Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,28 @@
-FROM python:3.13.5-slim
+# 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
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Copy all files from the project directory to /app
+COPY . .
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Install Python dependencies
+RUN pip3 install -r tourism_project/requirements.txt
 
-RUN pip3 install -r requirements.txt
+# Create a non-root user for security
+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 app files with proper ownership
+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
+CMD ["streamlit", "run", "tourism_project/deployment/app.py", \
+     "--server.port=8501", \
+     "--server.address=0.0.0.0", \
+     "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,221 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from huggingface_hub import hf_hub_download
+import joblib
+
+# App title and description
+st.set_page_config(
+    page_title="Wellness Tourism Prediction",
+    page_icon="🏖️",
+    layout="wide"
+)
+
+st.title("🏖️ Wellness Tourism Prediction App")
+st.markdown("""
+This application predicts whether a customer is likely to purchase a wellness tourism package
+based on their demographic, behavioral, and engagement data.
+""")
+
+# Sidebar for information
+with st.sidebar:
+    st.header("About This Model")
+    st.markdown("""
+    **Model Details:**
+    - Algorithm: XGBoost Classifier
+    - Trained on: Wellness Tourism Dataset
+    - Target: Product Taken (1 = Purchased, 0 = Not Purchased)
+
+    **Key Features:**
+    - Handles class imbalance with scale_pos_weight
+    - Uses preprocessing pipeline (scaling + encoding)
+    - Optimized for ROC-AUC score
+    """)
+
+    # Display model metrics from your training
+    st.subheader("Model Performance")
+    st.metric("ROC AUC", "0.9414")
+    st.metric("Precision (Class 1)", "0.69")
+    st.metric("Recall (Class 1)", "0.79")
+
+# Function to download and load model
+@st.cache_resource
+def load_model():
+    """Load the trained model from Hugging Face Hub"""
+    try:
+        model_path = hf_hub_download(
+            repo_id="simnid/wellness-tourism-model",
+            filename="best_wellness_tourism_model.joblib"
+        )
+        model = joblib.load(model_path)
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+# Load the model
+model = load_model()
+
+if model is None:
+    st.warning("Model could not be loaded. Please check your connection.")
+    st.stop()
+
+# User input section
+st.header("📋 Customer Information")
+
+# Create columns for better layout
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.subheader("Demographic Information")
+    Age = st.number_input("Age", min_value=18, max_value=80, value=35, step=1)
+    Gender = st.selectbox("Gender", ["Male", "Female"])
+    MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced", "Unmarried"])
+    NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=0, step=1)
+    Designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+
+with col2:
+    st.subheader("Travel Preferences")
+    CityTier = st.selectbox("City Tier", [1, 2, 3])
+    PreferredPropertyStar = st.selectbox("Preferred Property Star Rating", [3, 4, 5])
+    Passport = st.selectbox("Has Passport", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes")
+    OwnCar = st.selectbox("Owns Car", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes")
+    NumberOfTrips = st.number_input("Number of Previous Trips", min_value=0, max_value=20, value=2, step=1)
+
+with col3:
+    st.subheader("Engagement Details")
+    TypeofContact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+    DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=0.0, max_value=60.0, value=15.0, step=0.5)
+    NumberOfPersonVisiting = st.number_input("Number of People Visiting", min_value=1, max_value=10, value=2, step=1)
+    NumberOfFollowups = st.number_input("Number of Follow-ups", min_value=0, max_value=10, value=3, step=1)
+    ProductPitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"])
+    PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+
+# Additional inputs
+st.subheader("Financial Information")
+col4, col5 = st.columns(2)
+
+with col4:
+    Occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+    MonthlyIncome = st.number_input("Monthly Income ($)", min_value=1000, max_value=50000, value=15000, step=500)
+
+with col5:
+    # Calculate Pitch Efficiency (feature from your preprocessing)
+    PitchEfficiency = DurationOfPitch * PitchSatisfactionScore
+    st.metric("Calculated Pitch Efficiency", f"{PitchEfficiency:.2f}")
+
+# Assemble input into DataFrame
+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': Passport,
+    'PitchSatisfactionScore': PitchSatisfactionScore,
+    'OwnCar': OwnCar,
+    'NumberOfChildrenVisiting': NumberOfChildrenVisiting,
+    'Designation': Designation,
+    'MonthlyIncome': MonthlyIncome,
+    'PitchEfficiency': PitchEfficiency
+}])
+
+# Display the input data
+with st.expander("View Input Data"):
+    st.dataframe(input_data)
+
+# Prediction section
+st.header("���� Prediction")
+
+if st.button("Predict Purchase Probability", type="primary", use_container_width=True):
+    with st.spinner("Making prediction..."):
+        try:
+            # Make prediction
+            prediction_proba = model.predict_proba(input_data)[0]
+            prediction_class = model.predict(input_data)[0]
+
+            # Display results
+            col_result1, col_result2 = st.columns(2)
+
+            with col_result1:
+                st.subheader("Prediction Result")
+                if prediction_class == 1:
+                    st.success("✅ **Customer is LIKELY to purchase**")
+                    st.balloons()
+                else:
+                    st.info("❌ **Customer is UNLIKELY to purchase**")
+
+            with col_result2:
+                st.subheader("Probability Scores")
+                # Create gauge-like visualization
+                prob_purchase = prediction_proba[1] * 100
+                prob_no_purchase = prediction_proba[0] * 100
+
+                st.metric("Probability of Purchase", f"{prob_purchase:.1f}%")
+                st.metric("Probability of No Purchase", f"{prob_no_purchase:.1f}%")
+
+                # Visual progress bar
+                st.progress(int(prob_purchase))
+                st.caption(f"Confidence: {prob_purchase:.1f}%")
+
+            # Business insights
+            st.subheader("📊 Business Insights")
+
+            if prediction_class == 1:
+                if prob_purchase > 80:
+                    st.success("**High Confidence Lead** - Consider offering premium packages")
+                elif prob_purchase > 60:
+                    st.warning("**Medium Confidence Lead** - Standard follow-up recommended")
+                else:
+                    st.info("**Low Confidence Lead** - May require additional engagement")
+
+                st.markdown("""
+                **Recommended Actions:**
+                - Schedule follow-up call within 48 hours
+                - Offer personalized package options
+                - Highlight wellness benefits specific to customer profile
+                """)
+            else:
+                st.markdown("""
+                **Recommended Actions:**
+                - Consider re-engagement in 3-6 months
+                - Collect feedback on pitch satisfaction
+                - Update marketing materials for similar profiles
+                """)
+
+        except Exception as e:
+            st.error(f"Error making prediction: {e}")
+
+# Model information
+with st.expander("ℹ️ Model Information"):
+    st.markdown("""
+    **Model Architecture:**
+    - Preprocessing: StandardScaler for numeric features + OneHotEncoder for categorical features
+    - Algorithm: XGBoost Classifier
+    - Hyperparameters from grid search:
+        - n_estimators: 200
+        - max_depth: 7
+        - learning_rate: 0.1
+        - colsample_bytree: 0.6
+        - reg_lambda: 0.5
+
+    **Training Performance:**
+    - ROC AUC: 0.9414
+    - PR AUC: 0.8344
+    - Test Accuracy: 0.8898
+    - Precision (Class 1): 0.69
+    - Recall (Class 1): 0.79
+
+    **Note:** Class 1 represents customers who purchased the wellness tourism package.
+    """)
+
+# Footer
+st.markdown("---")
+st.caption("Wellness Tourism Prediction Model | Built with XGBoost & Streamlit")

requirements.txt

@@ -1,3 +1,8 @@
-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
+numpy==1.26.0