Women Safety App

Overview

The Women Safety App is designed to enhance personal security by leveraging machine learning to analyze emotions from voice recordings. When a user speaks, the app detects distress or fear using a trained emotion recognition model. Based on the detected emotion, the app automatically sends an emergency alert message to a predefined emergency contact number.

Features

• Real-time Voice Analysis: Listens to the user's voice and determines their emotional state.
• Machine Learning Integration: Uses a trained emotion detection model to recognize distress signals.
• Emergency Alert System: Automatically sends alerts when a distressing emotion is detected.
• Mobile Application: Provides a user-friendly interface for seamless interaction.
• Backend API: Flask-based API to handle emotion detection requests.

Repository Structure

📂 Teckzite
├── 📂 hack             # Backend Flask API for ML model inference
├── 📂 main             # Android application source code
└── emotion_model.h5    # Pre-trained emotion detection ML model

Usage

1. Launch the app and allow necessary permissions (Microphone, SMS, Location if applicable).
2. Click the "Start Listening" button.
3. Speak normally; the app will analyze emotions in real-time.
4. If distress is detected, an emergency alert will be sent.

Technologies Used

• Android (Jetpack Compose, Kotlin) - Mobile app development.
• Flask - Backend API for emotion recognition.
• TensorFlow/Keras - Emotion detection model.
• Librosa - Audio processing.

Contact

For any inquiries or suggestions, please reach out via the GitHub repository.