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 +1,112 @@
+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="affanthinks/Tourism-Package-Prediction", filename="best_tourism_pred_model_v1.joblib")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Streamlit UI for Customer Churn Prediction
+st.title("tourism Prediction App")
+st.write("The tourism Prediction App is an internal tool for tourism staff that predicts whether customers are purchasing the product based on their details and pitch.")
+st.write("Kindly enter the customer details to check whether they are likely to purchase.")
+
+# Collect user input
+Age = st.number_input("Age (Age of the customer)", min_value=0, max_value=120, value=30)
+
+TypeofContact = st.selectbox(
+    "Type of Contact (how the customer was contacted)", 
+    ["Company Invited", "Self Inquiry"]
+)
+
+CityTier = st.selectbox(
+    "City Tier (city category based on development)", 
+    [1, 2, 3]
+)
+
+Occupation = st.selectbox(
+    "Occupation (customer’s occupation)", 
+    ["Salaried", "Self Employed", "Freelancer", "Student", "Housewife", "Other"]
+)
+
+Gender = st.selectbox(
+    "Gender", 
+    ["Male", "Female"]
+)
+
+NumberOfPersonVisiting = st.number_input(
+    "Number of Persons Visiting (including the customer)", 
+    min_value=1, max_value=20, value=2
+)
+
+PreferredPropertyStar = st.selectbox(
+    "Preferred Property Star Rating", 
+    [1, 2, 3, 4, 5]
+)
+
+MaritalStatus = st.selectbox(
+    "Marital Status", 
+    ["Single", "Married", "Divorced"]
+)
+
+NumberOfTrips = st.number_input(
+    "Number of Trips Annually", 
+    min_value=0, max_value=50, value=1
+)
+
+Passport = st.selectbox(
+    "Passport (Does the customer hold a passport?)", 
+    ["Yes", "No"]
+)
+
+OwnCar = st.selectbox(
+    "Own Car (Does the customer own a car?)", 
+    ["Yes", "No"]
+)
+
+NumberOfChildrenVisiting = st.number_input(
+    "Number of Children Visiting (below age 5)", 
+    min_value=0, max_value=10, value=0
+)
+
+Designation = st.selectbox(
+    "Designation in Current Organization", 
+    ["Executive", "Manager", "Senior Manager", "AVP", "VP", "Other"]
+)
+
+MonthlyIncome = st.number_input(
+    "Monthly Income (Gross monthly income)", 
+    min_value=0.0, value=50000.0
+)
+
+# Convert categorical + numeric inputs into a dataframe that matches the 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': 1 if Passport == "Yes" else 0,
+    'OwnCar': 1 if OwnCar == "Yes" else 0,
+    '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" 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