| --- |
| title: AeroPulse Predictive Maintenance |
| emoji: ✈️ |
| colorFrom: blue |
| colorTo: indigo |
| sdk: docker |
| pinned: false |
| --- |
| |
| # AeroPulse RUL Prediction Dashboard |
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| This is a FastAPI application that serves a PyTorch LSTM model predicting the Remaining Useful Life (RUL) of turbofan engines using the NASA CMAPSS dataset. |
|
|
| ## How it works |
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| 1. **Backend**: A FastAPI server running via Uvicorn. |
| 2. **Model**: A trained PyTorch LSTM model (`lstm_model.pth`) and a Scikit-Learn scaler (`scaler.pkl`). |
| 3. **Data**: The application uses `test_FD001.txt` as a simulated data stream to provide predictions. |
| 4. **Frontend**: An interactive web dashboard (`index.html`) using Chart.js to visualize the sensor readings and RUL predictions. |
|
|
| ## Local Deployment |
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|
| To run this locally: |
|
|
| ```bash |
| pip install -r requirements.txt |
| uvicorn app:app --host 0.0.0.0 --port 8000 |
| ``` |
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|
| Then visit `http://127.0.0.1:8000` in your browser. |
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|
