Upload folder using huggingface_hub

Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# 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
+
+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
+
+# 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

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+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import os
+
+# Common constants
+HUGGINGFACE_USER_NAME = os.getenv('HUGGINGFACE_USER_NAME')
+HUGGINGFACE_MODEL_NAME = os.getenv('HUGGINGFACE_MODEL_NAME')
+
+# Download the model from the Model Hub
+# Note: Ensure the filename matches what you uploaded in your training script
+try:
+    model_path = hf_hub_download(
+        repo_id=f"{HUGGINGFACE_USER_NAME}/{HUGGINGFACE_MODEL_NAME}",
+        filename="model.joblib"
+    )
+    model = joblib.load(model_path)
+except Exception as e:
+    st.error(f"Error loading model from Hugging Face: {e}")
+    st.stop()
+
+# Streamlit UI Setup
+st.set_page_config(page_title="Wellness Package Prediction", layout="centered")
+st.title("Tourism Wellness Package Purchase Prediction")
+st.write("""
+This tool predicts whether a customer is likely to purchase a **Wellness Package** based on their demographic and interaction history.
+""")
+
+st.divider()
+
+# Create two columns for a cleaner UI layout
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("Demographics")
+    Age = st.number_input("Age", min_value=18, max_value=100, value=30)
+    Gender = st.selectbox("Gender", ["Male", "Female"])
+    MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+    Occupation = st.selectbox("Occupation", ['Salaried' 'Free Lancer' 'Small Business' 'Large Business'])
+    Designation = st.selectbox("Designation", ['Manager' 'Executive' 'Senior Manager' 'AVP' 'VP'])
+    MonthlyIncome = st.number_input("Monthly Income", min_value=0.0, value=25000.0)
+    CityTier = st.slider("City Tier", 1, 3, 1)
+
+with col2:
+    st.subheader("Travel Behavior")
+    TypeofContact = st.selectbox("Type of Contact", ['Self Enquiry' 'Company Invited'])
+    ProductPitched = st.selectbox("Product Pitched", ['Deluxe' 'Basic' 'Standard' 'Super Deluxe' 'King'])
+    DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=0, value=15)
+    NumberOfFollowups = st.slider("Number of Follow-ups", 1, 10, 3)
+    NumberOfTrips = st.number_input("Number of Trips", min_value=0, value=2)
+    PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+    PreferredPropertyStar = st.slider("Preferred Property Star", 3, 5, 3)
+
+st.subheader("Additional Info")
+c3, c4, c5 = st.columns(3)
+with c3:
+    Passport = st.selectbox("Has Passport?", ["Yes", "No"])
+with c4:
+    OwnCar = st.selectbox("Owns a Car?", ["Yes", "No"])
+with c5:
+    NumberOfPersonVisiting = st.number_input("Adults Visiting", min_value=1, value=2)
+    NumberOfChildrenVisiting = st.number_input("Children Visiting", min_value=0, value=0)
+
+# Prepare input data matching the exact training schema
+input_dict = {
+    'Age': Age,
+    'CityTier': CityTier,
+    'DurationOfPitch': DurationOfPitch,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'NumberOfFollowups': NumberOfFollowups,
+    'PreferredPropertyStar': PreferredPropertyStar,
+    'NumberOfTrips': NumberOfTrips,
+    'Passport': 1 if Passport == "Yes" else 0,
+    'PitchSatisfactionScore': PitchSatisfactionScore,
+    'OwnCar': 1 if OwnCar == "Yes" else 0,
+    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
+    'MonthlyIncome': MonthlyIncome,
+    'TypeofContact': TypeofContact,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'ProductPitched': ProductPitched,
+    'MaritalStatus': MaritalStatus,
+    'Designation': Designation
+}
+
+input_data = pd.DataFrame([input_dict])
+
+# Prediction Logic
+classification_threshold = 0.45
+
+st.divider()
+if st.button("Generate Prediction", type="primary"):
+    # Get probability from XGBoost
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+
+    # Apply custom threshold
+    prediction = 1 if prediction_proba >= classification_threshold else 0
+
+    if prediction == 1:
+        st.success(f"High Potential: Customer is likely to **PURCHASE** (Prob: {prediction_proba:.2f})")
+    else:
+        st.warning(f"Low Potential: Customer is likely to **NOT PURCHASE** (Prob: {prediction_proba:.2f})")

requirements.txt

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+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