Upload folder using huggingface_hub

Dockerfile

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+# 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
+
+# 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
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+# Define the command to run the Streamlit app on port "7860" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# ---------------------------------------------------
+# Page config
+# ---------------------------------------------------
+
+st.set_page_config(
+    page_title="Predictive Maintenance Engine Risk Predictor",
+    layout="centered"
+)
+
+# ---------------------------------------------------
+# Load Model from Hugging Face
+# ---------------------------------------------------
+
+REPO_ID = "subratm62/predictive-maintenance"
+MODEL_FILE = "predictive_maintenance_pipeline.joblib"
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id=REPO_ID,
+        filename=MODEL_FILE
+    )
+    return joblib.load(model_path)
+
+model = load_model()
+
+# Classification threshold (your tuned value)
+classification_threshold = 0.50
+
+# ---------------------------------------------------
+# UI Header
+# ---------------------------------------------------
+
+st.title("🔧 Predictive Maintenance — Engine Failure Risk")
+st.write(
+    """
+    Enter live engine sensor readings to estimate **failure risk**.
+    This tool supports proactive maintenance decisions.
+    """
+)
+
+st.markdown("---")
+
+# ---------------------------------------------------
+# Sensor Inputs
+# ---------------------------------------------------
+
+st.subheader("Engine Sensor Inputs")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    engine_rpm = st.number_input(
+        "Engine RPM",
+        min_value=0.0,
+        max_value=3000.0,
+        value=750.0
+    )
+
+    lub_oil_pressure = st.number_input(
+        "Lub Oil Pressure",
+        min_value=0.0,
+        max_value=10.0,
+        value=3.0
+    )
+
+    fuel_pressure = st.number_input(
+        "Fuel Pressure",
+        min_value=0.0,
+        max_value=25.0,
+        value=6.0
+    )
+
+with col2:
+    coolant_pressure = st.number_input(
+        "Coolant Pressure",
+        min_value=0.0,
+        max_value=10.0,
+        value=2.0
+    )
+
+    lub_oil_temp = st.number_input(
+        "Lub Oil Temperature",
+        min_value=60.0,
+        max_value=120.0,
+        value=77.0
+    )
+
+    coolant_temp = st.number_input(
+        "Coolant Temperature",
+        min_value=50.0,
+        max_value=200.0,
+        value=78.0
+    )
+
+st.markdown("---")
+
+# ---------------------------------------------------
+# Prepare input dataframe
+# ---------------------------------------------------
+
+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
+}])
+
+# ---------------------------------------------------
+# Prediction
+# ---------------------------------------------------
+
+if st.button("🔍 Predict Failure Risk"):
+
+    probability = model.predict_proba(input_data)[0, 1]
+    prediction = int(probability >= classification_threshold)
+
+    st.subheader("Prediction Result")
+
+    if prediction == 1:
+        st.error(
+            "⚠ HIGH FAILURE RISK — Maintenance inspection recommended."
+        )
+    else:
+        st.success(
+            "✅ Engine operating within normal range."
+        )
+
+    st.write(f"**Failure Probability:** {probability:.4f}")
+    st.write(f"**Decision Threshold:** {classification_threshold:.2f}")
+
+    # Business interpretation
+    if probability > 0.75:
+        st.warning("Critical condition — immediate inspection advised.")
+    elif probability > 0.50:
+        st.info("Moderate risk — schedule maintenance soon.")
+    else:
+        st.write("Low operational risk detected.")
+
+st.markdown("---")
+
+st.caption(
+    "Model hosted on Hugging Face | Experiment tracking via MLflow | Built with Streamlit"
+)

requirements.txt

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+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