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 +1,119 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from huggingface_hub import hf_hub_download
+
+
+# 1. Load Model from Hugging Face Model Hub
+# ============================================================
+
+MODEL_REPO = "Quantum9999/Tourism-Package-Prediction"
+MODEL_FILENAME = "xgb_model.pkl"
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILENAME, repo_type="model")
+    model = joblib.load(model_path)
+    return model
+
+model = load_model()
+
+
+# 2. Streamlit UI
+# ============================================================
+
+st.title(" Wellness Tourism Package Purchase Prediction")
+st.write("Fill in the customer details below to predict whether they will purchase the new Wellness Tourism Package.")
+
+st.markdown("---")
+
+# 3. User Inputs
+# ============================================================
+
+def user_input_form():
+    Age = st.number_input("Age", min_value=1, max_value=100, value=30)
+    CityTier = st.selectbox("City Tier", [1, 2, 3])
+    NumberOfPersonVisiting = st.number_input("Number of People Visiting", min_value=1, max_value=10, value=1)
+    PreferredPropertyStar = st.selectbox("Preferred Property Star", [3, 4, 5])
+    NumberOfTrips = st.number_input("Number of Trips per Year", min_value=0, max_value=20, value=2)
+    NumberOfChildrenVisiting = st.number_input("Children Visiting (Under 5 Years)", min_value=0, max_value=5, value=0)
+    MonthlyIncome = st.number_input("Monthly Income", min_value=1000, max_value=1000000, value=30000)
+    PitchSatisfactionScore = st.selectbox("Pitch Satisfaction Score", [1, 2, 3, 4, 5])
+    NumberOfFollowups = st.number_input("Number of Follow-ups", min_value=1, max_value=10, value=2)
+    DurationOfPitch = st.number_input("Duration of Pitch (Minutes)", min_value=1, max_value=60, value=15)
+
+    TypeofContact = st.selectbox("Type of Contact", ["Company Invited", "Self Enquiry"])
+    Occupation = st.selectbox("Occupation", ["Salaried", "Self Employed", "Small Business", "Large Business", "Free Lancer"])
+    Gender = st.selectbox("Gender", ["Male", "Female"])
+    MaritalStatus = st.selectbox("Marital Status", ["Married", "Single", "Divorced", "Unmarried"])
+    Passport = st.selectbox("Passport", [0, 1])
+    OwnCar = st.selectbox("Owns Car?", [0, 1])
+    Designation = st.selectbox("Designation", ["Junior", "Senior", "Manager", "Executive", "Other"])
+    ProductPitched = st.selectbox("Product Pitched", ["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
+
+    # Create DataFrame
+    data = pd.DataFrame({
+        "Age": [Age],
+        "CityTier": [CityTier],
+        "NumberOfPersonVisiting": [NumberOfPersonVisiting],
+        "PreferredPropertyStar": [PreferredPropertyStar],
+        "NumberOfTrips": [NumberOfTrips],
+        "NumberOfChildrenVisiting": [NumberOfChildrenVisiting],
+        "MonthlyIncome": [MonthlyIncome],
+        "PitchSatisfactionScore": [PitchSatisfactionScore],
+        "NumberOfFollowups": [NumberOfFollowups],
+        "DurationOfPitch": [DurationOfPitch],
+        "TypeofContact": [TypeofContact],
+        "Occupation": [Occupation],
+        "Gender": [Gender],
+        "MaritalStatus": [MaritalStatus],
+        "Passport": [Passport],
+        "OwnCar": [OwnCar],
+        "Designation": [Designation],
+        "ProductPitched": [ProductPitched]
+    })
+
+    return data
+
+user_data = user_input_form()
+
+st.markdown("---")
+
+
+# 4. Preprocess User Input → MATCH Training Preprocessing
+# ============================================================
+
+# Categorical + numerical split (same as prep.py)
+numerical_features = [
+    'Age', 'CityTier', 'NumberOfPersonVisiting', 'PreferredPropertyStar',
+    'NumberOfTrips', 'NumberOfChildrenVisiting', 'MonthlyIncome',
+    'PitchSatisfactionScore', 'NumberOfFollowups', 'DurationOfPitch'
+]
+
+categorical_features = [
+    'TypeofContact', 'Occupation', 'Gender', 'MaritalStatus',
+    'Passport', 'OwnCar', 'Designation', 'ProductPitched'
+]
+
+# Load preprocessors (generated in prep.py)
+preprocessor_path = hf_hub_download(repo_id=MODEL_REPO, filename="preprocessing_pipeline.pkl", repo_type="model")
+preprocessor = joblib.load(preprocessor_path)
+
+processed_user_data = preprocessor.transform(user_data)
+
+
+# 5. Make Prediction
+# ============================================================
+
+if st.button("Predict"):
+    prediction = model.predict(processed_user_data)[0]
+    proba = model.predict_proba(processed_user_data)[0][1]
+
+    st.subheader(" Prediction Result")
+
+    if prediction == 1:
+        st.success(f" Customer is LIKELY to purchase the Wellness Tourism Package! (Confidence: {proba:.2f})")
+    else:
+        st.error(f" Customer is NOT likely to purchase the package. (Confidence: {proba:.2f})")
+

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==1.26.4