APP_INTERFACE_GUIDE.md

Streamlit App Interface Guide

🚀 Accessing the App

The Streamlit app should now be running. Open your web browser and navigate to:

http://localhost:8501

If port 8501 is busy, Streamlit will automatically use the next available port (8502, 8503, etc.). Check the terminal output for the exact URL.

---

📱 Interface Overview

Main Page Layout

┌─────────────────────────────────────────────────────────┐
│  🛠️ Engine Predictive Maintenance – Failure Prediction  │
├─────────────────────────────────────────────────────────┤
│                                                           │
│  This app predicts whether an engine is operating        │
│  normally (0) or requires maintenance / at risk of      │
│  failure (1) based on real-time sensor readings.        │
│                                                           │
│  Adjust the sensor values below and click Predict to    │
│  see the model's classification and the probability of   │
│  a potential fault.                                       │
│                                                           │
├─────────────────────────────────────────────────────────┤
│  Input Sensor Readings                                   │
├─────────────────────────────────────────────────────────┤
│                                                           │
│  ┌─────────────────────┐  ┌─────────────────────┐     │
│  │ Engine RPM           │  │ Coolant Pressure    │     │
│  │ [800.0]              │  │ [2.0]               │     │
│  │                      │  │                     │     │
│  │ Lub Oil Pressure     │  │ Lub Oil Temperature │     │
│  │ [3.0]                │  │ [80.0]              │     │
│  │                      │  │                     │     │
│  │ Fuel Pressure        │  │ Coolant Temperature │     │
│  │ [10.0]               │  │ [80.0]              │     │
│  └─────────────────────┘  └─────────────────────┘     │
│                                                           │
│              [Predict Button]                            │
│                                                           │
└─────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────┐
│  Sidebar: Model Source                                   │
├─────────────────────────────────────────────────────────┤
│  Load model from:                                        │
│  ○ Hugging Face Hub                                      │
│  ● Local file                                            │
│                                                           │
│  Note: On Hugging Face Spaces, the model is typically   │
│  loaded from the model hub. Locally, you can choose     │
│  either source as long as you have run the training      │
│  pipeline or configured your HF token.                   │
└─────────────────────────────────────────────────────────┘

---

🎯 How to Use the Interface

Step 1: Select Model Source (Sidebar)

• Choose "Local file" if you've run python src/train.py locally
• Choose "Hugging Face Hub" if you've uploaded the model to HF and set HF_TOKEN

Step 2: Enter Sensor Values

Adjust the 6 sensor inputs:

1. Engine RPM (0-4000)

   • Default: 800.0
   • Typical range: 500-2000 RPM

2. Lub Oil Pressure (0-10 bar/kPa)

   • Default: 3.0
   • Typical range: 2-5 bar

3. Fuel Pressure (0-30 bar/kPa)

   • Default: 10.0
   • Typical range: 5-20 bar

4. Coolant Pressure (0-10 bar/kPa)

   • Default: 2.0
   • Typical range: 1-4 bar

5. Lub Oil Temperature (0-150°C)

   • Default: 80.0
   • Typical range: 70-90°C

6. Coolant Temperature (0-150°C)

   • Default: 80.0
   • Typical range: 70-90°C

Step 3: Click "Predict"

After clicking the Predict button, you'll see one of two results:

✅ Normal Operation

✅ The engine is LIKELY OPERATING NORMALLY (probability of fault X.XX%).

🚨 Faulty / Requires Maintenance

🚨 The engine is LIKELY FAULTY / REQUIRES MAINTENANCE (probability XX.XX%).

---

📊 Example Predictions

Example 1: Normal Engine

• Engine RPM: 1200
• Lub Oil Pressure: 3.5
• Fuel Pressure: 12.0
• Coolant Pressure: 2.5
• Lub Oil Temperature: 82.0
• Coolant Temperature: 85.0
• Result: ✅ Normal operation (low fault probability)

Example 2: Faulty Engine

• Engine RPM: 400
• Lub Oil Pressure: 1.5
• Fuel Pressure: 5.0
• Coolant Pressure: 1.0
• Lub Oil Temperature: 95.0
• Coolant Temperature: 100.0
• Result: 🚨 Requires maintenance (high fault probability)

---

🔧 Troubleshooting

App Not Loading?

1. Check terminal for errors
2. Verify port 8501 is available: lsof -ti:8501
3. Try a different port: streamlit run src/app.py --server.port 8502

Model Not Found Error?

1. For Local: Run python src/train.py first to create models/best_model.joblib
2. For HF: Set HF_TOKEN and HF_MODEL_REPO environment variables

Import Errors?

1. Activate virtual environment: source .venv/bin/activate
2. Install dependencies: pip install -r requirements.txt

---

🎨 Interface Features

• Clean, centered layout for easy input
• Two-column form for organized sensor inputs
• Real-time prediction with probability scores
• Color-coded results: Green for normal, Red for faulty
• Sidebar model selection for flexibility
• Responsive design that works on different screen sizes

---

📸 Screenshots for Your Report

When documenting this in your final report, you can:

1. Take a screenshot of the input form
2. Take a screenshot showing a "Normal" prediction
3. Take a screenshot showing a "Faulty" prediction
4. Include the URL: http://localhost:8501 (or your deployed HF Space URL)

---

🚀 Next Steps

1. Test the app locally with different sensor values
2. Deploy to Hugging Face Space using python src/deploy_to_hf.py
3. Include screenshots in your final report/notebook
4. Document the interface in your submission

The app is ready to use! 🎉