app.py

import gradio as gr
from transformers import pipeline
import pandas as pd
import os

# Initialize detector
detector = pipeline("text-classification", model="debojit01/fake-review-detector")

# CSV file setup
FEEDBACK_FILE = "training_data.csv"
if not os.path.exists(FEEDBACK_FILE):
    pd.DataFrame(columns=["text", "label"]).to_csv(FEEDBACK_FILE, index=False)

def predict(text):
    result = detector(text)[0]
    if result["label"] == "LABEL_0":  # Real
        return {"Real": result["score"], "Fake": 1 - result["score"]}
    else:  # Fake (LABEL_1)
        return {"Real": 1 - result["score"], "Fake": result["score"]}

def save_feedback(text, prediction, is_correct):
    """Save feedback only when user submits"""
    if is_correct is None:  # No feedback provided
        return "Prediction shown"
    
    predicted_label = "Real" if prediction["Real"] > 0.5 else "Fake"
    true_label = predicted_label if is_correct else ("Fake" if predicted_label == "Real" else "Real")
    
    new_data = pd.DataFrame({"text": [text], "label": [true_label]})
    new_data.to_csv(FEEDBACK_FILE, mode='a', header=not os.path.exists(FEEDBACK_FILE), index=False)
    return "Thank you for your feedback!"

with gr.Blocks() as app:
    gr.Markdown("## Fake Review Detector")
    
    with gr.Row():
        review_input = gr.Textbox(label="Enter Review")
        predict_btn = gr.Button("Predict")
    
    output_label = gr.Label(label="Prediction")
    
    with gr.Row(visible=False) as feedback_row:
        feedback_radio = gr.Radio(
            ["Correct", "Incorrect"],
            label="Is this prediction accurate?"
        )
        feedback_btn = gr.Button("Submit Feedback")
    
    status_text = gr.Textbox(label="Status", interactive=False)

    def show_prediction(text):
        prediction = predict(text)
        return prediction, gr.Row(visible=True), ""

    predict_btn.click(
        show_prediction,
        inputs=review_input,
        outputs=[output_label, feedback_row, status_text]
    )

    feedback_btn.click(
        save_feedback,
        inputs=[review_input, output_label, feedback_radio],
        outputs=status_text
    )

app.launch()