feat: Deploy latest version of Gradio app

README.md

@@ -1,9 +1,8 @@
-title: Predictive Maintenance for Turbofan Engines emoji: ✈️ colorFrom: blue colorTo: indigo sdk: gradio sdk_version: 4.20.0 app_file: app.py pinned: false
-
+title: Predictive Maintenance for Turbofan Engines emoji: ✈️ colorFrom: blue colorTo: indigo sdk: gradio app_file: app.py pinned: false
 Predictive Maintenance for Turbofan Engines
 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 a Hugging Face Space.
+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.
@@ -12,74 +11,13 @@ Automated CI/CD: The model is automatically retrained and the application is red
 
 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
-Backend: Python
-
-ML Model: Scikit-learn (Linear Regression)
-
-Web App: Gradio
-
-Dev Environment: GitHub Codespaces (Docker)
-
-CI/CD & Hosting: GitHub Actions, Hugging Face Spaces
-
-🚀 How to Run Locally
-To run this project on your own machine or Codespace, follow these steps.
-
-Prerequisites
-Python 3.9 or higher
-
-Git
-
-1. Clone the Repository
-git clone [https://github.com/ashandilgith/predictivemaintenance-.git](https://github.com/ashandilgith/predictivemaintenance-.git)
-cd predictivemaintenance-
-
-2. Set Up a Virtual Environment (Recommended)
-# Create a virtual environment
-python3 -m venv venv
-
-# Activate it
-source venv/bin/activate
-
-3. Install Dependencies
-Install all the required Python libraries from the requirements.txt file.
-
-pip install -r requirements.txt
-
-4. Prepare the Data
-Run the script to process the raw dataset. This will create processed_train_data.csv in the data/ directory.
-
-python prepare_data.py
-
-5. Train the Model
-Run the training script to create the model.joblib file.
-
-python train.py
-
-6. Launch the Gradio App
-Run the application file. The app will be available at a local URL shown in your terminal.
-
-python app.py
-
-⚙️ CI/CD Pipeline
-This project uses a GitHub Actions workflow defined in .github/workflows/main.yml. The pipeline automates the following steps on every push to the main branch:
-
-Checkout Code: Clones the repository onto a fresh virtual machine.
-
-Install Dependencies: Installs all necessary libraries.
-
-Prepare Data: Runs the data preparation script.
-
-Train Model: Trains the linear model and creates the model.joblib artifact.
+⚙️ How It Works
+This application is powered by a scikit-learn Linear Regression model trained on the NASA Turbofan Engine Degradation Simulation Data Set.
 
-Deploy to Space: Pushes the entire application, including the newly trained model, to the designated Hugging Face Space, making the updated app live.
+The CI/CD pipeline automates the following steps:
 
-📊 Dataset
-This project uses the Turbofan Engine Degradation Simulation Data Set provided by NASA.
+Prepare Data: Processes the raw dataset.
 
-Source: NASA Prognostics Data Repository
+Train Model: Trains the linear model and creates a model.joblib artifact.
 
-Subset Used: FD001
+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.