app.py

import gradio as gr
from web_backend import predict_image_pil

def analyze_image(image):
    label, confidence, heatmap = predict_image_pil(image)

    if label == "Fake":
        if confidence >= 90:
            risk = "🚨 High likelihood of Deepfake"
        elif confidence >= 60:
            risk = "⚠️ Possibly Deepfake"
        else:
            risk = "⚠️ Uncertain Deepfake"
    else:
        if confidence >= 90:
            risk = "✅ Likely Real"
        elif confidence >= 60:
            risk = "⚠️ Possibly Real"
        else:
            risk = "⚠️ Uncertain – Needs Review"

    return label, f"{confidence} %", risk, heatmap

with gr.Blocks() as demo:
    # -------- MAIN TITLE ONLY --------
    gr.Markdown("# 🧠 Deepfake Image Detection System")

    with gr.Row():
        # LEFT SIDE
        with gr.Column(scale=1):
            image_input = gr.Image(
                label="Upload Image",
                type="pil",
                height=280
            )

            submit_btn = gr.Button("Submit")
            clear_btn = gr.Button("Clear")

        # RIGHT SIDE
        with gr.Column(scale=2):
            prediction = gr.Text(label="Prediction")
            confidence = gr.Text(label="Confidence")
            risk = gr.Text(label="Risk Assessment")

            heatmap = gr.Image(
                label="Explainability Heatmap",
                height=280
            )

    submit_btn.click(
        fn=analyze_image,
        inputs=image_input,
        outputs=[prediction, confidence, risk, heatmap]
    )

    clear_btn.click(
        fn=lambda: (None, "", "", None),
        inputs=None,
        outputs=[image_input, prediction, confidence, risk]
    )

demo.launch()