README.md

---
title: LLM Data Analyzer
emoji: 📊
colorFrom: blue
colorTo: indigo
sdk: docker
sdk_version: latest
app_file: app.py
pinned: false
---

# 📊 LLM Data Analyzer

An AI-powered tool for analyzing data and having conversations with an intelligent assistant powered by Llama 2.

## Features

- **📤 Upload & Analyze**: Upload CSV or Excel files and get instant analysis
- **💬 Chat**: Have conversations with Llama 2 AI assistant
- **📊 Data Statistics**: View comprehensive data summaries and insights
- **🚀 Fast**: Runs on free Hugging Face CPU tier

## How to Use

1. **Upload Data** - Start by uploading a CSV or Excel file
2. **Preview** - Review your data and statistics
3. **Ask Questions** - Get AI-powered analysis and insights
4. **Chat** - Have follow-up conversations with the AI

## Technology Stack

- **Model**: Llama 2 7B (quantized to 4-bit)
- **Framework**: Streamlit
- **Inference Engine**: Llama.cpp
- **Hosting**: Hugging Face Spaces
- **Language**: Python 3.10+

## Performance

| Metric | Value |
|--------|-------|
| Speed | ~5-10 tokens/second (free CPU) |
| Model Size | 4GB (quantized) |
| Context Window | 2048 tokens |
| First Load | ~30 seconds (model download) |
| Subsequent Responses | ~5-15 seconds |
| Hardware | Free Hugging Face CPU |

## Local Development (Faster)

For faster local development with GPU acceleration on Apple Silicon Mac:

```bash
# Clone the repository
git clone https://github.com/Arif-Badhon/LLM-Data-Analyzer
cd LLM-Data-Analyzer

# Switch to huggingface-deployment branch
git checkout huggingface-deployment

# Install dependencies
pip install -r requirements.txt

# Run with MLX (Apple Silicon GPU - ~70 tokens/second)
streamlit run app.py
```

## Deployment Options

### Option 1: Hugging Face Space (Free)
- CPU-based inference
- Speed: 5-10 tokens/second
- Cost: Free

### Option 2: Local with MLX (Fastest)
- GPU-accelerated on Apple Silicon
- Speed: 70+ tokens/second
- Cost: Free (uses your Mac)

### Option 3: Hugging Face PRO (Fast)
- GPU-accelerated inference
- Speed: 50+ tokens/second
- Cost: $9/month

## Getting Started

### Quick Start (3 minutes)

```bash
# 1. Install Python 3.10+
# 2. Clone repo
git clone https://github.com/Arif-Badhon/LLM-Data-Analyzer
cd LLM-Data-Analyzer

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run Streamlit app
streamlit run app.py
```

### With Docker (Local Development)

```bash
# Make sure Docker Desktop is running
docker-compose up --build

# Access at http://localhost:8501
```

## Troubleshooting

### "Model download failed"
- Check internet connection
- HF Spaces need internet to download models from Hugging Face Hub
- Wait and refresh the page

### "App takes too long to load"
- Normal on first request (10-30 seconds)
- Model is being downloaded and cached
- Subsequent requests are much faster

### "Out of memory"
- Free tier CPU is limited
- Unlikely with quantized 4GB model
- If it happens, upgrade to HF PRO

### "Slow responses"
- Free tier CPU is slower than GPU
- Expected: 5-10 tokens/second
- For faster responses: use local MLX (70 t/s) or upgrade HF tier

## Technologies Used

- **Python** - Core language
- **Streamlit** - Web UI framework
- **Llama 2** - Large language model
- **Llama.cpp** - CPU inference
- **MLX** - Apple Silicon GPU inference
- **Pandas** - Data processing
- **Docker** - Containerization
- **Hugging Face Hub** - Model hosting

## License

MIT License

## Author

**Arif Badhon**

## Support

If you encounter any issues:
1. Check the Troubleshooting section above
2. Review Hugging Face Spaces Docs
3. Open an issue on GitHub

---

**Happy analyzing! 🚀**