Deploy Engine Condition Predictor

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y     gcc     g++     && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements
+COPY requirements.txt .
+
+# Install Python packages
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+# Copy app
+COPY streamlit_app.py .
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "streamlit_app.py", "--server.port=7860", "--server.address=0.0.0.0"]

README.md

@@ -1,10 +1,9 @@
 ---
-title: Engine Maintenance App
-emoji: 📊
-colorFrom: yellow
-colorTo: red
+title: Engine Condition App
+emoji: "🚧"
+colorFrom: indigo
+colorTo: blue
 sdk: docker
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This Space runs a Streamlit app that predicts engine condition from sensor data.

requirements.txt

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+streamlit==1.39.0
+pandas==2.2.2
+numpy==1.26.4
+scikit-learn==1.5.0
+joblib==1.4.2
+huggingface_hub==0.25.1

streamlit_app.py

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+
+import streamlit as st
+import joblib
+import pandas as pd
+import numpy as np
+from huggingface_hub import hf_hub_download
+import os
+
+# Configuration
+HF_MODEL_REPO = os.getenv("HF_MODEL_REPO", "dhani10/engine-maintenance-model")
+MODEL_FILE = os.getenv("MODEL_FILE", "best_engine_model.joblib")
+
+# Expected features (match your training data exactly)
+EXPECTED_COLS = [
+    'Engine rpm', 'Lub oil pressure', 'Fuel pressure',
+    'Coolant pressure', 'lub oil temp', 'Coolant temp'
+]
+
+@st.cache_resource
+def load_model():
+    """Load the model from Hugging Face Hub"""
+    try:
+        model_path = hf_hub_download(
+            repo_id=HF_MODEL_REPO,
+            filename=MODEL_FILE,
+            repo_type="model",
+            token=os.getenv("HF_TOKEN")
+        )
+        model = joblib.load(model_path)
+        st.success("Model loaded successfully!")
+        return model
+    except Exception as e:
+        st.error(f"Failed to load model: {e}")
+        return None
+
+def main():
+    st.set_page_config(
+        page_title="Engine Condition Predictor",
+        layout="centered",
+        page_icon="🚧"
+    )
+
+    st.title("Predictive Maintenance — Engine Condition")
+    st.markdown("Monitor engine health using real-time sensor data")
+    st.caption(f"Model: {HF_MODEL_REPO}")
+
+    # Load model
+    with st.spinner("Loading AI model..."):
+        model = load_model()
+
+    if model is None:
+        st.stop()
+
+    # Input form
+    st.header("🔧 Engine Sensor Readings")
+
+    with st.form("prediction_form"):
+        col1, col2 = st.columns(2)
+
+        with col1:
+            engine_rpm = st.slider("Engine RPM", 100, 2500, 1200)
+            lub_oil_pressure = st.slider("Lub Oil Pressure (bar)", 0.5, 7.0, 3.0, 0.1)
+            fuel_pressure = st.slider("Fuel Pressure (bar)", 0.5, 20.0, 6.0, 0.1)
+
+        with col2:
+            coolant_pressure = st.slider("Coolant Pressure (bar)", 0.5, 7.0, 2.0, 0.1)
+            lub_oil_temp = st.slider("Lub Oil Temp (°C)", 70.0, 110.0, 80.0, 0.1)
+            coolant_temp = st.slider("Coolant Temp (°C)", 60.0, 100.0, 75.0, 0.1)
+
+        submitted = st.form_submit_button("Analyze Engine Condition", type="primary")
+
+    if submitted:
+        # Create input data with EXACT column names from training
+        input_data = pd.DataFrame([{
+            'Engine rpm': engine_rpm,
+            'Lub oil pressure': lub_oil_pressure,
+            'Fuel pressure': fuel_pressure,
+            'Coolant pressure': coolant_pressure,
+            'lub oil temp': lub_oil_temp,
+            'Coolant temp': coolant_temp
+        }])
+
+        try:
+            # Make prediction
+            prediction = model.predict(input_data)[0]
+            probability = model.predict_proba(input_data)[0]
+
+            # Display results
+            st.header("Analysis Results")
+
+            if prediction == 1:
+                st.error("**FAULTY ENGINE DETECTED**")
+                st.progress(probability[1])
+                st.warning(f"**Risk Probability:** {probability[1]*100:.1f}%")
+                st.markdown("""
+                **Recommended Actions:**
+                - Schedule immediate maintenance
+                - Inspect lubrication system
+                - Check cooling system
+                """)
+            else:
+                st.success("**ENGINE OPERATING NORMALLY**")
+                st.progress(probability[0])
+                st.info(f"**Health Score:** {probability[0]*100:.1f}%")
+                st.markdown("""
+                **Status:** Continue routine monitoring
+                **Next maintenance:** As scheduled
+                """)
+
+            # Show input data
+            with st.expander("View Input Data"):
+                st.dataframe(input_data)
+
+        except Exception as e:
+            st.error(f"Prediction error: {str(e)}")
+            st.info("Please check that the model expects the correct feature names")
+
+if __name__ == "__main__":
+    main()