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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 +1,82 @@
+import streamlit as st
+import pandas as pd
+import joblib
+import os
+
+# Load the trained model
+try:
+    # Assuming the model is saved in the current directory or a known path
+    model_path = "best_tourism_model_v1.joblib"
+    model = joblib.load(model_path)
+    st.success("Model loaded successfully!")
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+
+st.title("Wellness Tourism Package Purchase Prediction")
+
+st.write("Enter the customer details to predict the likelihood of purchasing the Wellness Tourism Package.")
+
+# Define input fields based on the features used in the model
+# Numeric features: 'Age', 'NumberOfPersonVisiting', 'NumberOfTrips', 'NumberOfChildrenVisiting', 'MonthlyIncome', 'PitchSatisfactionScore', 'NumberOfFollowups', 'DurationOfPitch'
+# Categorical features: 'TypeofContact', 'CityTier', 'Occupation', 'Gender', 'PreferredPropertyStar', 'MaritalStatus', 'Designation', 'Passport', 'OwnCar'
+
+age = st.slider("Age", 18, 80, 30)
+number_of_person_visiting = st.slider("Number of Persons Visiting", 1, 10, 1)
+number_of_trips = st.slider("Number of Trips Annually", 0, 50, 5)
+number_of_children_visiting = st.slider("Number of Children Visiting (under 5)", 0, 5, 0)
+monthly_income = st.number_input("Monthly Income", 10000, 500000, 50000)
+pitch_satisfaction_score = st.slider("Pitch Satisfaction Score", 1, 5, 3)
+number_of_followups = st.slider("Number of Followups", 0, 10, 3)
+duration_of_pitch = st.slider("Duration of Pitch (minutes)", 1, 60, 15)
+passport = st.selectbox("Has Passport?", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+own_car = st.selectbox("Owns Car?", [0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+
+type_of_contact = st.selectbox("Type of Contact", ['Company Invited', 'Self Inquiry'])
+city_tier = st.selectbox("City Tier", [1, 2, 3])
+occupation = st.selectbox("Occupation", ['Salaried', 'Small Business', 'Large Business', 'Free Lancer', 'Government Sector', 'Retired', 'Student'])
+gender = st.selectbox("Gender", ['Male', 'Female'])
+preferred_property_star = st.selectbox("Preferred Property Star Rating", [3, 4, 5])
+marital_status = st.selectbox("Marital Status", ['Single', 'Married', 'Divorced'])
+designation = st.selectbox("Designation", ['Manager', 'Executive', 'Senior Manager', 'AVP', 'VP', 'Senior Executive', 'Junior Executive', 'Director', 'Assistant Manager', 'Lead'])
+product_pitched = st.selectbox("Product Pitched", ['Destination', 'Resort', 'Cruise', 'Holiday', 'Accommodation', 'Flight', 'Walk in'])
+
+
+if st.button("Predict Purchase"):
+    # Create a DataFrame from the input values
+    input_data = {
+        'Age': [age],
+        'NumberOfPersonVisiting': [number_of_person_visiting],
+        'NumberOfTrips': [number_of_trips],
+        'NumberOfChildrenVisiting': [number_of_children_visiting],
+        'MonthlyIncome': [monthly_income],
+        'PitchSatisfactionScore': [pitch_satisfaction_score],
+        'NumberOfFollowups': [number_of_followups],
+        'DurationOfPitch': [duration_of_pitch],
+        'Passport': [passport],
+        'OwnCar': [own_car],
+        'TypeofContact': [type_of_contact],
+        'CityTier': [city_tier],
+        'Occupation': [occupation],
+        'Gender': [gender],
+        'PreferredPropertyStar': [preferred_property_star],
+        'MaritalStatus': [marital_status],
+        'Designation': [designation],
+        'ProductPitched': [product_pitched] # Include ProductPitched for prediction
+    }
+    input_df = pd.DataFrame(input_data)
+
+    try:
+        # Make prediction
+        prediction = model.predict(input_df)
+        prediction_proba = model.predict_proba(input_df)[:, 1] # Probability of purchasing
+
+        st.subheader("Prediction Result:")
+        if prediction[0] == 1:
+            st.success(f"The customer is likely to purchase the package.")
+        else:
+            st.warning(f"The customer is unlikely to purchase the package.")
+
+        st.write(f"Probability of Purchase: {prediction_proba[0]:.2f}")
+
+    except Exception as e:
+        st.error(f"Error during prediction: {e}")

requirements.txt

@@ -1,3 +1,6 @@
-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