app.py

import os
import subprocess
import gradio as gr
import pandas as pd
import joblib

MODEL_PATH="models/pipeline.joblib"

def load_model():
    if os.path.exists(MODEL_PATH):
        return joblib.load(MODEL_PATH)
    return None

model=load_model()

def predict(age,balance):
    global model
    if model is None:
        return "Model not trained yet. Run pipeline first."
    df=pd.DataFrame([[age,balance]],columns=["Age","Balance"])
    p=model.predict(df)[0]
    return f"Prediction: {p}"

def run_pipeline():
    proc=subprocess.Popen(
        ["python","scripts/pipeline.py"],
        stdout=subprocess.PIPE,
        stderr=subprocess.STDOUT,
        text=True
    )
    log=""
    for line in proc.stdout:
        log+=line
        yield log

def build_ui():
    css=open("style.css").read()

    with gr.Blocks() as demo:
        gr.HTML(f"<style>{css}</style>")

        gr.Markdown("# Bank Churn Demo")

        with gr.Tab("Pipeline"):
            btn=gr.Button("Run Pipeline")
            log=gr.Textbox(lines=20,label="Execution Log")
            btn.click(run_pipeline,outputs=log)

        with gr.Tab("Prediction"):
            age=gr.Number(label="Age")
            balance=gr.Number(label="Balance")
            btn2=gr.Button("Predict")
            out=gr.Textbox()

            btn2.click(predict,[age,balance],out)

    return demo

if __name__=="__main__":
    demo=build_ui()
    demo.queue()
    port=int(os.environ.get("PORT",7860))
    demo.launch(server_name="0.0.0.0",server_port=port,show_api=False)