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 +1,49 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download the model from the Model Hub
+model_path = hf_hub_download(repo_id="csankaran3/engine-condition-prediction", filename="best_engine_condition_prediction_model_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.set_page_config(page_title="Predictive Maintenance", layout="centered")
+st.title("Predictive Maintenance - Engine fault prediction application")
+st.write("This App is an internal tool for automobie companies to predict engine condition (Active / Faulty) based on the sensor values.")
+st.subheader("Kindly enter the sensor details to check whether engine condition is active or faulty.")
+
+# Setting the minimum value and distplay value - Used min and average from the dataset for displaying values
+engine_rpm = st.number_input("Engine RPM", min_value=61.0, value=1150.0, step=10.0)
+lub_oil_pressure = st.number_input("Lub Oil Pressure (kPa)", min_value=0.0, value=3.63, step=0.01)
+fuel_pressure = st.number_input("Fuel Pressure (kPa)", min_value=0.0, value=10.57, step=0.01)
+coolant_pressure = st.number_input("Coolant Pressure (kPa)", min_value=0.0, value=7.48, step=0.01)
+lub_oil_temp = st.number_input("Lub Oil Temperature (°C)", min_value=71.32, value=89.58, step=0.01)
+coolant_temp = st.number_input("Coolant Temperature (°C)", min_value=61.67, value=128.60, step=0.01)
+
+
+# Convert inputs to match 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
+}])
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+# Predict button
+if st.button("Predict Engine Condition"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    prediction = (prediction_proba >= classification_threshold).astype(int)
+    result = "Active" if prediction == 1 else "Faulty"
+    if (result == "Active"):
+        st.success(f"Engine condition prediction completed!.. The Engine condition is {result}.")
+    else:
+        st.error(f"Engine condition prediction completed!.. The Engine condition is {result}.")
+

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