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app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+from PIL import Image
+import requests
+import h5py
+import os
+
+# Download model only if not already present
+model_path = "xception_model.h5"
+if not os.path.exists(model_path):
+    url = "https://huggingface.co/Zeyadd-Mostaffa/cv_GP/resolve/main/xception_model.h5"
+    with open(model_path, "wb") as f:
+        f.write(requests.get(url).content)
+
+# Load model
+model = load_model(model_path)
+
+# Preprocess function
+def preprocess_image(image):
+    image = image.resize((150, 150)).convert("RGB")
+    arr = np.array(image) / 255.0
+    return np.expand_dims(arr, axis=0)
+
+# Prediction function
+def predict(image):
+    img = preprocess_image(image)
+    prob = model.predict(img)[0][0]
+    label = "REAL" if prob >= 0.5 else "FAKE"
+    return {"REAL": 1 - prob, "FAKE": prob}, f"Prediction: {label}"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Label(num_top_classes=2), gr.Text()],
+    title="Deepfake Detection (Xception Model)"
+)
+
+demo.launch()

requirements.txt

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+gradio
+tensorflow
+pillow
+numpy
+requests
+h5py