Update app.py

app.py

@@ -9,10 +9,9 @@ from tensorflow.keras.applications.efficientnet import preprocess_input as eff_p
 from huggingface_hub import hf_hub_download
 from mtcnn import MTCNN
 
-# Load models
+# 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)
 
@@ -23,37 +22,43 @@ def extract_face(image):
     faces = detector.detect_faces(image)
     if not faces:
         return None
-    x, y, w, h = faces[0]["box"]
+    x, y, w, h = faces[0]['box']
     x, y = max(0, x), max(0, y)
-    return image[y:y+h, x:x+w]
+    face = image[y:y+h, x:x+w]
+    return face
 
 def predict(image):
     face = extract_face(image)
     if face is None:
-        return "No face detected"
+        return "No face detected", None
 
-    # Xception
+    # Prepare for Xception
     xcp_img = cv2.resize(face, (299, 299))
     xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
-    xcp_pred = xcp_model.predict(xcp_tensor, verbose=0)[0][0]
+    xcp_pred = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
 
-    # EfficientNet
+    # Prepare for EfficientNet
     eff_img = cv2.resize(face, (224, 224))
     eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...]
-    eff_pred = eff_model.predict(eff_tensor, verbose=0)[0][0]
+    eff_pred = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
+
+    # Ensemble average
+    avg_pred = (xcp_pred + eff_pred) / 2
+
+    # ✅ Important fix: if label "real" = 1, fake = 0, prediction > 0.5 = real
+    label = "Real" if avg_pred > 0.5 else "Fake"
 
-    # Ensemble
-    avg_pred = (xcp_pred + eff_pred) / 2.0
-    label = "Fake" if avg_pred > 0.5 else "Real"
-    return label
+    return label, face
 
-# Gradio interface
 interface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="numpy", label="Upload an image"),
-    outputs=gr.Label(label="Prediction"),
-    title="Deepfake Image Detector",
-    description="Upload an image. We detect the face and classify it using an ensemble of Xception + EfficientNetB4."
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=[
+        gr.Label(label="Prediction"),
+        gr.Image(type="numpy", label="Detected Face")
+    ],
+    title="Deepfake Image Detector (Ensemble: Xception + EfficientNetB4)",
+    description="Upload an image. The model detects the face, classifies it as real or fake using an ensemble of Xception and EfficientNetB4."
 )
 
 interface.launch()