📊 SQL Query Generator Assistant

An AI-powered chatbot built using Streamlit, Gemini API, and LangChain that helps users convert natural language questions into valid SQL queries. Just upload your CSV or Excel file, describe your query in plain English, and get instant SQL output!

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🚀 Features

• 🗣️ Natural Language to SQL: Just ask questions like:
  • "Show all students with grade A"
  • "Get the total sales for June"
• 📂 Upload Your Own Data: Supports .csv and .xlsx files
• 🤖 AI-Powered Responses: Uses Google’s Gemini model (via LangChain)
• 🧠 Chat Memory: Remembers your name and conversation context
• 📝 Conversation Summarizer: Generates a quick summary of your past chat
• 🔄 Refresh Chat: Easily reset the conversation with one click
• 📎 Schema-Aware SQL Generation: Automatically adapts SQL to your uploaded dataset

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🛠️ Tech Stack

• Frontend: Streamlit
• LLM Backend: Gemini API
• Memory: LangChain's ConversationBufferMemory
• Environment Management: Python dotenv
• Data Handling: pandas

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📁 Project Structure

├── app.py # Streamlit UI code ├── backend.py # NLP to SQL logic with LangChain & Gemini ├── .env # Environment variables (API keys) ├── requirements.txt # Python dependencies └── README.md # You're here!

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✅ How to Run Locally

1. Clone the Repo

   git clone https://github.com/DevWaqarAhmad/sql-query-assistant.git
   cd sql-query-assistant
   

pip install -r requirements.txt

GEMINI_API_KEY=your_google_gemini_api_key

streamlit run app.py 🧪 Example Queries Question Generated SQL Show all employees with salary > 5000 SELECT * FROM your_table WHERE salary > 5000; Count the number of students SELECT COUNT(*) FROM your_table; Get average marks SELECT AVG(marks) FROM your_table;

📬 Contact 📧 Email: devwaqarahmad@gmail.com

💡 Credits Built with by Waqar Ahmad Powered by Google Gemini API + LangChain

Let me know if you'd like GitHub repo badge, deployment badge (like Render, Vercel), or anything else added.