feat: Deploy latest version of Gradio app

README.md

@@ -1,23 +1,49 @@
-title: Predictive Maintenance for Turbofan Engines emoji: ✈️ colorFrom: blue colorTo: indigo sdk: gradio app_file: app.py pinned: false
-Predictive Maintenance for Turbofan Engines
+---
+title: Predictive Maintenance for Turbofan Engines
+emoji: ✈️
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.25.0
+app_file: app.py
+pinned: false
+---
+
+# Predictive Maintenance for Turbofan Engines
+
+[![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/ashandilgith/predictive_maintenance)
+[![CI/CD Status](https://github.com/ashandilgith/predictivemaintenance-/actions/workflows/main.yml/badge.svg)](https://github.com/ashandilgith/predictivemaintenance-/actions)
+
 A complete MLOps project demonstrating an end-to-end workflow for a predictive maintenance solution. This application uses a machine learning model to predict the Remaining Useful Life (RUL) of a turbofan engine based on operational settings and sensor data.
 
 The project is developed within a containerized GitHub Codespaces environment and features a CI/CD pipeline that automatically trains the model and deploys the application to this Hugging Face Space.
 
-✨ Features
-Interactive Demo: A user-friendly Gradio web interface to get real-time RUL predictions.
+## ✨ Features
+
+- **Interactive Demo:** A user-friendly Gradio web interface to get real-time RUL predictions.
+- **Automated CI/CD:** The model is automatically retrained and the application is redeployed on every push to the `main` branch using GitHub Actions.
+- **Reproducible Environment:** A defined development environment using Codespaces ensures that the project can be run consistently by anyone.
+- **Extensible Framework:** While this demo uses a turbofan engine dataset, the principles can be customized for any machinery that relies on sensor data to predict performance or potential faults.
+
+## 🛠️ Technology Stack
 
-Automated CI/CD: The model is automatically retrained and the application is redeployed on every push to the main branch using GitHub Actions.
+- **Backend:** Python
+- **ML Model:** Scikit-learn (Linear Regression)
+- **Web App:** Gradio
+- **Dev Environment:** GitHub Codespaces (Docker)
+- **CI/CD & Hosting:** GitHub Actions, Hugging Face Spaces
 
-Reproducible Environment: A defined development environment using Codespaces ensures that the project can be run consistently by anyone.
+## 🚀 How to Run Locally
 
-⚙️ How It Works
-This application is powered by a scikit-learn Linear Regression model trained on the NASA Turbofan Engine Degradation Simulation Data Set.
+To run this project on your own machine or Codespace, follow these steps.
 
-The CI/CD pipeline automates the following steps:
+### Prerequisites
 
-Prepare Data: Processes the raw dataset.
+- Python 3.9 or higher
+- Git
 
-Train Model: Trains the linear model and creates a model.joblib artifact.
+### 1. Clone the Repository
 
-Deploy to Space: Pushes the entire application, including the newly trained model and this README, to this Hugging Face Space to make the app live.
+```bash
+git clone [https://github.com/ashandilgith/predictivemaintenance-.git](https://github.com/ashandilgith/predictivemaintenance-.git)
+cd predictivemaintenance-