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 +1,100 @@
+import streamlit as st
+import pandas as pd
+import joblib
+import os
+from huggingface_hub import hf_hub_download
+
+# ==========================================
+# 1. Page Configuration & Model Loading
+# ==========================================
+st.set_page_config(page_title="Engine Predictive Maintenance", layout="centered")
+
+# Configuration from previous training steps
+REPO_ID = "dpanchali/predictive_maintenance_model"
+FILENAME = "predictive_maintenance_model.joblib"
+
+@st.cache_resource
+def load_model():
+    """Download and load the model from Hugging Face Hub."""
+    try:
+        # Download the model file from the repository
+        model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+        model = joblib.load(model_path)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model from Hugging Face: {e}")
+        return None
+
+# Load the model
+model = load_model()
+
+# ==========================================
+# 2. UI Layout
+# ==========================================
+st.title("Engine Condition Predictor")
+st.markdown("""
+This application uses a trained **XGBoost** model to predict the health of an engine
+based on real-time sensor data.
+""")
+
+st.header("Input Engine Sensor Data")
+st.info("Enter the current readings from the engine sensors below:")
+
+# Creating columns for a cleaner layout
+col1, col2 = st.columns(2)
+
+with col1:
+    engine_rpm = st.number_input("Engine RPM", min_value=0, max_value=10000, value=700, step=10)
+    lub_oil_pressure = st.number_input("Lub Oil Pressure (bar)", min_value=0.0, max_value=20.0, value=2.5, format="%.4f")
+    fuel_pressure = st.number_input("Fuel Pressure (bar)", min_value=0.0, max_value=50.0, value=11.8, format="%.4f")
+
+with col2:
+    coolant_pressure = st.number_input("Coolant Pressure (bar)", min_value=0.0, max_value=20.0, value=3.2, format="%.4f")
+    lub_oil_temp = st.number_input("Lub Oil Temp (°C)", min_value=0.0, max_value=200.0, value=84.1, format="%.4f")
+    coolant_temp = st.number_input("Coolant Temp (°C)", min_value=0.0, max_value=200.0, value=81.6, format="%.4f")
+
+# ==========================================
+# 3. Prediction Logic
+# ==========================================
+if st.button("Predict Engine Condition", type="primary"):
+    if model is not None:
+        # Prepare input data in the same format as 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
+        }])
+
+        # Perform prediction
+        prediction = model.predict(input_data)[0]
+        prediction_proba = model.predict_proba(input_data)[0]
+
+        # Display results
+        st.divider()
+        st.subheader("Results")
+
+        if prediction == 1:
+            st.success("**Prediction: Engine is in Good Condition**")
+        else:
+            st.error("**Prediction: Maintenance Required (Potential Fault Detected)**")
+
+        # Display confidence scores
+        st.write(f"**Confidence Score:** {max(prediction_proba)*100:.2f}%")
+
+        # Optional: Display gauge or progress bar for health
+        health_score = prediction_proba[1] # Probability of class 1 (Good)
+        st.progress(health_score)
+    else:
+        st.warning("Model is not loaded. Please check your Hugging Face credentials and Repo ID.")
+
+# ==========================================
+# 4. Footer
+# ==========================================
+st.sidebar.markdown("### Model Information")
+st.sidebar.text(f"Repo: {REPO_ID}")
+st.sidebar.text(f"File: {FILENAME}")
+st.sidebar.markdown("---")
+st.sidebar.write("This tool is intended for predictive maintenance scheduling based on sensor patterns.")

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