🚀 Grant Matching Application

An AI-powered system for intelligently matching researchers with funding opportunities.
Built with a Flask backend, a React (Vite) frontend, and modern AI/NLP techniques for semantic matching.

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✨ Key Features

• 🎯 AI-driven Matching: Semantic search and LLM-refined grant-researcher matching
• 🧑‍🔬 Researcher & Grant Management: Full database models and APIs
• 🌐 Smart Web Scraping: Automated extraction from NIH and CIHR funding databases
• 🧩 Clean Full-Stack Architecture: Flask REST API + React frontend with Vite bundler
• 🔥 Optimized for Scalability: Modular, extensible, and production-ready design

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🏗️ Project Structure

Grant_Matching/
├── app/                # Flask backend (models, routes, agents, utilities)
├── frontend/           # React frontend (pages, components, services)
├── config.py           # Centralized configuration
├── run.py              # Application launcher
├── requirements.txt    # Backend dependencies
├── .gitignore          # Git ignore rules

🔥 Backend (/app/)

• models.py — SQLAlchemy models (Researchers, Grants, Matches)
• routes.py — Flask API routes (scraping, matching, CRUD operations)
• agents/grant_matcher.py — Matching engine: TF-IDF + LLM-enhanced scoring
• scraper.py — Web scraping modules for grant data
• utils/ — Utilities: text cleaning, fingerprinting, data importers

🎨 Frontend (/frontend/)

• pages/ — React pages (Home, Researchers, Grants, Match Results, Details)
• components/ — UI elements (Navbar, etc.)
• services/api.js — Centralized API handling (Axios)
• ⚡ Vite-powered build system for ultra-fast frontend development

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🚀 Getting Started

Prerequisites

• Python 3.8+
• Node.js 18+
• PostgreSQL or SQLite (depending on backend configuration)

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⚙️ Backend Setup

# 1. Create and activate a virtual environment
python -m venv venv
source venv/bin/activate    # Windows: venv\Scripts\activate

# 2. Install backend dependencies
pip install -r requirements.txt

# 3. Initialize and migrate the database
flask db init
flask db migrate -m "initial migration"
flask db upgrade

# 4. Run the Flask development server
python run.py

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🎯 Frontend Setup

# Navigate to the frontend directory
cd frontend

# Install frontend dependencies
npm install

# Start the React development server
npm run dev

• Frontend accessible at: http://localhost:5173/
• Backend API available at: http://localhost:5000/

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🛠 Core Matching Logic

1. Semantic Pre-filtering:
   Researcher interests and grant descriptions are embedded using Sentence Transformers.
2. LLM Reasoning:
   Fine-tuned scoring through OpenAI GPT-4o-mini and OpenAI GPT-4.1-mini based on extracted embeddings.
3. Deduplication:
   Fingerprinting is applied to ensure no redundant researcher-grant matches are stored.

Main matching logic:
→ app/agents/grant_matcher.py

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📡 API Endpoints

Endpoint	Method	Description
/researchers	GET	List all researchers
/grants	GET	List all grants
/matches	GET	Retrieve all matched pairs
/generate_matches	POST	Generate matches (all or specific researcher)
/scrape_nih	POST	Scrape grants from NIH
/scrape_cihr	POST	Scrape grants from CIHR

(Endpoints defined in app/routes.py)

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📜 License

Distributed under the MIT License — free to use, modify, and distribute.

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🙏 Acknowledgments

Built with contributions from the amazing open-source community, including:

• OpenAI GPT models
• LangChain
• Sentence Transformers
• Flask, SQLAlchemy
• React, Vite
• SerpAPI, ScrapeGraphAI

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📝 How to Cite

If you use the Grant Matching Application in your work, please cite:

Software

Mortezaagha, P. (2025). Grant Matching Application (v1.0.0). GitHub. https://github.com/pouriamrt/Grant_Macher

BibTeX

@software{mortezaagha_grant_matching_2025,
  author  = {Mortezaagha, Pouria},
  title   = {Grant Matching Application},
  version = {1.0.0},
  year    = {2025},
  url     = {https://github.com/pouriamrt/Grant_Macher},
  license = {MIT},
  note    = {AI-powered researcher–grant semantic matching with Flask + React (Vite)}
}