.attr == 'show')
+    
+    def _is_plotly_show_call(self, call):
+        return (isinstance(call.func, ast.Attribute) and
+                call.func.attr == 'show')
+    
+    def _get_call_attr_name(self, call):
+        if isinstance(call.func, ast.Attribute):
+            if isinstance(call.func.value, ast.Name):
+                return call.func.value.id
+        return None
+
+
+class HybridConverter:
+    """Hybrid converter that combines AST parsing with regex for comprehensive conversion."""
+    def __init__(self, code: str, filename: str = "unknown", 
+                 conversion_mode: str = "hybrid", large_file_threshold: int = 200,
+                 add_main_guard: bool = False, preserve_comments: bool = True):
+        self.original_code = code
+        self.filename = filename
+        self.conversion_mode = conversion_mode.lower()
+        self.large_file_threshold = large_file_threshold * 1024
+        self.add_main_guard = add_main_guard
+        self.preserve_comments = preserve_comments
+        self.conversion_report = []
+        self.imports_needed = set()
+        self.source_lines = code.splitlines(keepends=True)
+        
+    def convert(self) -> str:
+        """Main conversion entry point."""
+        file_size = len(self.original_code.encode('utf-8'))
+        is_large = file_size > self.large_file_threshold
+        
+        if self.conversion_mode == "regex (fast)":
+            self.conversion_report.append("✅ Using fast regex-based conversion")
+            return self._regex_convert()
+        elif self.conversion_mode == "ast (precise)":
+            self.conversion_report.append("✅ Using precise AST-based conversion")
+            return self._ast_convert()
+        elif "hybrid" in self.conversion_mode:
+            self.conversion_report.append("✅ Using hybrid conversion (AST + Regex)")
+            return self._hybrid_convert()
+        else:  # Auto mode
+            if is_large:
+                self.conversion_report.append(f"✅ Large file detected ({file_size/1024:.1f}KB), using hybrid mode")
+                return self._hybrid_convert()
+            else:
+                self.conversion_report.append("✅ Using AST-based conversion")
+                return self._ast_convert()
+    
+    def _hybrid_convert(self) -> str:
+        """Hybrid approach: AST for structure, regex for patterns."""
+        try:
+            code = self._ast_convert_core()
+            code = self._apply_regex_patterns(code)
+            self._detect_needed_imports(self.original_code)
+            return self._add_streamlit_boilerplate(code)
+        except Exception as e:
+            self.conversion_report.append(f"⚠️ Hybrid conversion issue: {e}, falling back to regex")
+            return self._regex_convert()
+    
+    def _ast_convert(self) -> str:
+        """Pure AST-based conversion."""
+        try:
+            code = self._ast_convert_core()
+            self._detect_needed_imports(self.original_code)
+            return self._add_streamlit_boilerplate(code)
+        except Exception as e:
+            self.conversion_report.append(f"⚠️ AST conversion failed: {e}, falling back to regex")
+            return self._regex_convert()
+    
+    def _ast_convert_core(self) -> str:
+        """Core AST conversion logic."""
+        try:
+            tree = ast.parse(self.original_code, filename=self.filename)
+            transformer = CommentPreservingTransformer(self.source_lines)
+            transformed_tree = transformer.visit(tree)
+            ast.fix_missing_locations(transformed_tree)
+            
+            self.imports_needed.update(transformer.imports_needed)
+            self.conversion_report.extend(transformer.conversion_log)
+            
+            try:
+                return ast.unparse(transformed_tree)
+            except AttributeError:
+                return self._ast_to_source(transformed_tree)
+        except SyntaxError as e:
+            self.conversion_report.append(f"⚠️ Syntax error: {e}, using line-by-line fallback")
+            return self._line_by_line_fallback()
+        except Exception as e:
+            self.conversion_report.append(f"⚠️ AST parsing error: {e}")
+            raise
+    
+    def _ast_to_source(self, node) -> str:
+        """Custom AST to source converter for Python < 3.9."""
+        try:
+            import astor
+            return astor.to_source(node)
+        except ImportError:
+            self.conversion_report.append("⚠️ Python < 3.9 detected, using regex fallback")
+            return self._regex_convert()
+    
+    def _apply_regex_patterns(self, code: str) -> str:
+        """Apply regex patterns for additional conversions."""
+        lines = code.splitlines(keepends=True)
+        new_lines = []
+        
+        for line in lines:
+            stripped = line.strip()
+            
+            if stripped.startswith("%") or stripped.startswith("!"):
+                continue
+            
+            if re.search(r'^\s*([a-zA-Z_]\w*\.(?:head|tail)\([^)]*\))\s*$', stripped):
+                match = re.search(r'([a-zA-Z_]\w*\.(?:head|tail)\([^)]*\))', stripped)
+                if match:
+                    indent = len(line) - len(line.lstrip())
+                    new_lines.append(' ' * indent + f"st.dataframe({match.group(1)})\n")
+                    self.conversion_report.append("✅ Wrapped DataFrame method → st.dataframe()")
+                    continue
+            
+            if re.search(r'\.show\(\)', stripped) and ('sns.' in stripped or 'seaborn' in stripped):
+                var_match = re.search(r'([a-zA-Z_]\w*)\.show\(\)', stripped)
+                if var_match:
+                    indent = len(line) - len(line.lstrip())
+                    new_lines.append(' ' * indent + f"st.pyplot({var_match.group(1)})\n")
+                    self.conversion_report.append("✅ Converted seaborn plot → st.pyplot()")
+                    continue
+            
+            new_lines.append(line)
+        
+        return "".join(new_lines)
+    
+    def _regex_convert(self) -> str:
+        """Regex-based conversion for large files or fallback."""
+        lines = self.source_lines
+        new_lines = []
+        in_multiline_string = False
+        
+        i = 0
+        while i < len(lines):
+            line = lines[i]
+            stripped = line.strip()
+            
+            if stripped.startswith("%") or stripped.startswith("!"):
+                i += 1
+                continue
+            
+            if '"""' in line or "'''" in line:
+                triple_quotes = '"""' if '"""' in line else "'''"
+                count = line.count(triple_quotes)
+                if count % 2 == 1:
+                    in_multiline_string = not in_multiline_string
+                new_lines.append(line)
+                i += 1
+                continue
+            
+            if in_multiline_string:
+                new_lines.append(line)
+                i += 1
+                continue
+            
+            if re.match(r'^\s*print\s*\(', stripped):
+                new_line = re.sub(r'\bprint\s*\(', 'st.write(', line, count=1)
+                new_lines.append(new_line)
+                self.conversion_report.append("✅ Replaced print() → st.write()")
+                self.imports_needed.add("import streamlit as st")
+                i += 1
+                continue
+            
+            if re.match(r'^\s*display\s*\(', stripped):
+                new_line = re.sub(r'\bdisplay\s*\(', 'st.dataframe(', line, count=1)
+                new_lines.append(new_line)
+                self.conversion_report.append("✅ Replaced display() → st.dataframe()")
+                self.imports_needed.add("import streamlit as st")
+                i += 1
+                continue
+            
+            if re.match(r'^\s*plt\.show\s*\(\s*\)', stripped):
+                indent = len(line) - len(line.lstrip())
+                new_lines.append(' ' * indent + "st.pyplot(plt.gcf())\n")
+                self.conversion_report.append("✅ Replaced plt.show() → st.pyplot()")
+                self.imports_needed.add("import streamlit as st")
+                self.imports_needed.add("import matplotlib.pyplot as plt")
+                i += 1
+                continue
+            
+            if re.match(r'^\s*[a-zA-Z_]\w*\.show\s*\(\s*\)', stripped):
+                match = re.search(r'([a-zA-Z_]\w*)\.show\s*\(\s*\)', stripped)
+                if match:
+                    var_name = match.group(1)
+                    indent = len(line) - len(line.lstrip())
+                    new_lines.append(' ' * indent + f"st.plotly_chart({var_name})\n")
+                    self.conversion_report.append(f"✅ Replaced {var_name}.show() → st.plotly_chart()")
+                    self.imports_needed.add("import streamlit as st")
+                    i += 1
+                    continue
+            
+            if re.match(r'^\s*[a-zA-Z_]\w*\.(?:head|tail)\s*\([^)]*\)\s*$', stripped):
+                match = re.search(r'([a-zA-Z_]\w*\.(?:head|tail)\s*\([^)]*\))', stripped)
+                if match:
+                    indent = len(line) - len(line.lstrip())
+                    new_lines.append(' ' * indent + f"st.dataframe({match.group(1)})\n")
+                    self.conversion_report.append("✅ Wrapped DataFrame method → st.dataframe()")
+                    self.imports_needed.add("import streamlit as st")
+                    i += 1
+                    continue
+            
+            new_lines.append(line)
+            i += 1
+        
+        self._detect_needed_imports(self.original_code)
+        return self._add_streamlit_boilerplate("".join(new_lines))
+    
+    def _line_by_line_fallback(self) -> str:
+        """Fallback for when AST parsing fails."""
+        return self._regex_convert()
+    
+    def _detect_needed_imports(self, code: str):
+        """Detect which imports are needed based on code content."""
+        code_lower = code.lower()
+        self.imports_needed.add("import streamlit as st")
+        
+        if 'plt.' in code or 'matplotlib' in code_lower or 'pyplot' in code_lower:
+            self.imports_needed.add("import matplotlib.pyplot as plt")
+        if 'sns.' in code or 'seaborn' in code_lower:
+            self.imports_needed.add("import seaborn as sns")
+        if 'px.' in code or 'go.' in code or 'plotly' in code_lower:
+            self.imports_needed.add("import plotly.express as px")
+            self.imports_needed.add("import plotly.graph_objects as go")
+        if 'pd.' in code or 'pandas' in code_lower or 'dataframe' in code_lower:
+            self.imports_needed.add("import pandas as pd")
+        if 'np.' in code or 'numpy' in code_lower:
+            self.imports_needed.add("import numpy as np")
+    
+    def _add_streamlit_boilerplate(self, code: str) -> str:
+        """Add Streamlit boilerplate and imports."""
+        imports = sorted(list(self.imports_needed))
+        
+        existing_imports = []
+        code_lines = code.splitlines()
+        for line in code_lines[:20]:
+            if line.strip().startswith('import ') or line.strip().startswith('from '):
+                existing_imports.append(line.strip())
+        
+        filtered_imports = []
+        for imp in imports:
+            imp_name = imp.split()[1].split('.')[0] if 'import' in imp else None
+            if imp_name:
+                if not any(imp_name in existing for existing in existing_imports):
+                    filtered_imports.append(imp)
+            else:
+                filtered_imports.append(imp)
+        
+        boilerplate = [
+            "# ==============================",
+            "# AUTO-GENERATED STREAMLIT APP",
+            f"# Source: {self.filename}",
+            "# Converted with Python → Streamlit Converter Pro",
+            "# ==============================\n",
+            *filtered_imports,
+            "",
+            "st.set_page_config(",
+            "    page_title='Converted App',",
+            "    layout='wide'",
+            ")\n",
+            "st.title('🐍 Converted Streamlit App')",
+            f"st.caption(f'_Converted from: {self.filename}_')\n",
+            "st.divider()\n"
+        ]
+        
+        if self.add_main_guard:
+            indented_code = "\n".join("    " + line if line.strip() else line 
+                                     for line in code.splitlines())
+            return "\n".join(boilerplate) + "\nif __name__ == '__main__':\n" + indented_code
+        else:
+            return "\n".join(boilerplate) + code
+    
+    def get_conversion_report(self) -> List[str]:
+        """Get the conversion report."""
+        if not self.conversion_report:
+            return ["ℹ️ No transformations applied (code may already be Streamlit-compatible)"]
+        seen = set()
+        unique_report = []
+        for item in self.conversion_report:
+            if item not in seen:
+                seen.add(item)
+                unique_report.append(item)
+        return unique_report
+
+
+# ==============================
+# NOTEBOOK PROCESSING
+# ==============================
+
+def extract_code_from_notebook(notebook_content: str, preserve_markdown: bool = True) -> str:
+    """Convert notebook to Python script with enhanced markdown handling."""
+    try:
+        nb = nbformat.reads(notebook_content, as_version=4)
+    except Exception as e:
+        raise ValueError(f"Invalid notebook format: {e}")
+    
+    lines = []
+    cell_num = 0
+    
+    for cell in nb.cells:
+        cell_num += 1
+        
+        if cell.cell_type == "markdown" and preserve_markdown:
+            md_content = cell.source
+            lines.append(f"\n# {'='*60}")
+            lines.append(f"# MARKDOWN CELL {cell_num}")
+            lines.append(f"# {'='*60}")
+            
+            for md_line in md_content.split('\n'):
+                if not md_line.strip():
+                    lines.append("#")
+                else:
+                    clean_line = md_line.replace('"""', "'''").replace("'''", '"""')
+                    if md_line.strip().startswith('#'):
+                        lines.append(f"# {clean_line}")
+                    else:
+                        lines.append(f"# {clean_line}")
+            lines.append("")
+            
+        elif cell.cell_type == "code":
+            code_content = cell.source
+            
+            if lines and lines[-1].strip():
+                lines.append("")
+            
+            if hasattr(cell, 'metadata') and cell.metadata:
+                lines.append(f"# Cell {cell_num} metadata: {cell.metadata}")
+            
+            lines.append(code_content)
+            
+            if not code_content.endswith('\n'):
+                lines.append("")
+    
+    result = "\n".join(lines)
+    if not result.endswith('\n'):
+        result += "\n"
+    
+    return result
+
+
+# ==============================
+# FILE PROCESSING UTILITIES
+# ==============================
+
+@st.cache_data(show_spinner=False)
+def process_single_file_cached(file_bytes: bytes, filename: str, file_hash: str, 
+                              conversion_mode: str, large_file_threshold: int, 
+                              add_main_guard: bool, preserve_comments: bool):
+    """Cached file processing function."""
+    file_extension = Path(filename).suffix.lower()
+    
+    try:
+        if file_extension == ".ipynb":
+            original_code = extract_code_from_notebook(file_bytes.decode('utf-8'), 
+                                                      preserve_markdown=preserve_comments)
+        else:
+            original_code = file_bytes.decode("utf-8")
+        
+        converter = HybridConverter(
+            original_code, 
+            filename, 
+            conversion_mode=conversion_mode,
+            large_file_threshold=large_file_threshold,
+            add_main_guard=add_main_guard,
+            preserve_comments=preserve_comments
+        )
+        streamlit_code = converter.convert()
+        
+        return streamlit_code, original_code, converter.get_conversion_report()
+    
+    except Exception as e:
+        error_msg = f"Error processing {filename}: {str(e)}"
+        return f"# {error_msg}\n# Original file could not be processed.", "", [f"❌ {error_msg}"]
+
+def process_single_file(uploaded_file, **kwargs):
+    """Process a single uploaded file."""
+    file_bytes = uploaded_file.getvalue()
+    file_hash = hashlib.md5(file_bytes).hexdigest()[:8]
+    return process_single_file_cached(
+        file_bytes, uploaded_file.name, file_hash, **kwargs
+    )
+
+
+# ==============================
+# MAIN APP UI
+# ==============================
+
+# Hero Section
+render_hero_section()
+
+# Main Tabs
+tab1, tab2, tab3 = st.tabs(["🚀 Convert Files", "📊 Dashboard", "ℹ️ How It Works"])
+
+with tab1:
+    if st.session_state.uploaded_files:
+        st.markdown(f"### 📄 Processing {len(st.session_state.uploaded_files)} File(s)")
+        
+        for idx, uploaded_file in enumerate(st.session_state.uploaded_files):
+            file_key = f"file_{idx}_{uploaded_file.name}"
+            
+            with st.container():
+                st.markdown(f"<div class='main-card'>", unsafe_allow_html=True)
+                
+                # File Header
+                col1, col2, col3 = st.columns([3, 1, 1])
+                with col1:
+                    st.markdown(f"#### 📑 {uploaded_file.name}")
+                    file_size = len(uploaded_file.getvalue())
+                    st.caption(f"Size: {file_size:,} bytes ({file_size/1024:.2f} KB)")
+                
+                with col2:
+                    file_ext = Path(uploaded_file.name).suffix
+                    render_badge(file_ext.upper().replace('.', ''), "#00d4aa")
+                
+                with col3:
+                    render_badge("Ready", "#ff4b4b")
+                
+                try:
+                    with st.spinner(f"🔄 Converting {uploaded_file.name}..."):
+                        streamlit_code, original_code, report = process_single_file(
+                            uploaded_file,
+                            conversion_mode=st.session_state.conversion_mode.lower(),
+                            large_file_threshold=st.session_state.large_file_threshold,
+                            add_main_guard=st.session_state.add_main_guard,
+                            preserve_comments=st.session_state.preserve_comments
+                        )
+                    
+                    # Update stats
+                    st.session_state.conversion_stats['total_files'] += 1
+                    st.session_state.conversion_stats['successful'] += 1
+                    st.session_state.conversion_stats['total_conversions'] += len([r for r in report if '✅' in r])
+                    
+                    st.success(f"✅ Successfully converted {uploaded_file.name}!")
+                    
+                    # View Mode Selector
+                    view_mode = st.radio(
+                        "👁️ View Mode",
+                        ["🔁 Side-by-Side", "📜 Original Only", "✨ Converted Only"],
+                        index=0,
+                        horizontal=True,
+                        key=f"view_{file_key}"
+                    )
+
+                    # Code Display
+                    if "Side-by-Side" in view_mode:
+                        col1, col2 = st.columns(2)
+                        with col1:
+                            st.markdown("##### 📜 Original Code")
+                            display_original = original_code[:20000] + ("..." if len(original_code) > 20000 else "")
+                            st.code(display_original, language="python", line_numbers=True)
+                        with col2:
+                            st.markdown("##### ✨ Converted Streamlit App")
+                            display_converted = streamlit_code[:20000] + ("..." if len(streamlit_code) > 20000 else "")
+                            st.code(display_converted, language="python", line_numbers=True)
+                    elif "Original Only" in view_mode:
+                        st.markdown("##### 📜 Original Code")
+                        display_original = original_code[:30000] + ("..." if len(original_code) > 30000 else "")
+                        st.code(display_original, language="python", line_numbers=True)
+                    else:
+                        st.markdown("##### ✨ Converted Streamlit App")
+                        display_converted = streamlit_code[:30000] + ("..." if len(streamlit_code) > 30000 else "")
+                        st.code(display_converted, language="python", line_numbers=True)
+                    
+                    # File Metrics
+                    col1, col2, col3, col4 = st.columns(4)
+                    with col1:
+                        st.metric("Original Size", f"{len(original_code):,}", "chars")
+                    with col2:
+                        st.metric("Converted Size", f"{len(streamlit_code):,}", "chars")
+                    with col3:
+                        size_diff = len(streamlit_code) - len(original_code)
+                        st.metric("Size Change", f"{size_diff:+,}", "chars")
+                    with col4:
+                        st.metric("Conversions", len([r for r in report if '✅' in r]), "transformations")
+                    
+                    # Download Button
+                    st.download_button(
+                        f"⬇️ Download {Path(uploaded_file.name).stem}_streamlit.py",
+                        streamlit_code,
+                        file_name=f"{Path(uploaded_file.name).stem}_streamlit.py",
+                        mime="text/plain",
+                        key=f"dl_{file_key}",
+                        use_container_width=True,
+                        type="primary"
+                    )
+                    
+                    # Conversion Report
+                    with st.expander("📋 Conversion Report", expanded=False):
+                        for item in report:
+                            if '✅' in item:
+                                st.success(item)
+                            elif '⚠️' in item:
+                                st.warning(item)
+                            elif '❌' in item:
+                                st.error(item)
+                            else:
+                                st.info(item)
+                
+                except Exception as e:
+                    st.session_state.conversion_stats['total_files'] += 1
+                    st.session_state.conversion_stats['failed'] += 1
+                    st.error(f"❌ Failed to convert `{uploaded_file.name}`: {str(e)}")
+                    st.exception(e)
+                
+                st.markdown("</div>", unsafe_allow_html=True)
+                st.divider()
+
+    elif st.session_state.uploaded_zip:
+        st.markdown("### 📦 Processing ZIP Archive")
+        try:
+            with st.spinner("Extracting and converting files..."):
+                results = {}
+                with zipfile.ZipFile(st.session_state.uploaded_zip) as zip_ref:
+                    file_list = [f for f in zip_ref.namelist() if f.endswith(('.py', '.ipynb'))]
+                    if not file_list:
+                        st.warning("⚠️ No .py or .ipynb files found in ZIP!")
+                    else:
+                        progress_bar = st.progress(0)
+                        status_text = st.empty()
+                        
+                        for i, filename in enumerate(file_list):
+                            status_text.markdown(f"**Processing {i+1}/{len(file_list)}:** `{filename}`")
+                            with zip_ref.open(filename) as f:
+                                file_obj = io.BytesIO(f.read())
+                                file_obj.name = filename
+                                try:
+                                    code, orig, rep = process_single_file(
+                                        file_obj,
+                                        conversion_mode=st.session_state.conversion_mode.lower(),
+                                        large_file_threshold=st.session_state.large_file_threshold,
+                                        add_main_guard=st.session_state.add_main_guard,
+                                        preserve_comments=st.session_state.preserve_comments
+                                    )
+                                    results[filename] = (code, orig, rep)
+                                    st.session_state.conversion_stats['successful'] += 1
+                                except Exception as e:
+                                    results[filename] = (f"# Conversion failed: {str(e)}", "", [f"❌ Error: {str(e)}"])
+                                    st.session_state.conversion_stats['failed'] += 1
+                            progress_bar.progress((i + 1) / len(file_list))
+                            st.session_state.conversion_stats['total_files'] += 1
+                        
+                        status_text.empty()
+                        progress_bar.empty()
+                        
+                        # Create ZIP of results
+                        zip_buffer = io.BytesIO()
+                        with zipfile.ZipFile(zip_buffer, "w") as zf:
+                            for name, (code, _, _) in results.items():
+                                if not code.startswith("# Conversion failed"):
+                                    zf.writestr(f"{Path(name).stem}_streamlit.py", code)
+                        
+                        successful = len([r for r in results.values() if not r[0].startswith('# Conversion failed')])
+                        st.success(f"✅ Successfully converted {successful}/{len(file_list)} file(s)")
+                        
+                        # Download Button
+                        st.download_button(
+                            "⬇️ Download All Converted Apps (ZIP)",
+                            zip_buffer.getvalue(),
+                            "streamlit_converted_apps.zip",
+                            "application/zip",
+                            use_container_width=True,
+                            type="primary"
+                        )
+                        
+                        # Results Display
+                        for name, (code, orig, rep) in results.items():
+                            with st.expander(f"📄 {name}", expanded=False):
+                                if code.startswith("# Conversion failed"):
+                                    st.error(code)
+                                else:
+                                    view_mode = st.radio(
+                                        "👁️ View Mode",
+                                        ["🔁 Side-by-Side", "📜 Original Only", "✨ Converted Only"],
+                                        index=0,
+                                        horizontal=True,
+                                        key=f"view_{name}"
+                                    )
+
+                                    if "Side-by-Side" in view_mode:
+                                        col1, col2 = st.columns(2)
+                                        with col1:
+                                            st.markdown("##### 📜 Original")
+                                            st.code(orig[:5000] + ("..." if len(orig) > 5000 else ""), language="python")
+                                        with col2:
+                                            st.markdown("##### ✨ Converted")
+                                            st.code(code[:5000] + ("..." if len(code) > 5000 else ""), language="python")
+                                    elif "Original Only" in view_mode:
+                                        st.code(orig[:10000] + ("..." if len(orig) > 10000 else ""), language="python")
+                                    else:
+                                        st.code(code[:10000] + ("..." if len(code) > 10000 else ""), language="python")
+                                    
+                                    with st.expander("📋 Report"):
+                                        for r in rep:
+                                            if '✅' in r:
+                                                st.success(r)
+                                            elif '❌' in r:
+                                                st.error(r)
+                                            else:
+                                                st.info(r)
+        except Exception as e:
+            st.error(f"❌ ZIP processing failed: {str(e)}")
+            st.exception(e)
+    else:
+        st.info("""
+        👈 **Get Started:**
+        1. Upload files using the sidebar (or try the sample notebook)
+        2. Adjust settings if needed
+        3. View and download your converted Streamlit apps!
+        """)
+
+with tab2:
+    st.markdown("### 📊 Conversion Dashboard")
+    
+    stats = st.session_state.conversion_stats
+    
+    if stats['total_files'] > 0:
+        col1, col2, col3, col4 = st.columns(4)
+        with col1:
+            render_stat_card("Total Files", str(stats['total_files']), "📁")
+        with col2:
+            render_stat_card("Successful", str(stats['successful']), "✅")
+        with col3:
+            render_stat_card("Failed", str(stats['failed']), "❌")
+        with col4:
+            render_stat_card("Total Conversions", str(stats['total_conversions']), "🔄")
+        
+        # Success Rate
+        if stats['total_files'] > 0:
+            success_rate = (stats['successful'] / stats['total_files']) * 100
+            st.metric("Success Rate", f"{success_rate:.1f}%")
+    else:
+        st.info("📊 No conversions yet. Upload files to see statistics here!")
+
+with tab3:
+    st.markdown("### 📖 How It Works")
+    
+    st.markdown("""
+    <div class="info-box">
+    <h4>✨ Enhanced Conversion Engine</h4>
+    <p><strong>Hybrid Mode (Recommended)</strong>: Combines AST parsing for structure with regex for patterns.
+    - Preserves code structure and comments
+    - Handles large files efficiently
+    - Best balance of accuracy and performance</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    st.markdown("""
+    ### 🔄 Conversion Table
+    
+    | 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 |
+    """)
+    
+    st.markdown("""
+    ### 📦 Key Features
+    
+    - ✅ **Large File Support**: Handles files up to 5MB+ efficiently
+    - ✅ **Markdown Preservation**: Notebook markdown cells converted to comments
+    - ✅ **Comment Preservation**: All comments and docstrings maintained
+    - ✅ **ZIP Support**: Batch convert entire folders
+    - ✅ **Error Recovery**: Graceful fallbacks for malformed code
+    - ✅ **Import Detection**: Automatically adds required imports
+    - ✅ **Real-time Statistics**: Track your conversion progress
+    """)
+    
+    st.info("💡 **Pro Tip**: Use Hybrid mode for best results. It combines the accuracy of AST with the speed of regex!")