Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,21 @@
-FROM python:3.13.5-slim
+# 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
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+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
 
-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 +1,41 @@
+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="sp1505/Predictive-Maintenace-Model", filename="best_predictive_maintenance_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Prediction Maintenance 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
+Type = st.selectbox("Machine Type", ["H", "L", "M"])
+rpm = st.number_input("Engine rpm (K)")
+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': 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
+}])
+
+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

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+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