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 +1,56 @@
+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= "rojasnath/tourism-package-model", filename="best_model_v1.joblib")
+
+#Load the model
+model = joblib.load(model_path)
+
+#Streamlit UI for Customer Purchase Prediction
+st.title("Tourism Package Purchase Prediction App")
+st.write("Tourism Package Purchase Prediction App is an internal tool for Visit With Us staff that predicts whether a customer will purchase the new Wellness Tourism Package based on their details.")
+st.write("Kindly enter the customer details to check whether they are likely to purchase the package.")
+
+#Collect user input
+Age= st.number_input("Age (customer's age in years)", min_value=18, max_value=120, value=30)
+TypeofContact= st.selectbox("How did the customer contact?", ["Company Invited", "Self Inquiry"])
+CityTier= st.selectbox("Customer's City Tier", ["Tier 1", "Tier 2", "Tier 3"])
+Occupation= st.selectbox("Customer's Occupation", ["Salaried", "Freelancer"])
+Gender= st.selectbox("Gender", ["Male", "Female"])
+NumberOfPersonVisiting= st.number_input("Total number of adult visitors", min_value=1, max_value=20, value=2)
+PreferredPropertyStar= st.number_input("Preferred hotel rating", min_value=3, max_value=5, value=4)
+MaritalStatus= st.selectbox("Marital status", ["Single", "Married", "Divorced"])
+NumberOfTrips= st.number_input("Average number of trips in a year", min_value=0, max_value=15, value=2)
+Passport= st.selectbox("Valid passport holder?", ["Yes", "No"])
+OwnCar= st.selectbox("Is customer a car owner?", ["Yes", "No"])
+NumberOfChildrenVisiting= st.number_input("Number of children below 5 years age", min_value=0, max_value=10, value=2)
+Designation= st.selectbox("Customer's designation in their current organization", ["Executive", "Manager", "Senior Manager", "AVP", "VP"])
+MonthlyIncome= st.number_input("Gross monthly income of the customer", min_value=5000, max_value=50000, value=15000)
+PitchSatisfactionScore= st.number_input("Customer Satisfaction Score (of the sales pitch)", min_value=1, max_value=5, value=5)
+ProductPitched= st.selectbox("Type of product pitched to the customer",["Basic", "Standard", "Deluxe", "Super Deluxe", "King"])
+NumberOfFollowups= st.number_input("Total number of follow-ups by the salesperson", min_value=0, max_value=5, value=2)
+DurationOfPitch= st.number_input("Duration of the sales pitch (in mins)", min_value=5, max_value=50, value=15)
+
+#Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'TypeofContact': TypeofContact,
+    'CityTier': CityTier,
+    'Occupation': Occupation,
+    'Gender': Gender,
+    'MaritalStatus': MaritalStatus,
+    'Designation': Designation,
+    'ProductPitched': ProductPitched
+}])
+
+#Set the classification threshold
+classification_threshold = 0.45
+
+#Make prediction
+if st.button("Predict"):
+  prediction_proba = model.predict_proba(input_data)[0, 1]
+  prediction = (prediction_proba >= classification_threshold).astype(int)
+  result = "purchase" if prediction == 1 else "not purchase"
+  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