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 +1,68 @@
+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="jarpan03/engine-predictive-maintenance-model", filename="best_engine_maintenance_model_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Tourism Package Prediction")
+st.write("Fill the customer details below to predict if they'll purchase a travel package")
+
+# Collect user input
+Age = st.slider("Age", 18, 70, 30)
+TypeofContact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+CityTier = st.selectbox("City Tier", [1, 2, 3])
+DurationOfPitch = st.slider("Duration of Pitch (mins)", 0, 100, 15)
+Occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+Gender = st.selectbox("Gender", ["Male", "Female", "Others"])
+NumberOfPersonVisiting = st.slider("Number of Persons Visiting", 1, 5, 2)
+NumberOfFollowups = st.slider("Number of Follow-ups", 1, 10, 3)
+ProductPitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
+PreferredPropertyStar = st.selectbox("Preferred Property Star", [1, 2, 3, 4, 5])
+MaritalStatus = st.selectbox("Marital Status", ["Married", "Single", "Divorced", "Unmarried"])
+NumberOfTrips = st.slider("Number of Trips", 1, 20, 3)
+Passport = st.selectbox("Has Passport?", ["Yes", "No"])
+PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+OwnCar = st.selectbox("Owns a Car?", ["Yes", "No"])
+NumberOfChildrenVisiting = st.slider("Number of Children Visited", 0, 5, 1)
+Designation = st.selectbox("Designation", ["Executive", "Manager", "AVP", "VP", "Sr. Manager"])
+MonthlyIncome = st.number_input("Monthly Income", min_value=1000.0, value=30000.0)
+
+# ----------------------------
+# Prepare input data
+# ----------------------------
+input_data = pd.DataFrame([{
+    'Age': Age,
+    'TypeofContact': TypeofContact,
+    'CityTier': CityTier,
+    'DurationOfPitch': DurationOfPitch,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'NumberOfFollowups': NumberOfFollowups,
+    'ProductPitched': ProductPitched,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'MaritalStatus': MaritalStatus,
+    'NumberOfTrips': NumberOfTrips,
+    'Passport': 1 if Passport == "Yes" else 0,
+    'PitchSatisfactionScore': PitchSatisfactionScore,
+    'OwnCar': 1 if OwnCar == "Yes" else 0,
+    'NumberOfChildrenVisitings': NumberOfChildrenVisiting,
+    'Designation': Designation,
+    'MonthlyIncome': MonthlyIncome
+}])
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+# Predict button
+if st.button("Predict"):
+    prob = model.predict_proba(input_data)[0,1]
+    pred = int(prob >= classification_threshold)
+    result = "will purchase the travel package" if pred == 1 else "is unlikely to purchase"
+    st.write(f"Prediction: Customer {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