Update app.py

app.py

@@ -26,7 +26,7 @@ model_s = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-S")
 detector = MTCNN()
 
 
-def deepfakespredict(input_img, select_model):
+def deepfakespredict(select_model, input_img ):
 
     tf.keras.backend.clear_session()
     
@@ -66,11 +66,27 @@ def deepfakespredict(input_img, select_model):
 
 title="EfficientNetV2 Deepfakes Image Detector"
 description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector. To use it, simply upload your image, or click one of the examples to load them."
-examples = []
+examples = [
+            [
+                ['deepfakes-test-images/Fake-1.jpg'],
+                ['deepfakes-test-images/Fake-2.jpg'],
+                ['deepfakes-test-images/Fake-3.jpg'],
+                ['deepfakes-test-images/Fake-4.jpg'],
+                ['deepfakes-test-images/Fake-5.jpg']
+            ],
+            [
+                ['deepfakes-test-images/Real-1.jpg'],
+                ['deepfakes-test-images/Real-2.jpg'],
+                ['deepfakes-test-images/Real-3.jpg'],
+                ['deepfakes-test-images/Real-4.jpg'],
+                ['deepfakes-test-images/Real-5.jpg'],
+            ]
+           ]
+            
 gr.Interface(deepfakespredict,
                      inputs = [gr.inputs.Radio(["EfficientNetV2-B0", "EfficientNetV2-S"], label = "Select model:"), "image"],
                      outputs=["text", gr.outputs.Image(type="pil", label="Detected face"), gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")],
                      title=title,
-                     description=description
-
+                     description=description,
+                     examples = examples
                      ).launch()