app.py

import os
import cv2
import numpy as np
import gradio as gr
from PIL import Image
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.xception import preprocess_input as xcp_pre
from tensorflow.keras.applications.efficientnet import preprocess_input as eff_pre
from huggingface_hub import hf_hub_download

# Load models
xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="xception_model.h5")
eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="efficientnet_model.h5")
xcp_model = load_model(xcp_path)
eff_model = load_model(eff_path)

def predict(image_pil: Image.Image) -> str:
    try:
        # Convert PIL to numpy
        image = np.array(image_pil.convert("RGB"))

        # Resize and preprocess
        xcp_img = cv2.resize(image, (299, 299))
        eff_img = cv2.resize(image, (224, 224))

        xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
        eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...]

        # Predict
        xcp_pred = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
        eff_pred = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
        avg_pred = (xcp_pred + eff_pred) / 2

        return "Real" if avg_pred > 0.5 else "Fake"
    except Exception as e:
        return f"Error: {str(e)}"

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", label="Upload Image"),  # ✅ Use PIL instead of numpy
    outputs=gr.Textbox(label="Prediction"),             # ✅ Safe, schema-compatible
    title="Deepfake Image Detector",
    description="Upload a full image. The model classifies it as real or fake.",
    allow_flagging="never"
)

if __name__ == "__main__":
    demo.launch()