LLM Data Analyst Assistant

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🚀 What is this?

An AI-powered Data Analyst Assistant that converts natural language questions into SQL queries and generates real-time insights from CSV/Excel datasets.

Instead of manually writing SQL queries, users can simply ask business questions in plain English and the assistant automatically:

• Generates SQL queries
• Executes them on structured data
• Displays results instantly
• Creates automatic visualizations

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✨ Features

✅ Upload CSV or Excel datasets
✅ Ask business questions in plain English
✅ Automatic SQL query generation
✅ SQLite in-memory execution
✅ Real-time data visualization
✅ Local LLM integration (Ollama – gemma3)
✅ No cloud API required

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🧠 How It Works

User Question
→ LLM converts question to SQL
→ SQLite executes SQL
→ Streamlit displays results
→ Automatic visualization generated

This completely removes the need for manual SQL writing.

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📊 Example Queries

• Top 5 customers by sales
• Total revenue by month
• Average order value
• Sales by region
• Monthly growth trend

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🛠 Tech Stack

Layer	Technology
UI	Streamlit
Data Processing	Pandas
Database	SQLite (In-Memory)
LLM	Ollama (gemma3 local model)
Visualization	Streamlit Charts

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💻 Quick Setup

1️⃣ Clone the Repository

git clone https://github.com/YOUR_USERNAME/LLM-Data-Analyst-Assistant.git
cd LLM-Data-Analyst-Assistant

2️⃣ Install Dependencies

pip install streamlit pandas openpyxl requests

3️⃣ Install Ollama (Local LLM)

Download from:

https://ollama.com/download

Make sure Ollama Desktop is running.

4️⃣ Ensure Model is Available

Use an installed model such as:

gemma3:4b

Check available models:

Invoke-RestMethod http://localhost:11434/api/tags

5️⃣ Run the App

streamlit run app.py

Open in browser:

http://localhost:8501

📊 Example Use Case

👨‍💼 Business Manager asks:

Which customer generated the highest revenue this quarter?

The Assistant:

• Converts question into SQL
• Aggregates data
• Sorts by revenue
• Returns top customer instantly
• Displays a visualization

🎯 Why This Project?

• This project demonstrates:

• LLM + SQL integration

• Local AI deployment (No OpenAI API required)

• Real-time analytics

• Business intelligence automation

• Practical AI application for decision-making