Upload folder using huggingface_hub

Dockerfile

@@ -1,23 +1,28 @@
-# Use a minimal base image with Python 3.9 installed
+# Use a minimal base image with Python 3.9 installed.
 FROM python:3.9
 
-# Set the working directory inside the container to /app
+# Set the working directory inside the container to /app.
 WORKDIR /app
 
-# Copy all files from the current directory on the host to the container's /app directory
+# Copy all files from the current directory on the host to the container's /app directory.
 COPY . .
 
-# Install Python dependencies listed in requirements.txt
+# Install Python dependencies listed in requirements.txt.
 RUN pip3 install -r requirements.txt
 
+# Create a non-root user 'user' with UID 1000 and set ownership.
 RUN useradd -m -u 1000 user
 USER user
 ENV HOME=/home/user \
 	PATH=/home/user/.local/bin:$PATH
 
+# Change working directory to the user's application directory.
 WORKDIR $HOME/app
 
+# Copy the application files to the user's directory.
 COPY --chown=user . $HOME/app
 
-# Define the command to run the Streamlit app on port "8501" and make it accessible externally
+# Define the command to run the Streamlit app on port "8501" and make it accessible externally.
+# `--server.address=0.0.0.0` makes the app reachable from any IP address.
+# `--server.enableXsrfProtection=false` disables CSRF protection, often needed for public deployments.
 CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -3,42 +3,63 @@ import pandas as pd
 from huggingface_hub import hf_hub_download
 import joblib
 
-# Download and load the model
+# Download and load the model from Hugging Face Hub.
+# The model will be used for making predictions in the Streamlit app.
 model_path = hf_hub_download(repo_id="Garg06/Tourism-Package-Model", filename="best_machine_failure_model_v1.joblib")
 model = joblib.load(model_path)
 
-# Streamlit UI for Tourism Package Prediction
+# Set the title and description for the Streamlit web application.
 st.title("Tourism Package Prediction App")
 st.write("""
 This application predicts whether a customer will purchase the newly introduced Wellness Tourism Package.
 Please enter the customer details and interaction data below to get a prediction.
 """)
 
-# User input fields
+# User input fields for customer details, organized under a header.
 st.header("Customer Details")
+# Numerical input for Age, with defined min/max values and a default.
 age = st.number_input("Age", min_value=18, max_value=90, value=30)
+# Dropdown for Type of Contact, with string options.
 typeofcontact = st.selectbox("Type of Contact", options=['Company Invited', 'Self Inquiry'])
+# Numerical input for City Tier.
 citytier = st.number_input("City Tier (1, 2, or 3)", min_value=1, max_value=3, value=1)
+# Dropdown for Occupation.
 occupation = st.selectbox("Occupation", options=['Freelancer', 'Large Business', 'Salaried', 'Small Business', 'Unemployed'])
+# Dropdown for Gender.
 gender = st.selectbox("Gender", options=['Female', 'Male'])
+# Numerical input for Number of Persons Visiting.
 numberofpersonvisiting = st.number_input("Number of Persons Visiting", min_value=1, max_value=10, value=1)
+# Numerical input for Preferred Property Star rating.
 preferredpropertystar = st.number_input("Preferred Property Star (e.g., 3, 4, 5)", min_value=1, max_value=5, value=3)
+# Dropdown for Marital Status.
 maritalstatus = st.selectbox("Marital Status", options=['Divorced', 'Married', 'Single'])
+# Numerical input for Number of Trips Annually.
 numberoftrips = st.number_input("Number of Trips Annually", min_value=0, max_value=50, value=5)
+# Dropdown for Passport, with custom display for 0/1.
 passport = st.selectbox("Passport", options=[0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+# Dropdown for Own Car, with custom display for 0/1.
 owncar = st.selectbox("Own Car", options=[0, 1], format_func=lambda x: "Yes" if x == 1 else "No")
+# Numerical input for Number of Children Visiting.
 numberofchildrenvisiting = st.number_input("Number of Children Visiting (below age 5)", min_value=0, max_value=5, value=0)
+# Dropdown for Designation.
 designation = st.selectbox("Designation", options=['Director', 'Executive', 'Manager', 'Senior Executive', 'VP'])
+# Numerical input for Monthly Income.
 monthlyincome = st.number_input("Monthly Income", min_value=0.0, max_value=1000000.0, value=50000.0, step=100.0)
 
+# User input fields for customer interaction data, organized under a header.
 st.header("Customer Interaction Data")
+# Numerical input for Pitch Satisfaction Score.
 pitchsatisfactionscore = st.number_input("Pitch Satisfaction Score (1-5)", min_value=1, max_value=5, value=3)
+# Dropdown for Product Pitched.
 productpitched = st.selectbox("Product Pitched", options=['Basic', 'Deluxe', 'King', 'Standard', 'Super Deluxe'])
+# Numerical input for Number of Follow-ups.
 numberoffollowups = st.number_input("Number of Follow-ups", min_value=0, max_value=20, value=3)
+# Numerical input for Duration of Pitch.
 durationofpitch = st.number_input("Duration of Pitch (minutes)", min_value=0.0, max_value=60.0, value=15.0, step=0.5)
 
 
-# Assemble input into DataFrame, ensuring column order matches training data
+# Assemble the user input into a Pandas DataFrame.
+# The column names must exactly match those expected by the trained model.
 input_data = pd.DataFrame([{
     'Age': age,
     'TypeofContact': typeofcontact,
@@ -61,12 +82,16 @@ input_data = pd.DataFrame([{
 }])
 
 
+# When the "Predict Purchase" button is clicked:
 if st.button("Predict Purchase"):
+    # Get prediction probabilities from the model.
     prediction_proba = model.predict_proba(input_data)[:, 1]
-    # Using the classification_threshold defined in train.py
+    # Define the classification threshold (as used during model evaluation).
     classification_threshold = 0.45
+    # Convert probabilities to binary predictions based on the threshold.
     prediction = (prediction_proba >= classification_threshold).astype(int)[0]
 
+    # Display the prediction result to the user.
     result = "Customer WILL purchase the Wellness Tourism Package" if prediction == 1 else "Customer will NOT purchase the Wellness Tourism Package"
     st.subheader("Prediction Result:")
     st.success(f"The model predicts: **{result}**")

requirements.txt

@@ -1,3 +1,5 @@
+# Define the Python package dependencies for the Streamlit application.
+# These versions ensure reproducibility of the deployment environment.
 pandas==2.2.2
 huggingface_hub==0.32.6
 streamlit==1.43.2