Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,23 @@
+# 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 "8501" and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,103 @@
+import streamlit as st
+import numpy as np
+import joblib
+
+# ----------------------------------------------------
+# Load trained model + scaler
+# ----------------------------------------------------
+MODEL_PATH = "model.joblib"        # change if different
+SCALER_PATH = "scaler.joblib"      # optional
+
+model = joblib.load(MODEL_PATH)
+
+try:
+    scaler = joblib.load(SCALER_PATH)
+    use_scaler = True
+except:
+    use_scaler = False
+
+
+# ----------------------------------------------------
+# Page Config
+# ----------------------------------------------------
+st.set_page_config(
+    page_title="Engine Health Prediction",
+    layout="centered"
+)
+
+st.title("🛠️ Predictive Maintenance – Engine Health")
+st.markdown("Enter live sensor values to predict engine condition.")
+
+st.divider()
+
+# ----------------------------------------------------
+# Sidebar Inputs
+# ----------------------------------------------------
+st.sidebar.header("Sensor Inputs")
+
+engine_rpm = st.sidebar.number_input("Engine RPM", min_value=0.0, value=1500.0)
+
+lub_oil_pressure = st.sidebar.number_input(
+    "Lub Oil Pressure (bar/kPa)", min_value=0.0, value=3.5
+)
+
+fuel_pressure = st.sidebar.number_input(
+    "Fuel Pressure (bar/kPa)", min_value=0.0, value=4.0
+)
+
+coolant_pressure = st.sidebar.number_input(
+    "Coolant Pressure (bar/kPa)", min_value=0.0, value=2.0
+)
+
+lub_oil_temp = st.sidebar.number_input(
+    "Lub Oil Temperature (°C)", min_value=0.0, value=80.0
+)
+
+coolant_temp = st.sidebar.number_input(
+    "Coolant Temperature (°C)", min_value=0.0, value=75.0
+)
+
+# ----------------------------------------------------
+# Prediction
+# ----------------------------------------------------
+if st.button("Predict Engine Condition"):
+
+    input_data = np.array([[
+        engine_rpm,
+        lub_oil_pressure,
+        fuel_pressure,
+        coolant_pressure,
+        lub_oil_temp,
+        coolant_temp
+
+    ]])
+
+    if use_scaler:
+        input_data = scaler.transform(input_data)
+
+    prediction = model.predict(input_data)[0]
+
+    st.subheader("Prediction Result")
+
+    if prediction == 0:
+        st.success("Engine Condition: NORMAL")
+    else:
+        st.error("Engine Condition: FAULTY / AT RISK")
+
+    st.divider()
+
+    st.write("### Input Summary")
+    st.table({
+        "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],
+    })
+
+# ----------------------------------------------------
+# Footer
+# ----------------------------------------------------
+st.markdown("---")
+st.caption("Predictive Maintenance Dashboard | Built with Streamlit")

requirements.txt

@@ -0,0 +1,7 @@
+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==2.13.0