AI Chatbot for CSV Files

🚀 Interact with CSV Data Using AI

A powerful chatbot that allows users to upload CSV files and query data using natural language or Pandas-based queries. With semantic search, users can get relevant results even without exact matches!

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🔥 Features

✅ Upload & Select CSV Files – Easily upload or choose from existing files
✅ Pandas-Based Queries – Use Python-style queries like df.head() or df[df['Column'] == 'Value']
✅ Semantic Search – Find relevant data without exact wording
✅ Fast & Interactive UI – Built with Gradio for a seamless experience
✅ Caching for Faster Queries – Reduces redundant processing
✅ Handles Large CSVs – Optimized to process big datasets

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🎯 How It Works

1️⃣ Upload a CSV file – or select from previously uploaded files
2️⃣ Ask questions – Enter a Pandas-style query or a natural language question
3️⃣ Get instant results – Data is displayed in a table format like in Excel
4️⃣ Switch between files – Load different datasets dynamically

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🛠️ Tech Stack

• Python – Core logic
• FastAPI – Backend for file uploads and API endpoints
• Gradio – Interactive UI
• Pandas – Data processing
• FAISS – For semantic search
• SentenceTransformers – Embedding model for AI-powered queries

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🚀 Installation & Usage

1️⃣ Clone the Repository

git clone https://github.com/udaybhadauria/AI-CSV-Files-Chatbot.git
cd AI-Chatbot-CSV

2️⃣ Set Up Virtual Environment

python -m venv venv
source venv/bin/activate  # (For Mac/Linux)
venv\Scripts\activate  # (For Windows)

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the App

uvicorn app:app --reload

The app will run locally at http://127.0.0.1:7860/

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🏗️ Upcoming Features

🚀 SQL-like Query Support – Write queries in SQL syntax instead of Pandas
🚀 Advanced Data Visualization – Generate graphs from queries
🚀 Multi-File Querying – Search across multiple datasets at once
🚀 Cloud Storage Integration – Save and access CSVs from the cloud

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🤝 Contributing

Want to improve this project? Contributions are welcome!

1. Fork the repository
2. Create a new branch

   git checkout -b feature-branch

3. Make your changes and commit

   git commit -m "Added new feature"

4. Push the changes

   git push origin feature-branch

5. Open a Pull Request