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
+COPY streamlit_app.py ./app.py
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
 
-EXPOSE 8501
+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 +1,45 @@
+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="sasipriyank/predectivemodel", filename="best_predective_model.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Predective Maintainencen App")
+st.write("The Predective Maintainencen App is an internal tool for customer that predicts whether Machine sensor is failed or not.")
+st.write("Kindly enter the customer details to check whether they are likely to purchase or not.")
+
+# Collect user input
+
+LuboilPressure = st.number_input("Lub oil pressure",min_value=0.0, value=2.493592)
+EngineRpm = st.number_input("Engine rpm", min_value=0, value=700)
+FuelPressure= st.number_input("Fuel pressure",min_value=0.0, value=11.790927)
+CoolantPressure = st.number_input("Coolant pressure", min_value=0.0, value=3.178981)
+LuboilTemp = st.number_input("lub oil temp", min_value=0, value=84.144163)
+CoolantTemp = st.number_input("Coolant temp", min_value=0, value=81.632187)
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'Lub oil pressure': LuboilPressure,
+    'Engine rpm': EngineRpm,
+    'Fuel pressure': FuelPressure,
+    'Coolant pressure': CoolantPressure,
+    'lub oil temp': LuboilTemp,
+    'Coolant temp': CoolantTemp
+
+}])
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+# Predict button
+if st.button("Predict"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    prediction = (prediction_proba >= classification_threshold).astype(int)
+    result = "Failed" if prediction == 1 else "NotFailed"
+    st.write(f"Based on the information provided, the sensor is  {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

streamlit_app.py

@@ -0,0 +1,45 @@
+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="sasipriyank/predectivemodel", filename="best_predective_model.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Predective Maintainencen App")
+st.write("The Predective Maintainencen App is an internal tool for customer that predicts whether Machine sensor is failed or not.")
+st.write("Kindly enter the customer details to check whether they are likely to purchase or not.")
+
+# Collect user input
+
+LuboilPressure = st.number_input("Lub oil pressure",min_value=0.0, value=2.493592)
+EngineRpm = st.number_input("Engine rpm", min_value=0, value=700)
+FuelPressure= st.number_input("Fuel pressure",min_value=0.0, value=11.790927)
+CoolantPressure = st.number_input("Coolant pressure", min_value=0.0, value=3.178981)
+LuboilTemp = st.number_input("lub oil temp", min_value=0, value=84.144163)
+CoolantTemp = st.number_input("Coolant temp", min_value=0, value=81.632187)
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'Lub oil pressure': LuboilPressure,
+    'Engine rpm': EngineRpm,
+    'Fuel pressure': FuelPressure,
+    'Coolant pressure': CoolantPressure,
+    'lub oil temp': LuboilTemp,
+    'Coolant temp': CoolantTemp
+
+}])
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+# Predict button
+if st.button("Predict"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    prediction = (prediction_proba >= classification_threshold).astype(int)
+    result = "Failed" if prediction == 1 else "NotFailed"
+    st.write(f"Based on the information provided, the sensor is  {result} ")