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 +1,89 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="sumansaha1980/Tourism_Package", filename="best_wellness_tourism_model_v1.joblib")
+model = joblib.load(model_path)
+
+# ------------------------------
+# Streamlit UI
+# ------------------------------
+st.title("Wellness Tourism Prediction App")
+
+st.write("""
+This application predicts potential buyers of Wellness Tourism Package based on customer data.
+Please enter **Customer Data** and **Customer Interaction Data** below to get a prediction.
+""")
+
+# ------------------------------
+# User Inputs
+# ------------------------------
+st.subheader("Customer Details")
+
+CustomerID = st.text_input("CustomerID (Unique ID)", value="12345")  # Not used in model but for reference
+Age = st.number_input("Age", min_value=0, max_value=120, value=35)
+TypeofContact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
+CityTier = st.selectbox("City Tier", [1, 2, 3])
+Occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer", "Others"])
+Gender = st.radio("Gender", ["Male", "Female", "Other"])
+NumberOfPersonVisiting = st.number_input("Number of Persons Visiting", min_value=1, value=2)
+PreferredPropertyStar = st.selectbox("Preferred Property Star", [1, 2, 3, 4, 5])
+MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced", "Unmarried"])
+NumberOfTrips = st.number_input("Average Number of Trips per year", min_value=0, value=2)
+Passport = st.radio("Has Passport?", [0, 1])
+OwnCar = st.radio("Owns a Car?", [0, 1])
+NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, value=0)
+Designation = st.text_input("Designation", value="Manager")
+MonthlyIncome = st.number_input("Monthly Income", min_value=0, value=50000)
+
+st.subheader("Customer Interaction Data")
+
+PitchSatisfactionScore = st.slider("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
+ProductPitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"])
+NumberOfFollowups = st.number_input("Number of Followups", min_value=0, value=2)
+DurationOfPitch = st.number_input("Duration of Pitch (minutes)", min_value=0, value=20)
+
+# ------------------------------
+# Prepare Input for Prediction
+# ------------------------------
+input_data = {
+    "Age": Age,
+    "TypeofContact": TypeofContact,
+    "CityTier": CityTier,
+    "Occupation": Occupation,
+    "Gender": Gender,
+    "NumberOfPersonVisiting": NumberOfPersonVisiting,
+    "PreferredPropertyStar": PreferredPropertyStar,
+    "MaritalStatus": MaritalStatus,
+    "NumberOfTrips": NumberOfTrips,
+    "Passport": Passport,
+    "OwnCar": OwnCar,
+    "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
+    "Designation": Designation,
+    "MonthlyIncome": MonthlyIncome,
+    "PitchSatisfactionScore": PitchSatisfactionScore,
+    "ProductPitched": ProductPitched,
+    "NumberOfFollowups": NumberOfFollowups,
+    "DurationOfPitch": DurationOfPitch
+}
+
+input_df = pd.DataFrame([input_data])
+
+# ------------------------------
+# Prediction
+# ------------------------------
+if st.button("Predict"):
+    prediction = model.predict(input_df)[0]
+    probability = model.predict_proba(input_df)[0][1]
+
+    # Use custom threshold as dataset is imbalanced on target column
+    # where only 19% has taken the product
+    classification_threshold = 0.45
+    prediction = (probability >= classification_threshold).astype(int)
+
+    if prediction == 1:
+        st.success(f"✅ This customer is **likely to purchase** the Wellness Tourism Package. (Confidence: {probability:.2f})")
+    else:
+        st.error(f"❌ This customer is **unlikely to purchase** the package. (Confidence: {probability:.2f})")

requirements.txt

@@ -1,3 +1,7 @@
-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