Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,28 @@
-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
+# Create a non-root user 'user' with UID 1000 and set ownership.
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Change working directory to the user's application directory.
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Copy the application files to the user's directory.
+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.
+# --server.address=0.0.0.0 makes the app reachable from any IP address.
+# --server.enableXsrfProtection=false disables CSRF protection, often needed for public deployments.
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,96 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the best engine model from Hugging Face Model Hub.
+# The model is a scikit-learn compatible estimator saved with joblib.
+model_path = hf_hub_download(
+    repo_id='Garg06/Predictive-Maintenance-Model',
+    filename='best_engine_model.joblib'
+)
+model = joblib.load(model_path)
+
+# ── App header ────────────────────────────────────────────────────────────
+st.title('Engine Predictive Maintenance App')
+st.write("""
+This application predicts whether an engine is **Faulty** or **Normal** based on
+real-time sensor readings. Enter the current sensor values below to get a prediction.
+""")
+
+# ── Sensor input fields ───────────────────────────────────────────────────
+st.header('Engine Sensor Readings')
+
+# Engine RPM: observed range 61–2239, typical idle around 750–800 RPM.
+engine_rpm = st.number_input(
+    'Engine RPM',
+    min_value=0.0, max_value=2500.0, value=800.0, step=1.0,
+    help='Engine revolutions per minute. Typical idle: ~750 RPM.'
+)
+
+# Lub oil pressure: observed range 0–7.27 bar.
+lub_oil_pressure = st.number_input(
+    'Lub Oil Pressure (bar)',
+    min_value=0.0, max_value=10.0, value=3.3, step=0.01,
+    help='Lubrication oil pressure in bar. Healthy range: 2.5–4.5 bar.'
+)
+
+# Fuel pressure: observed range 0–21.14 bar.
+fuel_pressure = st.number_input(
+    'Fuel Pressure (bar)',
+    min_value=0.0, max_value=25.0, value=6.7, step=0.01,
+    help='Fuel system pressure in bar. Healthy range: 4.9–7.7 bar.'
+)
+
+# Coolant pressure: observed range 0–7.48 bar.
+coolant_pressure = st.number_input(
+    'Coolant Pressure (bar)',
+    min_value=0.0, max_value=10.0, value=2.3, step=0.01,
+    help='Engine coolant system pressure in bar. Healthy range: 1.6–2.8 bar.'
+)
+
+# Lub oil temperature: observed range 71–90 °C.
+lub_oil_temp = st.number_input(
+    'Lub Oil Temperature (°C)',
+    min_value=60.0, max_value=100.0, value=77.6, step=0.1,
+    help='Lubrication oil temperature in °C. Normal operating range: 75–80 °C.'
+)
+
+# Coolant temperature: observed range 62–196 °C.
+coolant_temp = st.number_input(
+    'Coolant Temperature (°C)',
+    min_value=60.0, max_value=200.0, value=78.4, step=0.1,
+    help='Engine coolant temperature in °C. Normal range: 74–83 °C.'
+)
+
+# ── Assemble input dataframe ──────────────────────────────────────────────
+# Column names must exactly match those used during model 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,
+}])
+
+# ── Prediction ────────────────────────────────────────────────────────────
+if st.button('Predict Engine Condition'):
+    prediction = model.predict(input_data)[0]
+    probability = model.predict_proba(input_data)[0]
+
+    if prediction == 1:
+        st.error(
+            f'⚠️ **Faulty Engine Detected!**  '
+            f'Confidence: {probability[1]:.1%}  '
+            f'\n\nImmediate inspection is recommended to prevent breakdown.'
+        )
+    else:
+        st.success(
+            f'✅ **Engine is Normal.**  '
+            f'Confidence: {probability[0]:.1%}  '
+            f'\n\nAll sensor readings are within acceptable limits.'
+        )
+
+    st.subheader('Input Summary')
+    st.dataframe(input_data)

requirements.txt

@@ -1,3 +1,10 @@
-altair
-pandas
-streamlit
+# Define the Python package dependencies for the Streamlit application.
+# These versions ensure reproducibility of the deployment environment.
+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
+imbalanced-learn==0.12.4
+mlflow==3.0.1