🍲 Food Waste Reduction & Hunger Relief System

A comprehensive Flask web application that connects food donors with organizations helping people in need, using machine learning to prioritize distribution.

Features

Admin Dashboard

• View all donations and needs
• Interactive charts and statistics (Chart.js)
• Priority heatmap visualization (Folium)
• Smart donation-to-need matching algorithm
• Real-time analytics

Donor Portal

• Submit food donations via intuitive form
• View donation history
• Track donation status
• Bootstrap 5 responsive design

NGO Portal

• Submit need requests
• View needs with AI-predicted priority scores
• Priority heatmap for needs distribution
• Sort by priority levels

Project Structure

Food-Waste-Reduction-Hunger-Relief-System/
├── app.py                      # Main Flask application
├── api_routes.py               # API endpoints (Blueprint)
├── requirements.txt            # Python dependencies
├── README.md                   # Documentation
├── data/
│   ├── donations.csv          # Donations dataset
│   └── needs.csv              # Needs dataset
├── model/
│   └── priority_model.pkl     # Trained ML model (RandomForest)
├── templates/
│   ├── index.html             # Home/Role selection page
│   ├── admin.html             # Admin dashboard
│   ├── user.html              # Donor dashboard
│   ├── ngo.html               # NGO dashboard
│   ├── donations.html         # Donations view
│   ├── needs.html             # Needs view
│   ├── charts.html            # Charts view
│   └── heatmap.html           # Heatmap view
└── static/
    ├── css/
    │   └── style.css          # Custom styles
    └── js/
        └── utils.js           # Utility functions

Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/FoodLoop.git
   cd FoodLoop

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

   python app.py

4. Access the application Open your browser and navigate to:

   http://localhost:5000
   

📊 API Endpoints

Method	Endpoint	Description
GET	/api/donations	Get all donations
GET	/api/needs	Get all needs with priority scores
POST	/api/donate	Submit new donation
POST	/api/need	Submit new need
GET	/api/match	Get donation-to-need matches
GET	/api/statistics	Get statistics for charts

Example API Usage

Submit a Donation:

curl -X POST http://localhost:5000/api/donate \
  -H "Content-Type: application/json" \
  -d '{
    "name": "John Doe",
    "food_type": "Vegetables",
    "quantity_kg": 25.5,
    "lat": 12.9716,
    "lon": 77.5946,
    "freshness": 0.85
  }'

Submit a Need:

curl -X POST http://localhost:5000/api/need \
  -H "Content-Type: application/json" \
  -d '{
    "org_name": "Care Foundation",
    "need_description": "Rice",
    "people_count": 100,
    "urgency": 3,
    "lat": 12.9716,
    "lon": 77.5946
  }'

🤖Machine Learning Model

The system uses a RandomForestClassifier (priority_model.pkl) to predict priority scores for needs based on:

• Number of people affected FoodLoop — Food Waste Reduction & Hunger Relief

A compact, production-minded Flask app that connects food donors with NGOs and uses a trained model to prioritize urgent needs. This README is tailored for hackathon judges: it highlights goals, architecture, how to run the app quickly, the core ML idea, and how we validated the approach.

Why this matters

• Reduce edible food waste by matching donations to verified needs.
• Prioritize distribution to high-impact, high-urgency requests.
• Simple, deployable stack that demonstrates data-driven social impact.

Repository at-a-glance

• app.py — Flask application and route registration
• api_routes.py — JSON API endpoints used by frontend and integrations
• models.py — SQLAlchemy models and DB initialization
• data/ — sample CSVs: donations.csv, needs.csv
• model/priority_model.pkl — trained priority model (RandomForest)
• templates/ + static/ — UI (Bootstrap, Chart.js, Folium heatmap)

Core features (concise)

• Donor portal: submit donations with location, quantity and freshness
• NGO portal: submit needs (people_count, urgency) and receive a priority score
• Admin: dashboards, charts and a heatmap to visualize demand and supply
• API: simple JSON endpoints for donations, needs, matches and stats
• ML: a RandomForest model that predicts priority for needs (packaged at model/priority_model.pkl)

API (examples)

• GET /api/donations — returns all donations
• GET /api/needs — returns needs with priority score
• POST /api/donate — submit donation
• POST /api/need — submit need
• GET /api/match — returns suggested matches

Example: submit a need (curl)

curl -X POST http://localhost:5000/api/need -H "Content-Type: application/json" -d "{\"org_name\":\"Care Foundation\",\"need_description\":\"Rice\",\"people_count\":100,\"urgency\":3,\"lat\":12.97,\"lon\":77.59}"

Machine learning summary

• Model: scikit-learn RandomForestClassifier saved as model/priority_model.pkl.
• Input features used in this demo: people_count, urgency (and optionally engineered spatial/time features).
• Output: a discrete or scaled priority score used to sort/visualize needs.
• Notes: model in the repo is a demo trained on synthetic data. For production, retrain with real labels, add cross-validation, feature importance tracking, and clear performance metrics..