ResQAI: Report. Respond. Rescue.

Overview

When disaster strikes, every second counts.
ResQAI is an AI-powered platform that connects citizens and first responders in real time.
Report emergencies, get instant AI classification, and help coordinate rescues more effectively.

Demo Video: https://youtu.be/r5XmbAlUuz8

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Table of Contents

• Problem Solved
• Features
• Tech Stack
• Getting Started
  • Prerequisites
  • Installation
• Project Structure
• Usage
  • Authentication
  • Machine Learning Model
• Future Enhancements
• Contributing
• License

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Problem Solved

Rescue operations often face challenges like lack of timely information, resource allocation, and situation assessment.
resQAI addresses these by:

• Automating data analysis from various sources.
• Providing real-time situational predictions.
• Improving coordination and response time.
• Enabling immediate, high-priority response for women’s safety emergencies via dedicated SOS alerts.

Features

• 🚨 Women’s Safety SOS with priority alerts.
• 📝 Submit reports with mandatory text and location.
• 🤖 AI classification of reports (Flood/Fire/Earthquake/Other) and confidence scoring.
• 💾 Store and manage reports in PostgreSQL database.
• 🗺️ Interactive Mapbox map with clickable pins for details.
• ✅ Admin verification: only verified reports are shown.
• 🛡️ CAPTCHA and API rate limiting for spam protection.

Tech Stack

• Languages: Python, JavaScript
• Frameworks: Next.js, FastAPI, React, Tailwind CSS
• Libraries: SQLAlchemy, Mongoose, Axios, bcryptjs
• Tools: Google Maps API, Pydantic Settings, SlowAPI

Getting Started

Prerequisites

• Python 3.x
• Node.js and npm
• pip

Installation

1. Clone the repository:

   git clone https://github.com/ananyaa0518/resQAI.git
   cd resQAI

2. Install Python dependencies:

   pip install -r requirements.txt

3. Install frontend dependencies (if applicable):

   cd frontend
   npm install

4. Set up environment variables:
   Create a .env file based on .env.example.

5. Run the application:

   python app.py
   # or frontend start command

Project Structure

resQAI/
├── app.py
├── requirements.txt
├── frontend/
│   ├── package.json
│   └── src/
├── models/
│   └── rescue_model.pkl
├── data/
└── README.md

Usage

Authentication

• User authentication is required for accessing sensitive endpoints.
• Supported via JWT or OAuth (specify as per implementation).
• Example:

  curl -X POST /login -d '{"username": "user", "password": "pass"}'

Machine Learning Model

• The integrated ML model predicts incident urgency and resource needs.
• Model training and inference scripts are in the models/ directory.
• Results are displayed in the dashboard or accessible via API.

Future Enhancements

• Expand ML capabilities for new incident types.
• Integrate geospatial data for better resource mapping.
• Mobile application for field responders.

Contributing

Contributions are welcome!
Please see CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License.
See LICENSE for details.