README.md

metadata

title: AeroPulse Predictive Maintenance
emoji: ✈️
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false

AeroPulse RUL Prediction Dashboard

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

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

To run this locally:

pip install -r requirements.txt
uvicorn app:app --host 0.0.0.0 --port 8000

Then visit http://127.0.0.1:8000 in your browser.