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 the model from the Model Hub
+model_path = hf_hub_download(repo_id="Anusha3/ab_predictive_maintenance", filename="Gradient_Boosting.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Predictive Maintence Prediction
+st.set_page_config(page_title="Predictive Maintenance - Engine Failure")
+st.title("Aircraft Engine Predictive Maintenance")
+
+st.write("Enter sensor readings below to predict engine failure probability.")
+
+# ----------------------------
+# Input Features (Based on Engine Dataset)
+# ----------------------------
+
+operational_setting_1 = st.number_input("Operational Setting 1", value=0.0)
+operational_setting_2 = st.number_input("Operational Setting 2", value=0.0)
+operational_setting_3 = st.number_input("Operational Setting 3", value=0.0)
+
+sensor_1 = st.number_input("Sensor Measurement 1", value=0.0)
+sensor_2 = st.number_input("Sensor Measurement 2", value=0.0)
+sensor_3 = st.number_input("Sensor Measurement 3", value=0.0)
+sensor_4 = st.number_input("Sensor Measurement 4", value=0.0)
+sensor_5 = st.number_input("Sensor Measurement 5", value=0.0)
+sensor_6 = st.number_input("Sensor Measurement 6", value=0.0)
+sensor_7 = st.number_input("Sensor Measurement 7", value=0.0)
+sensor_8 = st.number_input("Sensor Measurement 8", value=0.0)
+sensor_9 = st.number_input("Sensor Measurement 9", value=0.0)
+sensor_10 = st.number_input("Sensor Measurement 10", value=0.0)
+
+# ----------------------------
+# Prepare Input DataFrame
+# ----------------------------
+
+input_data = pd.DataFrame([{
+    "operational_setting_1": operational_setting_1,
+    "operational_setting_2": operational_setting_2,
+    "operational_setting_3": operational_setting_3,
+    "sensor_1": sensor_1,
+    "sensor_2": sensor_2,
+    "sensor_3": sensor_3,
+    "sensor_4": sensor_4,
+    "sensor_5": sensor_5,
+    "sensor_6": sensor_6,
+    "sensor_7": sensor_7,
+    "sensor_8": sensor_8,
+    "sensor_9": sensor_9,
+    "sensor_10": sensor_10
+}])
+
+# ----------------------------
+# Prediction Section
+# ----------------------------
+
+if st.button("Predict Engine Failure"):
+
+    prediction = model.predict(input_data)[0]
+
+    if prediction == 1:
+        st.error("🚨 High Risk: Engine Failure Likely. Immediate Maintenance Recommended.")
+    else:
+        st.success("✅ Engine Operating Normally. No Immediate Maintenance Required.")

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