app.py

import gradio as gr
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 🔹 Load pretrained classification model
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 🔹 Sample Telecom Dataset
sample_data = pd.DataFrame([
    {
        "gender": "Female", "SeniorCitizen": 0, "Partner": "Yes", "Dependents": "No",
        "tenure": 1, "PhoneService": "No", "InternetService": "DSL",
        "Contract": "Month-to-month", "MonthlyCharges": 29.85
    },
    {
        "gender": "Male", "SeniorCitizen": 1, "Partner": "No", "Dependents": "No",
        "tenure": 34, "PhoneService": "Yes", "InternetService": "Fiber optic",
        "Contract": "One year", "MonthlyCharges": 56.95
    },
    {
        "gender": "Female", "SeniorCitizen": 0, "Partner": "Yes", "Dependents": "Yes",
        "tenure": 2, "PhoneService": "Yes", "InternetService": "DSL",
        "Contract": "Month-to-month", "MonthlyCharges": 53.85
    }
])

# 🔹 Convert structured row to prompt
def preprocess(row):
    return (
        f"Customer: {row['gender']}, senior: {row['SeniorCitizen']}, partner: {row['Partner']}, "
        f"dependents: {row['Dependents']}, tenure: {row['tenure']}, phone: {row['PhoneService']}, "
        f"internet: {row['InternetService']}, contract: {row['Contract']}, charges: {row['MonthlyCharges']}. "
        f"Will they churn?"
    )

# 🔹 Prediction logic
def predict_churn(input_mode, table=None):
    df = sample_data.copy() if input_mode == "Use Sample Data" else pd.DataFrame(table or [])
    if df.empty:
        return pd.DataFrame()

    results = []
    for _, row in df.iterrows():
        try:
            prompt = preprocess(row)
            inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
            with torch.no_grad():
                logits = model(**inputs).logits
            pred = torch.argmax(logits, dim=1).item()
            results.append("Churn" if pred == 1 else "No Churn")
        except Exception:
            results.append("Error")
    df["Prediction"] = results
    return df

# 🔹 Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 📞 Telecom Customer Churn Predictor")
    input_mode = gr.Radio(["Use Sample Data", "Manual Entry"], label="Choose Input Mode", value="Use Sample Data")
    table_input = gr.Dataframe(
        headers=["gender", "SeniorCitizen", "Partner", "Dependents", "tenure", "PhoneService", "InternetService", "Contract", "MonthlyCharges"],
        label="Enter Customer Data",
        row_count=3
    )
    output = gr.Dataframe(label="Prediction Results")
    run_btn = gr.Button("Predict")

    run_btn.click(fn=predict_churn, inputs=[input_mode, table_input], outputs=output)

demo.launch()