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 +1,108 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+import os
+
+# --- Mappings for Label Encoding (based on alphabetical sort of unique values from original data) ---
+TYPEOFCONTACT_MAP = {'Company Invited': 0, 'Self Inquiry': 1}
+OCCUPATION_MAP = {'Free Lancer': 0, 'Large Business': 1, 'Salaried': 2, 'Small Business': 3}
+GENDER_MAP = {'Fe Male': 0, 'Female': 1, 'Male': 2, 'Unaware': 3}
+PRODUCTPITCHED_MAP = {'Basic': 0, 'Deluxe': 1, 'King': 2, 'Standard': 3, 'Super Deluxe': 4}
+MARITALSTATUS_MAP = {'Divorced': 0, 'Married': 1, 'Single': 2}
+DESIGNATION_MAP = {'AVP': 0, 'Executive': 1, 'Manager': 2, 'Senior Manager': 3, 'VP': 4}
+
+# Download and load the model
+MODEL_REPO_ID = "deepakpathania/tourism_xgboost_model"
+MODEL_FILENAME = "xgboost_model/best_tourism_model_v1.joblib"
+
+try:
+    model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)
+    model = joblib.load(model_path)
+except Exception as e:
+    st.error(f"Error loading model from Hugging Face Hub: {e}")
+    st.stop()
+
+# Streamlit UI for Wellness Tourism Package Purchase Prediction
+st.title("Wellness Tourism Package Purchase Prediction")
+st.write("""
+This application predicts whether a customer will purchase the newly introduced Wellness Tourism Package.
+Fill in the customer details below to get a prediction.
+""")
+
+# User input fields
+st.header("Customer Details")
+
+age = st.slider("Age", min_value=18, max_value=80, value=35)
+type_of_contact = st.selectbox("Type of Contact", list(TYPEOFCONTACT_MAP.keys()))
+city_tier = st.selectbox("City Tier", [1, 2, 3])
+duration_of_pitch = st.slider("Duration of Pitch (minutes)", min_value=5, max_value=100, value=15)
+occupation = st.selectbox("Occupation", list(OCCUPATION_MAP.keys()))
+gender = st.selectbox("Gender", list(GENDER_MAP.keys())) # Using full list due to EDA observation
+number_of_person_visiting = st.slider("Number of Persons Visiting", min_value=1, max_value=5, value=3)
+number_of_followups = st.slider("Number of Follow-ups", min_value=1, max_value=6, value=3)
+product_pitched = st.selectbox("Product Pitched", list(PRODUCTPITCHED_MAP.keys()))
+preferred_property_star = st.selectbox("Preferred Property Star", [3.0, 4.0, 5.0])
+marital_status = st.selectbox("Marital Status", list(MARITALSTATUS_MAP.keys()))
+number_of_trips = st.slider("Number of Trips Annually", min_value=1, max_value=25, value=3)
+passport = st.selectbox("Passport Holder?", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+pitch_satisfaction_score = st.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
+own_car = st.selectbox("Owns Car?", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+number_of_children_visiting = st.slider("Number of Children Visiting (under 5)", min_value=0, max_value=3, value=1)
+designation = st.selectbox("Designation", list(DESIGNATION_MAP.keys()))
+monthly_income = st.number_input("Monthly Income", min_value=1000.0, max_value=100000.0, value=25000.0, step=100.0)
+
+# Prepare input data for the model (matching Xtrain structure after LabelEncoding)
+if st.button("Predict Purchase"):
+    # Convert categorical inputs to numerical using defined mappings
+    encoded_type_of_contact = TYPEOFCONTACT_MAP[type_of_contact]
+    encoded_occupation = OCCUPATION_MAP[occupation]
+    encoded_gender = GENDER_MAP[gender]
+    encoded_product_pitched = PRODUCTPITCHED_MAP[product_pitched]
+    encoded_marital_status = MARITALSTATUS_MAP[marital_status]
+    encoded_designation = DESIGNATION_MAP[designation]
+
+    # Create a DataFrame with the same column order as Xtrain
+    input_data = pd.DataFrame([{
+        'Age': age,
+        'TypeofContact': encoded_type_of_contact,
+        'CityTier': city_tier,
+        'DurationOfPitch': duration_of_pitch,
+        'Occupation': encoded_occupation,
+        'Gender': encoded_gender,
+        'NumberOfPersonVisiting': number_of_person_visiting,
+        'NumberOfFollowups': number_of_followups,
+        'ProductPitched': encoded_product_pitched,
+        'PreferredPropertyStar': preferred_property_star,
+        'MaritalStatus': encoded_marital_status,
+        'NumberOfTrips': number_of_trips,
+        'Passport': passport,
+        'PitchSatisfactionScore': pitch_satisfaction_score,
+        'OwnCar': own_car,
+        'NumberOfChildrenVisiting': number_of_children_visiting,
+        'Designation': encoded_designation,
+        'MonthlyIncome': monthly_income
+    }])
+
+    # Ensure column order matches Xtrain used during training
+    # This list should match the column order in Xtrain exactly.
+    # Using a predefined list as inference from Xtrain.columns from kernel state is reliable.
+    column_order = [
+        'Age', 'TypeofContact', 'CityTier', 'DurationOfPitch', 'Occupation', 'Gender',
+        'NumberOfPersonVisiting', 'NumberOfFollowups', 'ProductPitched',
+        'PreferredPropertyStar', 'MaritalStatus', 'NumberOfTrips', 'Passport',
+        'PitchSatisfactionScore', 'OwnCar', 'NumberOfChildrenVisiting', 'Designation',
+        'MonthlyIncome'
+    ]
+    input_data = input_data[column_order]
+
+    prediction = model.predict(input_data)[0]
+    prediction_proba = model.predict_proba(input_data)[:, 1][0]
+
+    st.subheader("Prediction Result:")
+    if prediction == 1:
+        st.success(f"The model predicts: **Customer WILL purchase the Wellness Tourism Package!** (Probability: {prediction_proba:.2f})")
+    else:
+        st.info(f"The model predicts: **Customer will NOT purchase the Wellness Tourism Package.** (Probability: {prediction_proba:.2f})")
+
+    st.write("Note: The model's classification threshold is 0.45.")

requirements.txt

@@ -1,3 +1,8 @@
-altair
 pandas
-streamlit
+scikit-learn
+xgboost
+joblib
+mlflow==3.0.1
+pyngrok==7.2.12
+streamlit
+huggingface_hub