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| title: Engine Predictive Maintenance App | |
| emoji: "🛠️" | |
| colorFrom: purple | |
| colorTo: pink | |
| sdk: docker | |
| pinned: false | |
| # 🛠️ Smart Engine Predictive Maintenance App | |
| This interactive Streamlit application predicts whether an engine is likely to be **Faulty (1)** or **Normal (0)** using real-time sensor readings. | |
| It is designed to support **preventive maintenance decision-making** by identifying engines at higher risk of failure before breakdown occurs. | |
| --- | |
| ## ✅ Key Features | |
| - **Single Engine Prediction** using manual sensor inputs | |
| - **Probability-based output** for Faulty / Normal (where supported by the model) | |
| - **Feature engineering built-in** (the app automatically computes engineered features to match the training schema) | |
| - **Download engineered input row** as CSV for traceability | |
| - **Bulk CSV Prediction** (upload a CSV and generate batch predictions) | |
| - **Download bulk predictions** directly from the UI | |
| --- | |
| ## 🧠 Model Details | |
| - **Algorithm:** Gradient Boosting Classifier | |
| - **Training Data:** Engine sensor telemetry dataset | |
| - **Target Variable:** `Engine Condition` | |
| - `0 = Normal` | |
| - `1 = Faulty` | |
| **Reference Metrics (from model evaluation):** | |
| - Recall (Faulty): ~0.84 | |
| - ROC-AUC: ~0.70 | |
| - PR-AUC: ~0.80 | |
| --- | |
| ## 🧾 Required Input Features (Single & Bulk) | |
| Your CSV or manual inputs must include **only the raw sensor columns** below: | |
| 1. `Engine rpm` | |
| 2. `Lub oil pressure` | |
| 3. `Fuel pressure` | |
| 4. `Coolant pressure` | |
| 5. `lub oil temp` | |
| 6. `Coolant temp` | |
| The app computes additional engineered features internally (ratios, indices, and warning flags) to align with the model training pipeline. | |
| --- | |
| ## 📦 Bulk Prediction Instructions | |
| 1. Upload a CSV file with the 6 required raw sensor columns listed above. | |
| 2. The app will generate: | |
| - `Predicted_Class` (0/1) | |
| - `Faulty_Probability` (if available) | |
| 3. Download the results using the provided **Download Bulk Predictions CSV** button. | |
| --- | |
| ## 🚀 Deployment | |
| This Space uses a Docker-based deployment with Streamlit running on port **8501**. Hugging Face automatically maps ports during deployment. | |
| --- | |
| ## 🔗 Project Links | |
| - **Model Hub:** `simnid/predictive-maintenance-model` | |
| - **Dataset Hub:** `simnid/predictive-engine-maintenance-dataset` | |
| - **GitHub Repository:** *(add your repo link here once finalized)* | |
| --- | |