raghavvram/temp-predict-web-app repository overview

⁠Surface Temperature Analysis

🌍 Earth Surface Temperature Prediction Web App (Dockerized Flask Deployment)

This Docker image packages a full-featured Flask web application designed to predict Earth’s surface temperature using a pre-trained deep learning model built with Keras. The app offers a seamless, mobile-first user experience and is ideal for educational purposes, data science demonstrations, and quick deployment in climate-related research contexts.

🔧 Key Components Inside the Image:

• Flask Web Server: Serves a responsive and intuitive interface for user interaction.
• Keras Prediction Model (best_model.keras): Deep learning model trained to estimate surface temperatures based on input climate parameters.
• MinMax Scalers (scaler_x.pkl, scaler_y.pkl): Ensure consistent input preprocessing and accurate inverse-transformed outputs.
• Theme Toggle Support: Includes frontend logic for light/dark mode switching via JavaScript.
• HTML Templates: Clean, mobile-responsive pages for user input and result display (index.html, response.html).

🚀 Why Use This Image?

• No Setup Hassle: Just pull and run the container to get the Flask app live instantly.
• Pre-Configured Environment: Includes all necessary dependencies and model files.
• Streamlined ML Integration: Ideal for demonstrating the deployment of machine learning models in production-ready web applications.
• Scalable & Portable: Easily extendable for larger datasets, alternate models, or production hosting.

🔍 Intended Use Cases:

• Educational projects or workshops in climate science and machine learning.
• Quick ML app prototyping and container-based deployment.
• Demonstration of full-stack ML workflows using Flask and Docker.

🛠 Getting Started:

# Run the container
docker run --name Flask-Web-App -p 9595:5000 raghavvram/temp-predict-web-app

Once running, you can access the app at http://localhost:9595 in your browser.

Authors - OZA ASHWIN, RUTHWIK SAI, ANUSHA T, RAGHAVVRAM J