| title: Engine Condition Prediction | |
| sdk: docker | |
| # Engine Condition Prediction | |
| This Streamlit application predicts the **engine condition (Normal or Faulty)** using an **XGBoost machine learning model**. | |
| ## Model Details | |
| - **Algorithm**: XGBoost Classifier | |
| - **Model Source**: Hugging Face Model Hub | |
| - **Input Features**: | |
| - Engine rpm | |
| - Lub oil pressure | |
| - Fuel pressure | |
| - Coolant pressure | |
| - lub oil temp | |
| - Coolant temp | |
| ## How It Works | |
| 1. User enters real-time engine sensor values. | |
| 2. The app loads a pre-trained XGBoost model from Hugging Face. | |
| 3. The model predicts the engine condition. | |
| 4. Inputs and predictions are stored in a CSV file for logging. | |
| ## Deployment | |
| - **Framework**: Streamlit | |
| - **Containerized with**: Docker | |
| - **Hosted on**: Hugging Face Spaces | |
| ## Dependencies | |
| All dependencies are defined in `requirements.txt` and installed during Docker build. | |
| --- | |