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A_Simple_AI_Data_Analyzer
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---
license: mit
title: A simple Data Analyzer
sdk: gradio
emoji: 📊
colorFrom: red
pinned: true
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/69848113135d8332da94b363/JFpif40IVp5pJhMZ0-n8x.png
---
# Personal Data Assistant

A simple interactive data analysis and visualization assistant built with Python and Gradio. Ask questions about your datasets, generate insightful visualizations, and get instant answers—all in a beautiful, user-friendly web interface.

![Gradio App Screenshot] (images/image.png)
## Features

- **Conversational Data Analysis**: Chat with the agent to analyze your CSV datasets.
- **Automatic Visualizations**: Instantly generate bar charts and other graphs from your data.
- **Modern UI**: Custom black and red theme for a sleek, professional look.
- **Conversation Logging**: Keeps a log of your questions and the agent's responses.
- **Easy Dataset Upload**: Upload your own CSV files for instant analysis.

## Demo

https://your-demo-link.com

## Getting Started

### Prerequisites
- Python 3.10+
- pip (Python package manager)

### Installation

1. **Clone the repository:**
   ```bash
   git clone https://github.com/yourusername/personal-data-assistant.git
   cd personal-data-assistant
   ```
2. **Create a virtual environment (optional but recommended):**
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```
3. **Install dependencies:**
   ```bash
   pip install -r requirements.txt
   ```
4. **Set up environment variables:**
   - Create a `.env` file in the project root with your API keys for the model:
     ```env
     MISTRAL_API_KEY=your_mistral_api_key
     
     ```

### Running the App

```bash
python app.py
```

The Gradio interface will launch in your browser.

## Usage

- **Ask questions** about your dataset in natural language.
- **Upload a CSV file** to analyze your own data.
- **View generated graphs** and download them if needed.

## Example Datasets
- `housing_dataset.csv`: Sample housing prices by city.

## Project Structure

```
├── app.py                  # Gradio web app
├── main.py                 # Core agent logic and tools
├── housing_dataset.csv     # Example dataset
├── vgsales.csv             # Example dataset
├── conversation_log.txt    # Conversation history
├── .gitignore
└── .gradio/flagged/        # Gradio flagged data
```

## Technologies Used
- Python
- Gradio
- Matplotlib, Seaborn, Pandas
- smolagents, LiteLLM

## Customization
- Modify `custom_css` in `app.py` to change the UI theme.
- Add new tools or models in `main.py`.

## License

MIT License. See [LICENSE](LICENSE) for details.

## Acknowledgements
- [Gradio](https://gradio.app/)
- [Mistral AI](https://mistral.ai/)
- [smol-ai/smolagents](https://github.com/smol-ai/smolagents)

---

*Made with ❤️ for data enthusiasts.*