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 model
+model_path = hf_hub_download(repo_id="sindhoorasuresh/ML-Project", filename="best_engine_failure_model_v1.joblib")
+model = joblib.load(model_path)
+
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Engine Failure Prediction App")
+st.write("""
+This application predicts the likelihood of a machine failing based on its operational parameters.
+Please enter the sensor and configuration data below to get a prediction.
+""")
+
+# User input
+engine_rpm = st.number_input("Engine rpm)", min_value=250.0, max_value=400.0, value=298.0, step=0.1)
+lub_oil_pres = st.number_input("Lub oil pressure", min_value=250.0, max_value=500.0, value=324.0, step=0.1)
+fuel_pres= st.number_input("Fuel pressure", min_value=0, max_value=3000, value=1400)
+coolant_pres = st.number_input("Coolant pressure", min_value=0.0, max_value=100.0, value=40.0, step=0.1)
+lub_oil_temp = st.number_input("Lub oil temp", min_value=0, max_value=300, value=10)
+coolant_temp = st.number_input("Coolant temp", min_value=0, max_value=300, value=10)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Engine rpm': engine_rpm,
+    'Lub oil pressure': lub_oil_pres,
+    'Fuel pressure': fuel_pres,
+    'Coolant pressure': coolant_pres,
+    'Lub oil temp': lub_oil_temp,
+    'Coolant temp': coolant_temp
+}])
+
+
+if st.button("Predict Failure"):
+    prediction = model.predict(input_data)[0]
+    result = "Engine Failure" if prediction == 1 else "No Failure"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

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