Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
+# Set the working directory inside the container to /app
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Define the command to run the Streamlit app on port "8501" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,177 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="JohnsonSAimlarge/engine-failure-predictor", filename="engine_failure_model.joblib")
+model = joblib.load(model_path)
+
+# ------------------------------
+# Streamlit UI
+# ------------------------------
+st.title("🔧 Engine Failure Prediction System")
+st.write("""
+This application predicts the likelihood of engine failure based on sensor readings and operational parameters.
+Please enter **Engine Sensor Data** below to get a prediction.
+""")
+
+# ------------------------------
+# User Inputs
+# ------------------------------
+st.subheader("Engine Operational Parameters")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    engine_rpm = st.number_input(
+        "Engine RPM (Revolutions Per Minute)", 
+        min_value=0, 
+        max_value=10000, 
+        value=3000,
+        help="Normal range: 500-8000 RPM"
+    )
+    
+    lub_oil_pressure = st.number_input(
+        "Lubricating Oil Pressure (bar)", 
+        min_value=0.0, 
+        max_value=10.0, 
+        value=4.5,
+        step=0.1,
+        help="Normal range: 2.0-6.0 bar"
+    )
+    
+    fuel_pressure = st.number_input(
+        "Fuel Pressure (bar)", 
+        min_value=0.0, 
+        max_value=10.0, 
+        value=4.0,
+        step=0.1,
+        help="Normal range: 2.0-6.0 bar"
+    )
+
+with col2:
+    coolant_pressure = st.number_input(
+        "Coolant Pressure (bar)", 
+        min_value=0.0, 
+        max_value=5.0, 
+        value=2.5,
+        step=0.1,
+        help="Normal range: 1.5-3.5 bar"
+    )
+    
+    lub_oil_temp = st.number_input(
+        "Lubricating Oil Temperature (°C)", 
+        min_value=0, 
+        max_value=200, 
+        value=75,
+        help="Normal range: 50-120°C"
+    )
+    
+    coolant_temp = st.number_input(
+        "Coolant Temperature (°C)", 
+        min_value=0, 
+        max_value=150, 
+        value=80,
+        help="Normal range: 60-100°C"
+    )
+
+# ------------------------------
+# Prepare Input for Prediction
+# ------------------------------
+input_data = {
+    "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
+}
+
+input_df = pd.DataFrame([input_data])
+
+# Display input summary
+st.subheader("Input Summary")
+st.dataframe(input_df, use_container_width=True)
+
+# ------------------------------
+# Prediction
+# ------------------------------
+if st.button("🔍 Predict Engine Condition", type="primary"):
+    try:
+        prediction = model.predict(input_df)[0]
+        probability = model.predict_proba(input_df)[0][1]
+        
+        # Use custom threshold for imbalanced dataset
+        # Adjust based on your model's optimal threshold
+        classification_threshold = 0.5
+        prediction = (probability >= classification_threshold).astype(int)
+        
+        st.markdown("---")
+        st.subheader("Prediction Results")
+        
+        if prediction == 1:
+            st.error(f"⚠️ **ENGINE FAILURE PREDICTED** - Immediate maintenance required!")
+            st.error(f"**Failure Probability: {probability:.2%}**")
+            st.warning("""
+            **Recommended Actions:**
+            - Stop engine operation immediately
+            - Conduct thorough inspection
+            - Check all sensor readings
+            - Consult maintenance team
+            """)
+        else:
+            st.success(f"✅ **ENGINE CONDITION NORMAL** - No immediate action required")
+            st.success(f"**Failure Probability: {probability:.2%}**")
+            st.info("""
+            **Maintenance Recommendations:**
+            - Continue regular monitoring
+            - Schedule routine maintenance as planned
+            - Keep monitoring sensor readings
+            """)
+        
+        # Display confidence meter
+        st.subheader("Confidence Level")
+        confidence = max(probability, 1 - probability)
+        st.progress(confidence)
+        st.write(f"Model Confidence: {confidence:.2%}")
+        
+    except Exception as e:
+        st.error(f"Error during prediction: {str(e)}")
+        st.info("Please check your input values and try again.")
+
+# ------------------------------
+# Additional Information
+# ------------------------------
+with st.expander("ℹ️ About This Model"):
+    st.write("""
+    **Model Information:**
+    - Algorithm: XGBoost with SMOTE for class balancing
+    - Test Accuracy: 64.42%
+    - Precision: 76.42%
+    - Recall: 63.01%
+    - Dataset: 19,535 engine records
+    
+    **Most Important Features:**
+    1. Engine RPM (38.3%)
+    2. Fuel Pressure (16.2%)
+    3. Oil Temperature (13.7%)
+    
+    **Model Repository:** [JohnsonSAimlarge/engine-failure-predictor](https://huggingface.co/JohnsonSAimlarge/engine-failure-predictor)
+    """)
+
+with st.expander("📊 Feature Ranges & Guidelines"):
+    st.write("""
+    | Parameter | Normal Range | Critical Threshold |
+    |-----------|--------------|-------------------|
+    | Engine RPM | 500-8000 | >8000 or <500 |
+    | Lub Oil Pressure | 2.0-6.0 bar | <2.0 or >6.0 |
+    | Fuel Pressure | 2.0-6.0 bar | <2.0 or >6.0 |
+    | Coolant Pressure | 1.5-3.5 bar | <1.5 or >3.5 |
+    | Lub Oil Temp | 50-120°C | >120°C |
+    | Coolant Temp | 60-100°C | >100°C |
+    """)
+
+# Footer
+st.markdown("---")
+st.caption("Engine Failure Prediction System | Powered by XGBoost & Hugging Face")

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==2.1.4
+mlflow==3.0.1