app.py

import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.xception import preprocess_input
from PIL import Image
from huggingface_hub import hf_hub_download

# Download and load the model
model_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/cv_GP", filename="xception_model.h5")
model = load_model(model_path)

# Inference function
def predict(image):
    image = image.resize((299, 299))  # Resize to match model input
    image = img_to_array(image)       # Convert to numpy array
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    image = preprocess_input(image)   # Apply Xception preprocessing (important fix!)

    prob = model.predict(image)[0][0]

    # Based on training: label 0 = Fake, label 1 = Real
    label = "Real" if prob > 0.5 else "Fake"
    confidence = round(float(prob if prob > 0.5 else 1 - prob), 3)

    return f"{label} ({confidence * 100:.1f}%)"

# Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Text(),
    title="Deepfake Detection (Xception Model)"
)

iface.launch()