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 +1,60 @@
+
+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="papsofts/tourism-project", filename="best_tourism_model_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Wellness Tourism Package Prediction App")
+st.write("The Wellness Tourism Package Prediction App is an internal tool for the tourism staff that predicts whether customers who might choose the wellness tour package based on their details.")
+st.write("Kindly enter the customer details to check whether they are likely to choose 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", ["Company Invited", "Self Inquiry"])
+CityTier = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
+Occupation = st.selectbox("Occupation", ["Salaried", "Freelancer"])
+Gender = st.selectbox("Gender", ["Male", "Female"])
+NumberOfPersonVisiting = st.number_input("Number of People Visiting", min_value=1, max_value=10, value=2)
+PreferredPropertyStar = st.number_input("Preferred Property Star", min_value=1, max_value=5, value=4)
+MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+NumberOfTrips = st.number_input("Number of Trips", min_value=1, max_value=10, value=2)
+Passport = st.selectbox("Passport", ["No", "Yes"])
+OwnCar = st.selectbox("Own Car", ["No", "Yes"])
+NumberOfChildrenVisiting = st.number_input("Number of Children Visiting", min_value=0, max_value=5, value=0)
+Designation = st.selectbox("Designation", ["Executive", "Managerial", "Professional", "Other"])
+MonthlyIncome = st.number_input("Monthly Income", min_value=0, value=5000)
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    '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
+}])
+
+# 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 package" if prediction == 1 else "not purchase the package"
+    st.write(f"Based on the information provided, the customer is likely to {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