Chill Check is a web application designed to detect and predict stress levels using an Artificial Neural Network (ANN). The app provides AI generated personalized stress analysis and actionable tips to help users manage their stress effectively. The app also features chart visualizations to help users track their stress trends over time.

It is built with Flask for the backend, SQLite for the database.

• 🧠 Stress Prediction
  Predicts stress levels using an ANN model trained on physiological data such as heart rate, snoring rate, and respiratory rate.

• 🔐 User Authentication
  Secure login and registration system using Flask-Login to protect user data.

• 📊 Stress Trend Analysis
  Visualizes stress trends over time using beautiful charts powered by Chart.js, allowing users to track their stress levels and identify patterns.

• 📌 Personalized Suggestions
  Provides AI generated personalized actionable tips based on the user's stress levels, including breathing exercises, sleep hygiene tips, and relaxation techniques.

• 📱 Responsive Design
  Works seamlessly on both desktop and mobile devices, ensuring a smooth user experience.

1. 📥 Data Input
   Users enter physiological data (heart rate, snoring rate, respiratory rate) through an intuitive web interface.

2. 🧠 Stress Prediction
   The ANN model processes input data and predicts whether the user is Stressed or Not Stressed.

3. 📊 Trend Analysis
   The system visualizes user stress trends over time using cool charts, helping them identify patterns and triggers.

4. 📌 Personalized Suggestions
   Based on predictions and trends, the app provides tailored suggestions via GEMINI to help users manage stress more effectively.

• 📡 Integration with Wearable Devices : Real-time data collection from smartwatches and fitness trackers.

• 📊 Advanced Analytics & AI : Deeper insights into stress patterns using predictive analytics and AI-driven recommendations.

• 🐍 Python: Backend language for implementing core logic and machine learning models.
• 🌐 Flask: Lightweight web framework for backend and API development.
• 🗄️ SQLite: Embedded database for storing user data and stress predictions.
• 🧠 TensorFlow/Keras: Libraries for building and training the Artificial Neural Network (ANN) model.
• 📊 Chart.js: JavaScript library for interactive stress trend visualizations.
• 💻 HTML/CSS/JavaScript: Technologies for building the user interface.
• 🔐 Flask-Login: Library for user authentication and session management.
• 🔄 Flask-Migrate: Handles database migrations.

https://github.com/anandpanda/Stress-Detection-and-Prediction-using-ANN.git
cd Stress-Detection-and-Prediction-using-ANN

chmod +x setup.sh
./setup.sh

This will:

• Create & activate a virtual environment
• Install dependencies
• Apply database migrations
• Start the application

The app will be available at http://127.0.0.1:10000🎉