Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,24 @@
-FROM python:3.13.5-slim
+#Creating the dockerfile
+# 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 +1,43 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download the saved model from the Hugging Face model hub
+model_path = hf_hub_download(repo_id="adityasharma0511/predictive-maintenance-model", filename="best_predict_model.joblib")
+
+# Load the saved model from the Hugging Face model hub
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Engine Predictive Maintenance App")
+st.write("Engine Predictive Maintenance App is a tool to predicts whether an engine will fail or not based on the engine health parameters.")
+st.write("Kindly enter the enging parameters.")
+
+# Get the inputs and save them into a dataframe
+Engine_rpm = st.number_input("Engine rpms", min_value=0, max_value=5000, value=500)
+Lub_oil_pressure = st.number_input("Lub oil pressure(in kPa)", min_value=1, max_value=5, value=3)
+Fuel_pressure = st.number_input("Fuel pressure (in kPa)", min_value=0, max_value=100, value=50)
+Coolant_pressure = st.number_input("Coolant pressure (in kPa)", min_value=0, max_value=50, value=1)
+lub_oil_temp = st.number_input("Lub oil temprature (in °C)", min_value=0, max_value=50, value=1)
+Coolant_temp = st.number_input("Coolant temprature (in °C)", min_value=1, max_value=5, value=3)
+
+# Save the inputs into a Dataframe. Convert categorical 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"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    prediction = (prediction_proba >= classification_threshold).astype(int)
+    result = "Fali" if prediction == 1 else "Not Fail"
+    st.write(f"Based on the information provided, the engine is likely to {result}.")

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+#Creating the dependency file
+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