Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,23 @@
-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
+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 the model from the Model Hub
+model_path = hf_hub_download(repo_id="nv185001/pred-model", filename="best_engine_failure_predictor_model.joblib")
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Engine Failure Prediction
+st.title("Engine Failure Prediction App")
+st.write("The Engine Failure Prediction App is an internal tool to predict whether engine would fail due to current vital parameters.")
+st.write("Kindly enter different parameters of engine to check whether they are likely to fail or not")
+
+Engine_rpm = st.number_input("Engine RPM", min_value=0 )
+Lub_oil_pressure = st.number_input("Lub Oil Pressure", min_value=0)
+Fuel_pressure = st.number_input("Fuel Pressure", min_value=0)
+Coolant_pressure = st.number_input("Coolant Pressure", min_value=0)
+lub_oil_temp = st.number_input("Lub Oil Temperature", min_value=0)
+Coolant_temp = st.number_input("Coolant Temperature", min_value=0)
+
+
+input_data = pd.DataFrame([{
+    'Engine_rpm': Engine_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
+}])
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+# Predict button
+if st.button("Predict"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    result = "to shutdown soon, due to inconsistent paramters" if prediction_proba == 1 else "to work fine"
+    st.write(f"Based on the information provided, the machine is likely {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