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 +1,68 @@
+
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import os
+
+from config import HF_REPO_ID
+
+# Assuming config.py is in the same directory or accessible via PYTHONPATH
+# from config import HF_REPO_ID # If you want to load HF_REPO_ID from config.py
+
+# For now, hardcode the repo ID as it's defined elsewhere in the notebook
+#HF_REPO_ID = "CodingBuddy/Predictive-maintenance"
+
+# Download and load the model
+try:
+    model_path = hf_hub_download(repo_id=HF_REPO_ID, filename="Predictive_maintenance_project_best_model.joblib", repo_type="model")
+    model = joblib.load(model_path)
+    st.success("Model loaded successfully!")
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
+
+# Streamlit UI for Predictive Maintenance
+st.title("Engine Predictive Maintenance App")
+st.write("""
+This application predicts whether an engine requires maintenance based on its sensor readings.
+Please enter the engine sensor data below to get a prediction.
+""")
+
+# User input
+st.header("Engine Sensor Data Input")
+
+Engine_RPM = st.number_input("Engine RPM (Revolutions per Minute)", min_value=0.0, max_value=10000.0, value=700.0, step=1.0)
+Lub_oil_pressure = st.number_input("Lubricating Oil Pressure (bar/kPa)", min_value=0.0, max_value=50.0, value=2.493592, step=0.000001, format="%.6f")
+Fuel_pressure = st.number_input("Fuel Pressure (bar/kPa)", min_value=0.0, max_value=50.0, value=11.790927, step=0.000001, format="%.6f")
+Coolant_pressure = st.number_input("Coolant Pressure (bar/kPa)", min_value=0.0, max_value=50.0, value=3.178981, step=0.000001, format="%.6f")
+lub_oil_temp = st.number_input("Lubricating Oil Temperature (°C)", min_value=0.0, max_value=200.0, value=84.144163, step=0.000001, format="%.6f")
+Coolant_temp = st.number_input("Coolant Temperature (°C)", min_value=0.0, max_value=200.0, value=81.632187, step=0.000001, format="%.6f")
+
+# Assemble input into 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]
+})
+
+
+if st.button("Predict Engine Condition"):
+    try:
+        # Ensure the model is loaded before predicting
+        if 'model' in locals() and model is not None:
+            prediction = model.predict(input_data)[0]
+            # Assuming 0 = Normal, 1 = Requires Maintenance (Faulty)
+            result = "Requires Maintenance (Faulty)" if prediction == 1 else "Operating Normally"
+            st.subheader("Prediction Result:")
+            if prediction == 1:
+                st.error(f"The model predicts: **{result}**")
+            else:
+                st.success(f"The model predicts: **{result}**")
+        else:
+            st.warning("Model is not loaded. Please check the model loading process.")
+    except Exception as e:
+        st.error(f"An error occurred during prediction: {e}")

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