🎓 AI-Powered College Data Assistant

An AI-powered natural language interface for querying and analyzing college student data. Built with FastAPI, Streamlit, SQLite, and OpenAI (with rule-based fallback).

🏗️ Architecture

User Query → Context Engine → Decision Engine → Response
                                    │
                    ┌───────────────┼───────────────┐
                    ▼               ▼               ▼
              SQL Query      LLM Analysis     RAG Simulation
              (SQLite)       (OpenAI/FB)     (Static Docs)
                    │               │               │
                    └───────────────┼───────────────┘
                                    ▼
                          Structured Response
                     (Answer + Explanation + Data)

📁 Project Structure

File	Description
db.py	Database schema, sample data (25 students), query helpers
logic.py	Context Engine + Decision Engine + LLM integration
main.py	FastAPI backend server
app.py	Streamlit chat UI
knowledge_base/	Static documents for RAG simulation

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. (Optional) Configure OpenAI API Key

cp .env.example .env
# Edit .env and add your OpenAI API key

Note: The app works fully without an API key using a rule-based fallback engine.

3. Initialize the Database

python db.py

This creates college.db with 25 sample students, attendance records, and marks.

4. Start the FastAPI Backend

uvicorn main:app --reload --port 8000

The API will be available at http://localhost:8000.

• Health check: http://localhost:8000/health
• API docs: http://localhost:8000/docs

5. Start the Streamlit Frontend

Open a new terminal and run:

streamlit run app.py

The UI will open at http://localhost:8501.

💬 Example Queries

Query	Type	Engine
"Show students with attendance below 60%"	Data	SQL
"List all students in ECE branch"	Data	SQL
"Identify weak students and suggest improvements"	Analytical	LLM/Fallback
"Analyze performance of CSE 3rd year students"	Analytical	LLM/Fallback
"Who are the top performers?"	Analytical	LLM/Fallback
"What is the minimum attendance policy?"	Knowledge	RAG

🧠 Key Features

• Context Engine: Tracks user role and maintains last 3 queries in memory
• Decision Engine: Classifies queries → routes to SQL / LLM / RAG handlers
• Explainability: Every response includes an explanation of why the result was generated
• Weak Student Flagging: "These students are flagged because attendance < 60% and marks < 50%"
• Dual Mode: Works with OpenAI API or fully offline with rule-based analysis

📊 Database Schema

students(id, name, branch, year)
attendance(student_id, percentage)
marks(student_id, subject, score)

Sample data includes students from CSE, ECE, ME, and CE branches across years 1-4.