Upload folder using huggingface_hub

Dockerfile

@@ -1,21 +1,23 @@
-FROM python:3.9-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 \
-    software-properties-common \
-    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
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+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 +1,40 @@
+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="bala-ai/machine_failure_model", filename="best_machine_failure_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Machine 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
+Type = st.selectbox("Machine Type", ["H", "L", "M"])
+air_temp = st.number_input("Air Temperature (K)", min_value=250.0, max_value=400.0, value=298.0, step=0.1)
+process_temp = st.number_input("Process Temperature (K)", min_value=250.0, max_value=500.0, value=324.0, step=0.1)
+rot_speed = st.number_input("Rotational Speed (RPM)", min_value=0, max_value=3000, value=1400)
+torque = st.number_input("Torque (Nm)", min_value=0.0, max_value=100.0, value=40.0, step=0.1)
+tool_wear = st.number_input("Tool Wear (min)", min_value=0, max_value=300, value=10)
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Air temperature': air_temp,
+    'Process temperature': process_temp,
+    'Rotational speed': rot_speed,
+    'Torque': torque,
+    'Tool wear': tool_wear,
+    'Type': Type
+}])
+
+
+if st.button("Predict Failure"):
+    prediction = model.predict(input_data)[0]
+    result = "Machine Failure" if prediction == 1 else "No Failure"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

requirements.txt

@@ -1,3 +1,6 @@
-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