Update app.py

app.py

@@ -7,7 +7,6 @@ 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
-from mtcnn import MTCNN
 
 # Download and load models
 xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="xception_model.h5")
@@ -15,52 +14,32 @@ eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", fi
 xcp_model = load_model(xcp_path)
 eff_model = load_model(eff_path)
 
-# Face detector
-detector = MTCNN()
-
-def extract_face(image):
-    faces = detector.detect_faces(image)
-    if not faces:
-        return None
-    x, y, w, h = faces[0]['box']
-    x, y = max(0, x), max(0, y)
-    return image[y:y+h, x:x+w]
-
 def predict(image):
-    face = extract_face(image)
-    if face is None:
-        return "No face detected", None
+    # Use the full image directly (no face extraction)
+    xcp_img = cv2.resize(image, (299, 299))
+    eff_img = cv2.resize(image, (224, 224))
 
-    # 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).flatten()[0]
-
-    # EfficientNet
-    eff_img = cv2.resize(face, (224, 224))
     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]
 
-    # Ensemble
     avg_pred = (xcp_pred + eff_pred) / 2
     label = "Real" if avg_pred > 0.5 else "Fake"
 
-    # Log probabilities
-    print(f"Xception: {xcp_pred:.4f}, EfficientNetB4: {eff_pred:.4f}, Ensemble Avg: {avg_pred:.4f}")
-
-    # Return label with confidence
     result = f"{label} (Avg: {avg_pred:.3f}, XCP: {xcp_pred:.3f}, EFF: {eff_pred:.3f})"
-    return result, face
+    return result, image
 
 interface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy", label="Upload Image"),
     outputs=[
         gr.Label(label="Prediction"),
-        gr.Image(type="numpy", label="Detected Face")
+        gr.Image(type="numpy", label="Input Image")
     ],
     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."
+    description="Upload a full image. The model classifies it as real or fake using an ensemble of Xception and EfficientNetB4."
 )
 
 interface.launch()