🎓 ScholarMatch

An AI-powered scholarship discovery platform and automated counsellor for Indian students.

ScholarMatch uses Machine Learning, RAG (Retrieval-Augmented Generation), and Explainable AI (XAI) to match students with the most relevant scholarships, verify their documents via OCR, and guide them through their application process via a conversational AI chatbot.

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✨ Key Features

• 🎯 AI Scholarship Matching: Recommends top scholarships based on a student's exact profile (GPA, Income, Region, Caste, Gender).
• 🤖 RAG-powered Chatbot: A 24/7 AI counsellor powered by Google Gemini and ChromaDB that grounds its answers strictly in real scholarship data.
• 📄 OCR Document Verification: Instantly extracts and verifies documents (Aadhaar, Income Certificates, Marksheets) using PyTesseract.
• ⚖️ Explainable AI (XAI): Transparent recommendations with fairness notes, showing students exactly why they matched.
• 🎛️ What-If Simulator: Real-time sliders for GPA and Income to dynamically simulate how eligibility rankings change.
• 🛡️ Rate Limiting & Protection: Built-in IP rate-limiting on both Node.js and Python microservices to prevent abuse.

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🛠️ Tech Stack

Frontend

• Framework: React.js (Vite)
• Styling: Tailwind CSS
• Libraries: Axios, React Markdown, i18next

Backend (Node.js)

• Framework: Express.js
• Database: MongoDB & Mongoose
• Middleware: Express Rate Limit, CORS

ML Microservice (Python)

• Framework: FastAPI, Uvicorn, SlowAPI
• AI & RAG: Google Gemini (2.0 Flash API), ChromaDB, SentenceTransformers (all-MiniLM-L6-v2)
• Computer Vision: PyTesseract (OCR), Pillow

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📂 Project Architecture

ScholarMatch/
├── frontend/          # React Vite app (UI, Chat Widget, Forms)
├── backend/           # Node.js Express server (MongoDB, Auth, Routing)
└── ml-service/        # Python FastAPI (Predictions, OCR, ChromaDB RAG, Gemini)

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🚀 Installation & Setup

Prerequisites

• Node.js (v18+)
• Python (v3.9+)
• MongoDB (Local or Atlas URI)
• Optional: Tesseract OCR installed on your system for real document scanning.

1. Setup ML Microservice (Python)

Navigate to the ML service directory and install dependencies:

cd ml-service
python -m venv venv

# Activate virtual environment
# On Windows: venv\Scripts\activate
# On Mac/Linux: source venv/bin/activate

pip install -r requirements.txt

Create a .env file inside ml-service/:

PORT=8000
GOOGLE_API_KEY="your_google_gemini_api_key_here"

Start the FastAPI server:

python main.py
# Server runs on http://localhost:8000

2. Setup Node.js Backend

Open a new terminal and navigate to the backend directory:

cd backend
npm install

Create a .env file inside backend/:

PORT=5000
MONGO_URI="mongodb://localhost:27017/scholarmatch"
ML_SERVICE_URL="http://localhost:8000"

Start the Node.js server:

npm run dev
# Server runs on http://localhost:5000

3. Setup Frontend

Open a third terminal and navigate to the frontend directory:

cd frontend
npm install
npm run dev
# App runs on http://localhost:5173