app.py

import gradio as gr
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download

# Load model from Hugging Face Model Hub
model_path = hf_hub_download(
    repo_id="AngadSi/wellness-purchase-prediction-model",
    filename="wellness_purchase_model.joblib",
    repo_type="model"
)

model = joblib.load(model_path)

def predict_purchase(
    Age,
    MonthlyIncome,
    NumberOfTrips,
    PitchSatisfactionScore
):
    df = pd.DataFrame([{
        "Age": Age,
        "MonthlyIncome": MonthlyIncome,
        "NumberOfTrips": NumberOfTrips,
        "PitchSatisfactionScore": PitchSatisfactionScore
    }])

    pred = model.predict(df)[0]
    prob = model.predict_proba(df)[0][1]

    return {
        "Prediction": int(pred),
        "Purchase_Probability": round(prob, 3)
    }

demo = gr.Interface(
    fn=predict_purchase,
    inputs=[
        gr.Number(label="Age"),
        gr.Number(label="Monthly Income"),
        gr.Number(label="Number of Trips"),
        gr.Number(label="Pitch Satisfaction Score")
    ],
    outputs="json",
    title="Wellness Tourism Purchase Prediction",
    description="Predicts whether a customer will purchase the Wellness Tourism Package."
)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)