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| title: PiranaWare Engine Diagnostics | |
| emoji: 🛠️ | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 4.29.0 | |
| app_file: app.py | |
| pinned: false | |
| # PiranaWare: Predictive Engine Diagnostics | |
| This is an MVP of a predictive maintenance system for engines. It uses a regression-based AI model (XGBoost) to predict the normal vibration of an engine based on its current operating conditions. | |
| ## How to Use This Demo | |
| 1. **Set Operating Conditions:** Use the sliders and dropdowns for input the engine's current RPM, the ambient temperature, fuel level, and sea state. | |
| 2. **Enter Actual Vibration:** In the final box, enter the vibration reading you would get from a real-world sensor. | |
| 3. **Get Prediction:** The model will compare the predicted "normal" vibration to the actual value and determine if it's an anomaly. | |