Update app.py

app.py

@@ -63,6 +63,7 @@ def process_video(video_path, true_label=None):
     qcnn_class1 = 0
     total_frames = len(sampled_frames)
     for frame in sampled_frames:
+        frame = frame.reshape(1, -1)  # Flatten the frame
         cnn_pred = cnn_model.predict(frame)
         cnn_label = np.argmax(cnn_pred)
         if cnn_label == 0:
@@ -125,21 +126,44 @@ def predict(video_input):
     return result
 
 with gr.Blocks() as demo:
-    gr.Markdown("<h1 style='text-align: center;'>Quanvolutional Neural Networks for Deepfake Detection</h1>")
-    gr.Markdown("<h2 style='text-align: center;'>Steven Fernandes, Ph.D.</h2>")
-    
-    with gr.Row():
-        with gr.Column():
-            video_input = gr.Video(label="Upload Video or Select an Example")
+    gr.HTML("<h1 style='text-align: center;'>Quanvolutional Neural Networks for Deepfake Detection</h1>")
+    gr.HTML("<h2 style='text-align: center;'>Steven Fernandes, Ph.D.</h2>")
+
+    with gr.Tabs():
+        with gr.TabItem("Original Videos"):
+            original_input = gr.Video(label="Upload Original Video or Select an Example")
             gr.Examples(
-                examples=[example['path'] for example in examples],
-                inputs=video_input,
-                label="Examples"
+                examples=[example['path'] for example in original_videos],
+                inputs=original_input,
+                label="Original Examples"
             )
-            predict_button = gr.Button("Predict")
-        with gr.Column():
-            output = gr.Textbox(label="Result")
-    
-    predict_button.click(fn=predict, inputs=video_input, outputs=output)
-    
+        with gr.TabItem("Deepfake Roop Videos"):
+            deepfake_roop_input = gr.Video(label="Upload Deepfake Roop Video or Select an Example")
+            gr.Examples(
+                examples=[example['path'] for example in deepfake_roop_videos],
+                inputs=deepfake_roop_input,
+                label="Deepfake Roop Examples"
+            )
+        with gr.TabItem("Deepfake Web Videos"):
+            deepfake_web_input = gr.Video(label="Upload Deepfake Web Video or Select an Example")
+            gr.Examples(
+                examples=[example['path'] for example in deepfake_web_videos],
+                inputs=deepfake_web_input,
+                label="Deepfake Web Examples"
+            )
+
+    predict_button = gr.Button("Predict")
+    output = gr.Textbox(label="Result")
+
+    # Function to handle input from any tab
+    def predict_combined(original_video, deepfake_roop_video, deepfake_web_video):
+        video_input = original_video or deepfake_roop_video or deepfake_web_video
+        return predict(video_input)
+
+    predict_button.click(
+        fn=predict_combined,
+        inputs=[original_input, deepfake_roop_input, deepfake_web_input],
+        outputs=output
+    )
+
     demo.launch()