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README.md

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-# **Documentation for DataCoder AI**  
-**Version**: 1.0  
-**Last Updated**: 2024-06-15  
-
----
-
-## **1. Introduction**  
-**DataCoder AI** is a web-based interactive data analysis tool that combines:  
-- **Jupyter-like Python execution**  
-- **AI-powered code generation** (via Groq API)  
-- **Data visualization**  
-- **Chat-based coding assistance**  
-
-Built with `Streamlit`, `Pandas`, and `Plotly`, designed for **data scientists** and **analysts**.  
-Here are the **required libraries** for your DataCoder AI application, extracted from your code:
-
----
-
-### **Core Dependencies**  
-**Install via `pip install -r requirements.txt`:**
-```text
-streamlit==1.32.0        # Web UI framework
-pandas==2.0.0           # Data manipulation
-matplotlib==3.7.0       # Basic visualization
-seaborn==0.12.2         # Advanced plots
-plotly==5.18.0          # Interactive charts
-requests==2.31.0        # API calls (Groq)
-langchain==0.1.0        # LLM interface (if used)
-python-dotenv==1.0.0    # Environment variables
-```
-
-### **Optional/Implicit Dependencies**  
-```text
-numpy==1.24.0           # (Auto-installed with pandas)
-scipy==1.10.0           # (For stats operations)
-fuzzywuzzy==0.18.0      # (For column name matching - if used)
-```
-
----
-
-### **Notes**:  
-1. **Groq API**: No Python SDK required (uses raw `requests`).  
-2. **Virtual Environment**: Recommended to avoid conflicts:  
-   ```bash
-   python -m venv venv
-   source venv/bin/activate  # Linux/Mac
-   venv\Scripts\activate    # Windows
-   pip install -r requirements.txt
-   ```
-
-3. **Production Considerations**:  
-   - Add `gunicorn` for deployment:  
-     ```text
-     gunicorn==20.1.0
-     ```  
-   - For Windows, use `waitress`:  
-     ```text
-     waitress==2.1.2
-     ```
-
-Let me know if you need version adjustments or encounter dependency conflicts!
----
-
-## **2. System Architecture**  
-
-### **2.1 Component Diagram**  
-```mermaid
-flowchart TB
-    subgraph Frontend
-        A[Streamlit UI] --> B[File Uploader]
-        A --> C[Code Editor]
-        A --> D[Output Display]
-    end
-
-    subgraph Backend
-        B --> E[Data Processor]
-        C --> F[Code Executor]
-        C --> G[Groq LLM API]
-        F --> H[Session State]
-    end
-
-    subgraph External
-        G -->|API Calls| I[Groq Cloud]
-    end
-```
-
-### **2.2 Data Flow**  
-1. **Input**: CSV files → Pandas DataFrame  
-2. **Processing**: Merge/clean data → Modified DataFrame  
-3. **Execution**: Python code → Results (text/plots/variables)  
-4. **Output**: Visualizations + Export files  
-
----
-
-## **3. Installation**  
-
-### **3.1 Prerequisites**  
-- Python 3.8+  
-- `pip` package manager  
-
-### **3.2 Setup**  
-```bash
-# Clone repository
-git clone https://github.com/yourrepo/datacoder-ai.git
-cd datacoder-ai
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the app
-streamlit run app.py
-```
-
-### **3.3 Configuration**  
-Rename `.env.example` to `.env` and add your Groq API key:  
-```env
-GROQ_API_KEY=your_api_key_here
-```
-
----
-
-## **4. User Guide**  
-
-### **4.1 Data Upload**  
-1. Click **"Upload CSV file(s)"**  
-2. Select 1+ CSV files  
-3. Configure merge options:  
-   - **Horizontal**: Combine columns (align by index)  
-   - **Vertical**: Stack rows  
-
-### **4.2 Data Cleaning**  
-| Feature          | How to Use                              |
-|------------------|----------------------------------------|
-| Handle NaN       | Select: Keep/Remove/Fill with mean     |
-| Remove Duplicates| Choose: Keep first/last/all            |
-| Rename Columns   | Enter: `old_name:new_name`             |
-| Drop Columns     | Enter comma-separated column names     |
-
-### **4.3 Code Execution**  
-#### **Auto-Run Mode**  
-1. Type request (e.g., "Plot sales distribution")  
-2. Click **▶️ Execute** → AI generates and runs code  
-
-#### **Manual Mode**  
-1. Write/edit Python code  
-2. Click **▶️ Execute**  
-
-### **4.4 Chat Assistant**  
-Ask questions like:  
-- _"How to normalize this data?"_  
-- _"Explain the output of this code"_  
-
----
-
-## **5. API Reference**  
-
-### **5.1 Core Functions**  
-#### `execute_code(code: str, df: pd.DataFrame) -> dict`  
-**Parameters**:  
-- `code`: Python code to execute  
-- `df`: Target DataFrame  
-
-**Returns**:  
-```python
-{
-    "success": bool,
-    "stdout": str,       # Console output
-    "figure": Figure,     # Matplotlib plot
-    "variables": dict     # New variables created
-}
-```
-
-#### `analyze_code_execution(code: str, execution_result: dict, df: pd.DataFrame) -> str`  
-Generates natural language analysis of results in **Indonesian**.  
-
-### **5.2 LLM Classes**  
-#### `GroqLLM()`  
-- **Model**: `qwen-2.5-coder-32b`  
-- **Temperature**: 0.3  
-- **Purpose**: Code generation  
-
-#### `GroqTextLLM()`  
-- **Model**: `deepseek-r1-distill-llama-70b`  
-- **Temperature**: 0.7  
-- **Purpose**: Chat explanations  
-
----
-
-## **6. Examples**  
-
-### **6.1 Sample Workflow**  
-```python
-# AI-Generated Code Example (Auto-Run)
-import matplotlib.pyplot as plt
-plt.figure(figsize=(10, 6))
-df['sales'].plot(kind='hist', bins=20)
-plt.title('Sales Distribution')
-plt.show()
-```
-
-### **6.2 Expected Output**  
-1. Histogram plot rendered  
-2. Console output:  
-   ```text
-   Plot generated for column: sales
-   ```
-
----
-
-## **7. Troubleshooting**  
-
-| Issue                          | Solution                               |
-|--------------------------------|----------------------------------------|
-| Groq API errors                | Check quota/network connection        |
-| CSV parsing fails              | Verify delimiter (use `delimiter=';'`)|
-| Plot not showing               | Ensure `plt.show()` is called         |
-
----
-
-## **8. License & Contribution**  
-- **License**: MIT  
-- **Contribute**: Fork + PRs welcome  
-
----
-
-**Notes for Production**:  
-1. Replace hardcoded API keys with environment variables  
-2. Add user authentication for multi-user support  
-3. Implement rate limiting for Groq API calls  
-
-For full source code, visit: [GitHub Repository Link]
+---
+title: DataCoder AI
+emoji: 📊
+colorFrom: indigo
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.31.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: 🚀 A web-based interactive data analysis tool using LLM mode
+---
+
+tress`:  
+     ```text
+     waitress==2.1.2
+     ```
+
+Let me know if you need version adjustments or encounter dependency conflicts!
+---