Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,28 @@
-FROM python:3.13.5-slim
 
+# Use Python base image
+FROM python:3.9
+
+# Set working directory
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Copy project files
+COPY . .
+
+# Install dependencies
+RUN pip install -r requirements.txt
+
+# Create non-root user
+RUN useradd -m -u 1000 user
+
+USER user
 
-COPY requirements.txt ./
-COPY src/ ./src/
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
 
-RUN pip3 install -r requirements.txt
+WORKDIR $HOME/app
 
-EXPOSE 8501
+COPY --chown=user . $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Run Streamlit application
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

@@ -0,0 +1,91 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+
+from huggingface_hub import hf_hub_download
+
+# =========================================
+# Load Model from Hugging Face Hub
+# =========================================
+
+model_path = hf_hub_download(
+    repo_id="geniusut/engine-predictive-maintenance-model",
+    filename="best_model.pkl"
+)
+
+model = joblib.load(model_path)
+
+# =========================================
+# Streamlit UI
+# =========================================
+
+st.title("Engine Predictive Maintenance")
+
+st.write(
+    "Enter engine operational parameters "
+    "to predict maintenance requirements."
+)
+
+# =========================================
+# User Inputs
+# =========================================
+
+engine_rpm = st.number_input(
+    "Engine RPM",
+    value=800.0
+)
+
+lub_oil_pressure = st.number_input(
+    "Lub Oil Pressure",
+    value=3.5
+)
+
+fuel_pressure = st.number_input(
+    "Fuel Pressure",
+    value=6.5
+)
+
+coolant_pressure = st.number_input(
+    "Coolant Pressure",
+    value=2.5
+)
+
+lub_oil_temp = st.number_input(
+    "Lub Oil Temperature",
+    value=75.0
+)
+
+coolant_temp = st.number_input(
+    "Coolant Temperature",
+    value=80.0
+)
+
+# =========================================
+# Prepare Input Data
+# =========================================
+
+input_data = pd.DataFrame([{
+    "Engine_RPM": engine_rpm,
+    "Lub_Oil_Pressure": lub_oil_pressure,
+    "Fuel_Pressure": fuel_pressure,
+    "Coolant_Pressure": coolant_pressure,
+    "Lub_Oil_Temperature": lub_oil_temp,
+    "Coolant_Temperature": coolant_temp
+}])
+
+# =========================================
+# Predict
+# =========================================
+
+if st.button("Predict"):
+
+    prediction = model.predict(input_data)[0]
+
+    result = (
+        "Maintenance Required"
+        if prediction == 1
+        else "Engine Operating Normally"
+    )
+
+    st.success(f"Prediction Result: {result}")

requirements.txt

@@ -1,3 +1,8 @@
-altair
+
 pandas
-streamlit
+streamlit
+huggingface_hub
+joblib
+scikit-learn
+xgboost
+mlflow