Update app.py

app.py

@@ -2,43 +2,40 @@ 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 once
+# Download and 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: Image.Image) -> str:
-    try:
-        image_np = np.array(image.convert("RGB"))
-        xcp_img = cv2.resize(image_np, (299, 299))
-        eff_img = cv2.resize(image_np, (224, 224))
-
-        xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
-        eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...]
-
-        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 "Error: " + str(e)
-
-# ✅ Use Blocks instead of Interface to avoid schema bugs
-with gr.Blocks() as demo:
-    with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload Image")
-    with gr.Row():
-        output = gr.Textbox(label="Prediction")
-    image_input.change(fn=predict, inputs=image_input, outputs=output)
-
-if __name__ == "__main__":
-    demo.launch()
+def predict(image):
+    # Use the full image directly (no face extraction)
+    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, ...]
+
+    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
+    label = "Real" if avg_pred > 0.5 else "Fake"
+
+    return  label
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Label(label="Prediction"),
+    title="Deepfake Image Detector",
+    description="Upload a full image. The model classifies it as real or fake."
+)
+
+interface.launch()