அவள் — Aval 🛡️

அவள் (Tamil: "She") — An AI-powered women safety system combining machine learning threat detection with real-time emergency response, built to protect and empower.

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📁 Project Structure

Aval/
├── backend/
│   ├── Training/
│   │   ├── Training.ipynb               # Main model training notebook
│   │   └── darktraining.ipynb           # Low-light / night scenario training
│   ├── models/
│   │   ├── women_safety_complete_model  # Full trained model (Keras/H5)
│   │   ├── women_safety_complete_model  # Alternate export format
│   │   ├── women_safety_mobile_integrati# TFLite model for mobile inference
│   │   └── women_safety_model_config.json
│   └── app.py                           # Python backend server (Flask/FastAPI)
├── frontend/
│   └── index.html                       # Web frontend
├── .gitignore
├── requirements.txt
└── README.md

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✨ Features

🆘 SOS & Emergency Alert

• One-tap SOS to instantly notify emergency contacts
• Sends real-time GPS coordinates along with the alert
• Triggers automatically on shake detection

📍 Live Location Sharing

• Continuous real-time location updates to trusted contacts
• Background location tracking during active SOS

📳 Shake to Trigger Alert

• Detects sudden shake gestures via accelerometer
• Activates SOS without requiring the phone to be unlocked

🤖 ML-Based Threat Detection

• Trained on women safety scenarios including low-light and night conditions (darktraining.ipynb)
• Complete model available in full and mobile-optimised (TFLite) formats
• Configurable via women_safety_model_config.json

👥 Crowd Analysis

• Detects unsafe crowd patterns and density using computer vision
• Alerts user when entering a potentially dangerous zone

🏥 Nearby Police / Hospital Finder

• Fetches nearby police stations and hospitals using GPS
• Provides distance, contact info, and navigation

🛠️ Admin Panel

• View SOS alert logs and incident history
• Monitor active sessions and user data (with consent)

🥋 Self-Defense Tips & Videos

• Curated self-defense guides accessible from the frontend

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🧠 ML Models

File	Description
women_safety_complete_model	Full trained model (Keras / H5 format)
women_safety_complete_model (alt)	Secondary export format for serving
women_safety_mobile_integrati...	TFLite — optimised for mobile inference
women_safety_model_config.json	Model configuration and class mappings

Training Notebooks

Notebook	Purpose
Training.ipynb	Main model training pipeline
darktraining.ipynb	Training on low-light / night-time scenarios

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🛠️ Tech Stack

Layer	Technology
Backend	Python (Flask / FastAPI)
ML Framework	TensorFlow / Keras + TFLite
Training	Jupyter Notebook
Frontend	HTML5
Location Services	Google Maps API / Browser Geolocation
Notifications	SMS / Firebase Cloud Messaging

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🚀 Getting Started

Prerequisites

• Python 3.9+
• pip
• Jupyter Notebook (for training)
• A modern web browser (for frontend)

Installation

1. Clone the repository

git clone https://github.com/th30d4y/Aval.git
cd Aval

2. Install dependencies

pip install -r requirements.txt

3. Run the backend server

cd backend
python app.py

4. Open the frontend

Open frontend/index.html in your browser, or serve it:

cd frontend
python -m http.server 8080

5. (Optional) Retrain the model

cd backend/Training
jupyter notebook Training.ipynb
# For low-light training:
jupyter notebook darktraining.ipynb

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📦 Dependencies

Install all required packages with:

pip install -r requirements.txt

Key dependencies include TensorFlow, Flask/FastAPI, OpenCV, and NumPy. Refer to requirements.txt for the full list.

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👥 Contributors

• Stalin-143 — Stalin
• harriiinnii

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📄 License

This project is open source. See the LICENSE file for details.

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அவள் பாதுகாப்பாக இருக்கட்டும்
May She Be Safe