README2.md

🚀 Features

• 🌐 Interactive Map: View AQI predictions across different locations.
• 📡 Real-Time Data: Integrates live air quality data from Weatherbit API.
• 🧠 Deep Learning Model: Predicts AQI for the next 3 days based on PM2.5, PM10, NO2, SO2, CO, and current AQI.
• 📊 Comparative Visualization: Shows bar plots comparing model predictions and API forecasts.
• 📁 CSV Logging: Stores prediction data and actual AQI values from API into CSV files.

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🛠️ Setup Instructions

1. Clone the Repository

git clone https://github.com/your-username/aqi-forecast-app.git
cd aqi-forecast-app

2. Install Requirements

We recommend using a virtual environment.

pip install -r requirements.txt

3. Add Your API Key

Replace the API_KEY variable in app.py with your Weatherbit API key.

API_KEY = "your_api_key_here"

4. Add Model and Scalers

Ensure the following model and scaler files are in the project directory:

• FUTURE_AQI_v1.json
• FUTURE_AQI_v1.weights.h5
• scaler_X_cpcb_4.pkl
• scaler_y_cpcb_4.pkl

These are required to load the trained model and scale inputs/outputs.

5. Run the App

python app.py

Visit http://127.0.0.1:5000 in your browser.

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📂 File Structure

.
├── app.py                       # Main Flask app
├── FUTURE_AQI_v1.json          # Model architecture
├── FUTURE_AQI_v1.weights.h5    # Trained model weights
├── scaler_X_cpcb_4.pkl         # Scaler for input features
├── scaler_y_cpcb_4.pkl         # Scaler for output predictions
├── aqi_data.csv                # Stores model predictions
├── aqi_data_actual_api.csv     # Stores actual API forecast data
└── templates/
    └── aqi_forecast_with_legend.html  # HTML template

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📊 AQI Color Codes

The app uses the following colors for AQI categories:

AQI Range	Color
0–50	Green
51–100	Light Green
101–150	Orange
151–200	Red
201–300	Purple
301+	Gray

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📌 Future Improvements

• 📍 Add support for historical AQI trends
• ⏳ Allow user-defined forecasting range
• 📈 Deploy on cloud for public access

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