Upload folder using huggingface_hub

Dockerfile

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+# 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
+
+# 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
+
+# ================================
+# App Title & Description
+# ================================
+st.set_page_config(page_title="Tourism Package Prediction", page_icon="🌍", layout="centered")
+
+st.title("🌍 Tourism Package Prediction App")
+st.write(
+    """
+    This application predicts whether a customer is likely to **opt for a tourism package**
+    based on their profile and preferences.
+    Please provide the customer details below:
+    """)
+
+# ================================
+# Load Model from Hugging Face Hub
+# ================================
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id="Parthi07/Package-Prediction-Model",
+        filename="models/best_package_prediction_model_v1.joblib"
+    )
+    return joblib.load(model_path)
+
+model = load_model()
+
+# Mapping for City Tier
+city_tier_map = {"Tier 1": 1, "Tier 2": 2, "Tier 3": 3}
+
+# ================================
+# Sidebar Input Form (Improved Layout)
+# ================================
+st.sidebar.header("📝 Enter Customer Details")
+
+# --------- 1. Personal Information ---------
+with st.sidebar.expander("👤 Personal Information", expanded=True):
+    age = st.number_input("Age of Customer", min_value=18, max_value=100, value=30)
+    gender = st.selectbox("Gender", ["Female", "Male"])
+    marital_status = st.selectbox("Marital Status", ["Single", "Divorced", "Married", "Unmarried"])
+    occupation = st.selectbox("Occupation", ["Salaried", "Free Lancer", "Small Business", "Large Business"])
+    designation = st.selectbox("Designation", ["Manager", "Executive", "Senior Manager", "AVP", "VP"])
+    city_tier = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
+# --------- 2. Lifestyle & Financial ---------
+with st.sidebar.expander("💰 Lifestyle & Financial", expanded=True):
+    monthly_income = st.number_input("Monthly Income", min_value=100, max_value=200000, value=10000)
+    own_car = st.radio("Owns a Car?", ["Yes", "No"])
+    passport = st.radio("Has Passport?", ["Yes", "No"])
+
+# --------- 3. Travel Preferences ---------
+with st.sidebar.expander("✈️ Travel Preferences", expanded=False):
+    product_pitched = st.selectbox("Product Pitched", ["Deluxe", "Basic", "Standard", "Super Deluxe", "King"])
+    preferred_property_star = st.selectbox("Preferred Property Star", [3, 4, 5])
+
+# --------- 4. Trip & Family Details ---------
+with st.sidebar.expander("👨‍👩‍👧 Family & Trips", expanded=False):
+    num_person_visiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=5, value=1)
+    num_children_visiting = st.number_input("Number of Children Visiting", min_value=0, max_value=3, value=0)
+    num_trips = st.number_input("Number of Trips", min_value=1, max_value=22, value=3)
+
+# --------- 5. Sales Interaction ---------
+with st.sidebar.expander("📞 Sales Interaction", expanded=False):
+    type_of_contact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+    duration_of_pitch = st.number_input("Pitch Duration (minutes)", min_value=0, max_value=150, value=30)
+    num_followups = st.number_input("Number of Followups", min_value=1, max_value=6, value=1)
+    pitch_satisfaction_score = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
+
+# ================================
+# Prepare Input Data
+# ================================
+input_data = pd.DataFrame([{
+    "TypeofContact": type_of_contact,
+    "CityTier": city_tier_map[city_tier],
+    "Occupation": occupation,
+    "Gender": gender,
+    "ProductPitched": product_pitched,
+    "PreferredPropertyStar": preferred_property_star,
+    "MaritalStatus": marital_status,
+    "Designation": designation,
+    "NumberOfPersonVisiting": num_person_visiting,
+    "NumberOfFollowups": num_followups,
+    "NumberOfTrips": num_trips,
+    "PitchSatisfactionScore": pitch_satisfaction_score,
+    "NumberOfChildrenVisiting": num_children_visiting,
+    "MonthlyIncome": monthly_income,
+    "DurationOfPitch": duration_of_pitch,
+    "Age": age,
+    "Passport": 1 if passport == "Yes" else 0,
+    "OwnCar": 1 if own_car == "Yes" else 0
+}])
+
+# ================================
+# Prediction
+# ================================
+
+# Classification threshold used during training
+CLASSIFICATION_THRESHOLD = 0.45
+
+if st.button("🔮 Predict"):
+    # Get probability of "Product Taken" (class = 1)
+    proba = model.predict_proba(input_data)[0][1]
+    prediction = 1 if proba >= CLASSIFICATION_THRESHOLD else 0
+
+    result = "✅ Package Opted" if prediction == 1 else "❌ Package Not Opted"
+    confidence = round(proba * 100, 2)
+
+    st.subheader("📊 Prediction Result")
+    st.success(f"**{result}** with {confidence}% confidence")
+
+    st.write("### Entered Customer Profile:")
+    st.dataframe(input_data.T, use_container_width=True)

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