Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,23 @@
+# 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

@@ -0,0 +1,67 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download the model from the Model Hub
+model_path = hf_hub_download(repo_id="Georgek17/vistit-predictor-model", filename="best_visit_predictor_model_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Customer visit Prediction App")
+st.write("The Customer visit Prediction App is an internal tool for predicts whether customer will purchase the newly introduced Wellness Tourism Package before contacting them based on their details.")
+st.write("Kindly enter the customer details to check whether they are likely to purchase the Wellness Tourism Package.")
+
+# Collect user input
+Age = st.number_input("Age (customer's age in years)", min_value=18, max_value=100, value=30)
+TypeofContact = st.selectbox("Type of Contact (method by which the customer was contacted)", ["Self Enquiry", "Company Invited"])
+CityTier= st.selectbox("City Tier (The city category based on development, population, and living standards)", ["1", "2", "3"])
+DurationOfPitch = st.number_input("DurationOfPitch (Duration of the sales pitch delivered to the customer.)", min_value=1, value=14)
+Occupation= st.selectbox("Occupation", ["Free Lancer", "Large Business", "Salaried", "Small Business"])
+Gender= st.selectbox("Gender", ["Female", "Male"])
+NumberOfPersonVisiting= st.number_input("Number Of PersonVisiting (Total number of people accompanying the customer on the trip.)", value=3)
+NumberOfFollowups= st.number_input("Number Of Followups (Total number of follow-ups by the salesperson after the sales pitch.)", value=3)
+ProductPitched= st.selectbox("Product Pitched (The type of product pitched to the customer.)", ["Basic", "Deluxe", "King", "Standard", "Super Deluxe"])
+PreferredPropertyStar= st.selectbox("Preferred Property Star (Preferred hotel rating by the customer.)", ["1", "2", "3", "4", "5"])
+MaritalStatus= st.selectbox("Marital Status", ["Divorced", "Married", "Single", "Unmarried"])
+NumberOfTrips= st.number_input("Number Of Trips (Average number of trips the customer takes annually.)", min_value=1, value=2)
+Passport= st.selectbox("Has Passport? (Whether the customer holds a valid passport (0: No, 1: Yes).)", ["0", "1"])
+PitchSatisfactionScore= st.selectbox("Pitch Satisfaction Score (Score indicating the customer's satisfaction with the sales pitch.)", ["1", "2", "3", "4", "5"])
+OwnCar= st.selectbox("Own Car? (Whether the customer owns a car (0: No, 1: Yes).)", ["0", "1"])
+NumberOfChildrenVisiting= st.number_input("Number Of Children Visiting)", value=1)
+Designation= st.selectbox("Designation (Customer's designation in their current organization.)", ["AVP", "Executive", "Manager", "Senior Manager", "VP"])
+MonthlyIncome = st.number_input("Monthly Income (Gross monthly income of the customer.)", min_value=0, value=1700)
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'Age': Age,
+    'TypeofContact': TypeofContact,
+    'CityTier': CityTier,
+    'DurationOfPitch': DurationOfPitch,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'NumberOfPersonVisiting': NumberOfPersonVisiting,
+    'NumberOfFollowups':NumberOfFollowups,
+    'ProductPitched': ProductPitched,
+    'PreferredPropertyStar' : PreferredPropertyStar,
+    'MaritalStatus' : MaritalStatus,
+    'NumberOfTrips' : NumberOfTrips, 
+    'Passport' : Passport,
+    'PitchSatisfactionScore' : PitchSatisfactionScore,
+    'OwnCar' : OwnCar,
+    'NumberOfChildrenVisiting' : NumberOfChildrenVisiting,
+    'Designation' : Designation,
+    'MonthlyIncome' : MonthlyIncome
+}])
+
+# Set the classification threshold
+classification_threshold = 0.45
+
+# Predict button
+if st.button("Predict"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    prediction = (prediction_proba >= classification_threshold).astype(int)
+    result = "purchase the Wellness Tourism Package " if prediction == 1 else "not purchase the Wellness Tourism Package"
+    st.write(f"Based on the information provided, the customer is likely to {result}.")

requirements.txt

@@ -0,0 +1,7 @@
+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