Pollution & Health Analyzer

A comprehensive dashboard for tracking and predicting pollution levels and their impact on public health in Atchutapuram, Andhra Pradesh.

Project Structure

• Backend/: Python/Flask API that handles data simulation and health predictions.
• Frontend/: A modern, interactive dashboard built with vanilla JavaScript and CSS.

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Getting Started

Follow these steps to run the project locally on your machine.

1. Run the Backend

The backend is a Flask API that serves pollution and hospital data.

Prerequisites:

• Python 3.x installed on your system.

Steps:

1. Open your terminal or command prompt.
2. Navigate to the Backend directory:

   cd Project/Backend

3. Install the required dependencies:

   pip install -r requirements.txt

4. Start the server:

   python app.py

   [!NOTE] The backend will run at http://127.0.0.1:5000.

2. Run the Frontend

The frontend is a static web application that communicates with the backend.

Steps:

1. Navigate to the Frontend directory:

   cd Project/Frontend

2. Open index.html in your browser.
   • Recommended: For the best experience (and to handle Firebase module imports correctly), use a local web server like Live Server (VS Code extension).
   • Alternatively, use Python's built-in server:

     python -m http.server 8000

     Then visit http://localhost:8000 in your browser.

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Features

• Real-time Pollution Tracking: Monitor AQI, PM2.5, PM10, and NO2.
• Health Predictions: View predicted hospital cases for various diseases (COPD, Stroke, etc.) based on pollution levels.
• Interactive Maps: Locate hospitals in Atchutapuram.
• Theme Switching: Seamlessly toggle between Light and Dark modes.
• Data Export: Download data in CSV format.

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API Endpoints

• GET /data: Returns aggregated pollution and prediction data for all hospitals.
• GET /predict?month=...&year=...&hospital=...: Returns health predictions for a specific location.

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Deployment to Render

You can deploy this project to Render either automatically (using a Blueprint) or manually (setting up separate services).

Option 1: Automatic Deployment (Blueprint)

This is the fastest way to get both services running at once.

1. Push your code to GitHub.
2. Login to your Render Dashboard.
3. Click New + and select Blueprint.
4. Connect your GitHub repository.
5. Render will automatically use the render.yaml file to set up both the Backend and Frontend.

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Option 2: Manual Deployment (Separate Services)

Use this if you want to manage the Backend and Frontend independently.

1. Deploy the Backend (Web Service)

1. Click New + on Render and select Web Service.
2. Connect your GitHub repository.
3. Set the following configuration:
   • Name: pollution-backend
   • Root Directory: Project/Backend
   • Runtime: Python 3
   • Build Command: pip install -r requirements.txt
   • Start Command: gunicorn app:app
4. Click Create Web Service.

2. Deploy the Frontend (Static Site)

1. Wait for the Backend to deploy, then copy its URL (e.g., https://pollution-backend.onrender.com).
2. Update the API constant in Project/Frontend/script.js with your backend URL:

   const API = "https://your-backend-name.onrender.com";

3. Commit and push this change to GitHub.
4. Click New + on Render and select Static Site.
5. Connect your GitHub repository.
6. Set the following configuration:
   • Name: pollution-frontend
   • Root Directory: Project/Frontend
   • Build Command: (Leave empty)
   • Publish Directory: .
7. Click Create Static Site.