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
+import joblib
+from huggingface_hub import hf_hub_download
+
+st.set_page_config(page_title="Wellness Tourism Package Purchase Predictor", layout="centered")
+
+# ------------------------------
+# Load Model + Threshold from Hugging Face Hub
+# ------------------------------
+
+REPO_ID = "subratm62/tourism-project"
+
+# Download model pipeline
+model_path = hf_hub_download(repo_id=REPO_ID, filename="best_tourism_model.joblib")
+model = joblib.load(model_path)
+
+# Download threshold metadata (saved during training)
+threshold_path = hf_hub_download(repo_id=REPO_ID, filename="chosen_threshold.txt")
+with open(threshold_path, "r") as f:
+    classification_threshold = float(f.read().strip())
+
+# ------------------------------
+# Streamlit UI
+# ------------------------------
+
+st.title("Wellness Tourism Package Purchase Predictor")
+st.write("Predict whether a customer is likely to purchase the **Wellness Tourism Package** based on their details.")
+
+st.markdown("---")
+
+# ------------------------------
+# Create Input Form
+# ------------------------------
+
+st.subheader("Customer Information")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    Age = st.number_input("Age", min_value=1, max_value=100, value=35)
+    TypeofContact = st.selectbox("Type of Contact", ["Self Enquiry", "Company Invited"])
+    CityTier = st.selectbox("City Tier", [1, 2, 3])
+    DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=0, max_value=60, value=10)
+    Occupation = st.selectbox("Occupation", ["Salaried", "Self Employed", "Business", "Free Lancer"])
+
+with col2:
+    Gender = st.selectbox("Gender", ["Male", "Female"])
+    NumberOfPersonVisiting = st.number_input("Number Of Persons Visiting", min_value=1, max_value=10, value=2)
+    NumberOfFollowups = st.number_input("Number of Follow-ups", min_value=0, max_value=10, value=2)
+    PreferredPropertyStar = st.selectbox("Preferred Property Star", [3, 4, 5])
+    ProductPitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Super Deluxe", "King", "Queen"])
+
+st.markdown("---")
+
+col3, col4 = st.columns(2)
+
+with col3:
+    MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+    NumberOfTrips = st.number_input("Number of Trips per year", min_value=0, max_value=50, value=2)
+    Passport = st.selectbox("Passport Available?", ["Yes", "No"])
+    
+with col4:
+    PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+    OwnCar = st.selectbox("Owns a Car?", ["Yes", "No"])
+    NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=10, value=0)
+    Designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP"])
+
+MonthlyIncome = st.number_input("Monthly Income (₹)", min_value=0.0, value=25000.0)
+
+st.markdown("---")
+
+# ------------------------------
+# Prepare input for model
+# ------------------------------
+
+input_data = pd.DataFrame([{
+    "Age": Age,
+    "CityTier": CityTier,
+    "DurationOfPitch": DurationOfPitch,
+    "NumberOfPersonVisiting": NumberOfPersonVisiting,
+    "NumberOfFollowups": NumberOfFollowups,
+    "PreferredPropertyStar": PreferredPropertyStar,
+    "NumberOfTrips": NumberOfTrips,
+    "PitchSatisfactionScore": PitchSatisfactionScore,
+    "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
+    "MonthlyIncome": MonthlyIncome,
+
+    "TypeofContact": TypeofContact,
+    "Occupation": Occupation,
+    "Gender": Gender,
+    "ProductPitched": ProductPitched,
+    "MaritalStatus": MaritalStatus,
+    "Designation": Designation,
+
+    "Passport": 1 if Passport == "Yes" else 0,
+    "OwnCar": 1 if OwnCar == "Yes" else 0
+}])
+
+# ------------------------------
+# Prediction
+# ------------------------------
+if st.button("Predict Purchase Likelihood"):
+    proba = model.predict_proba(input_data)[0, 1]
+    prediction = 1 if proba >= classification_threshold else 0
+
+    st.subheader("Prediction Result")
+    if prediction == 1:
+        st.success(f" The customer is **LIKELY to buy** the Wellness Tourism Package.")
+    else:
+        st.error(f"❗ The customer is **NOT likely to buy** the package.")
+
+    st.write(f"**Predicted Probability:** {proba:.4f}")
+    st.write(f"**Decision Threshold:** {classification_threshold:.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