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Dockerfile

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+
+# Use the official lightweight Python image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Install system dependencies for XGBoost/Scikit-learn
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy the requirements file and install Python libraries
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Expose the port Streamlit runs on (Hugging Face default is 7860)
+EXPOSE 7860
+
+# Command to run the app
+CMD ["streamlit", "run", "app.py", "--server.port", "7860", "--server.address", "0.0.0.0"]

app.py

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+
+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# 1. Load the model from Hugging Face Hub
+# Replace with your actual repo and filename if different
+REPO_ID = "P-Mishra/engine-predictive-maintenance"
+FILENAME = "rf_predictive_maintenance.pkl"
+
+try:
+    model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+    model = joblib.load(model_path)
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+
+st.title("🚢 Engine Predictive Maintenance")
+st.write("Professional Monitoring System for Engine Health")
+
+# 2. Input Fields
+col1, col2 = st.columns(2)
+with col1:
+    engine_rpm = st.number_input("Engine RPM", value=1000)
+    lub_oil_temp = st.number_input("Lubricating Oil Temp", value=80.0)
+    fuel_pressure = st.number_input("Fuel Pressure", value=5.0)
+with col2:
+    coolant_temp = st.number_input("Coolant Temp", value=75.0)
+    coolant_pressure = st.number_input("Coolant Pressure", value=3.0)
+
+# 3. Feature Engineering (must match your notebook logic exactly)
+eps = 1e-6
+coolant_interaction = coolant_temp * coolant_pressure
+coolant_ratio = coolant_temp / (coolant_pressure + eps)
+oil_rpm_interaction = lub_oil_temp * engine_rpm
+fuel_rpm_ratio = fuel_pressure / (engine_rpm + eps)
+
+# 4. Prediction
+input_df = pd.DataFrame([{
+    'engine_rpm': engine_rpm,
+    'lub_oil_temp': lub_oil_temp,
+    'fuel_pressure': fuel_pressure,
+    'coolant_temp': coolant_temp,
+    'coolant_pressure': coolant_pressure,
+    'coolant_temp_pressure_interaction': coolant_interaction,
+    'coolant_temp_pressure_ratio': coolant_ratio,
+    'lub_oil_temp_engine_rpm_interaction': oil_rpm_interaction,
+    'fuel_pressure_engine_rpm_ratio': fuel_rpm_ratio
+}])
+
+if st.button("Analyze Engine Condition"):
+    prediction = model.predict(input_df)
+    if prediction[0] == 1:
+        st.error("🚨 CRITICAL: Maintenance Required Immediately")
+    else:
+        st.success("✅ NORMAL: Engine operating within safe parameters")
+    

requirements.txt

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+
+streamlit
+pandas
+numpy
+joblib
+huggingface_hub
+scikit-learn
+xgboost