Update deployment files via CI/CD

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 +1,77 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# -----------------------------
+# Load the trained tourism model
+# -----------------------------
+model_path = hf_hub_download(
+    repo_id="absethi1894/churn-model",
+    filename="best_tourism_model_v1.joblib"
+)
+model = joblib.load(model_path)
+
+# -----------------------------
+# Streamlit App UI
+# -----------------------------
+st.title("Tourism Product Purchase Prediction")
+st.write("""
+This application predicts the likelihood of a customer purchasing the tourism package
+based on their profile and travel-related information.
+Please provide the details below:
+""")
+
+# -----------------------------
+# User Inputs
+# -----------------------------
+gender = st.selectbox("Gender", ["Male", "Female"])
+marital_status = st.selectbox("Marital Status", ["Married", "Single", "Divorced"])
+age = st.number_input("Age", min_value=18, max_value=80, value=30)
+designation = st.selectbox("Designation", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+occupation = st.selectbox("Occupation", ["Salaried", "Small Business", "Large Business", "Free Lancer"])
+monthly_income = st.number_input("Monthly Income (INR)", min_value=10000, max_value=500000, value=50000, step=1000)
+
+num_trips = st.number_input("Number of Trips", min_value=0, max_value=50, value=1)
+num_person_visiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=2)
+num_children = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=0)
+
+passport = st.selectbox("Passport", [0, 1])
+own_car = st.selectbox("Own Car", [0, 1])
+
+duration_of_pitch = st.number_input("Duration of Pitch (minutes)", min_value=0, max_value=100, value=15)
+num_followups = st.number_input("Number of Followups", min_value=0, max_value=10, value=1)
+preferred_star = st.selectbox("Preferred Property Star", [3, 4, 5])
+product_pitched = st.selectbox("Product Pitched", ["Basic", "Deluxe", "Standard", "Super Deluxe", "King"])
+pitch_score = st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, value=3)
+
+# -----------------------------
+# Create DataFrame for prediction
+# -----------------------------
+input_data = pd.DataFrame([{
+    'Gender': gender,
+    'MaritalStatus': marital_status,
+    'Age': age,
+    'Designation': designation,
+    'Occupation': occupation,
+    'MonthlyIncome': monthly_income,
+    'NumberOfTrips': num_trips,
+    'NumberOfPersonVisiting': num_person_visiting,
+    'NumberOfChildrenVisiting': num_children,
+    'Passport': passport,
+    'OwnCar': own_car,
+    'DurationOfPitch': duration_of_pitch,
+    'NumberOfFollowups': num_followups,
+    'PreferredPropertyStar': preferred_star,
+    'ProductPitched': product_pitched,
+    'PitchSatisfactionScore': pitch_score
+}])
+
+# -----------------------------
+# Prediction
+# -----------------------------
+if st.button("Predict Purchase"):
+    prediction = model.predict(input_data)[0]
+    result = "Will Purchase Package ✅" if prediction == 1 else "Will Not Purchase Package ❌"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

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