Create README2.md

README2.md

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+
+---
+
+# 🌍 AQI Forecasting Web App with Map Visualization
+
+This Flask-based web application allows users to input geographical coordinates (latitude and longitude) and receive air quality forecasts (AQI) for the next three days. It combines a deep learning model with real-time data from the Weatherbit API, and visualizes predictions on an interactive map using Folium.
+
+---
+
+## 🚀 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.
+
+---
+
+## 🛠️ Setup Instructions
+
+### 1. Clone the Repository
+
+```bash
+git clone https://github.com/your-username/aqi-forecast-app.git
+cd aqi-forecast-app
+```
+
+### 2. Install Requirements
+
+We recommend using a virtual environment.
+
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Add Your API Key
+
+Replace the `API_KEY` variable in `app.py` with your [Weatherbit API key](https://www.weatherbit.io/account/create).
+
+```python
+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
+
+```bash
+python app.py
+```
+
+Visit [http://127.0.0.1:5000](http://127.0.0.1:5000) in your browser.
+
+---
+
+## 📂 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
+```
+
+---
+
+## 📊 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         |
+
+---
+
+## 📌 Future Improvements
+
+- 📍 Add support for historical AQI trends
+- ⏳ Allow user-defined forecasting range
+- 📈 Deploy on cloud for public access
+
+---