Upload folder using huggingface_hub

Dockerfile

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+# 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
+
+# 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
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+# 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

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+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the trained model
+model_path = hf_hub_download(repo_id="Ankurkamboj21/Enginedataset1", filename="best_Model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI
+st.title("Ankur Predictive Maintenance")
+st.write("""
+This application predicts Engine Condition.
+""")
+
+# User input
+engine_rpm=st.number_input("Engine rpm")
+lub_oil_pressure=st.number_input("Lub oil pressure")
+fuel_pressure=st.number_input("Fuel pressure")
+coolant_pressure=st.number_input("Coolant pressure")
+lub_oil_temp=st.number_input("lub oil temp")
+coolant_temp=st.number_input("Coolant temp")
+
+
+# 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
+
+}])
+
+# Predict button
+if st.button("Submit"):
+    prediction = model.predict(input_data)[0]
+    results="Engine Condition is Good" if prediction==1 else "Engine Condition is not Good"
+    st.subheader("Prediction Result:")
+    st.success(f"Estimated Ad Revenue: {results}")

requirements.txt

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+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