Update app.py

app.py

@@ -15,23 +15,17 @@ deepfake_roop_dir = os.path.join(examples_dir, 'DeepfakeRoop')
 deepfake_web_dir = os.path.join(examples_dir, 'DeepfakeWeb')
 
 # Function to get video paths from a directory
-def get_video_paths(directory, label):
+def get_video_paths(directory):
     videos = []
     for vid in os.listdir(directory):
         if vid.endswith('.mp4'):
-            videos.append({'path': os.path.join(directory, vid), 'label': label})
+            videos.append(os.path.join(directory, vid))
     return videos
 
 # Get video paths for each category
-original_videos = get_video_paths(original_dir, 'Original')
-deepfake_roop_videos = get_video_paths(deepfake_roop_dir, 'DeepfakeRoop')
-deepfake_web_videos = get_video_paths(deepfake_web_dir, 'DeepfakeWeb')
-
-# Combine all examples
-examples = original_videos + deepfake_roop_videos + deepfake_web_videos
-
-# Map from example video path to label
-example_videos_dict = {example['path']: example['label'] for example in examples}
+original_videos = get_video_paths(original_dir)
+deepfake_roop_videos = get_video_paths(deepfake_roop_dir)
+deepfake_web_videos = get_video_paths(deepfake_web_dir)
 
 def process_video(video_path, true_label=None):
     cap = cv2.VideoCapture(video_path)
@@ -128,42 +122,28 @@ def predict(video_input):
 with gr.Blocks() as demo:
     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 original_videos],
-                inputs=original_input,
-                label="Original Examples"
+    
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.Video(label="Upload Video", type="file")
+            examples_original = gr.Examples(
+                label="Original Videos",
+                inputs=video_input,
+                examples=original_videos
             )
-        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"
+            examples_deepfake_roop = gr.Examples(
+                label="Deepfake Roop Videos",
+                inputs=video_input,
+                examples=deepfake_roop_videos
             )
-        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"
+            examples_deepfake_web = gr.Examples(
+                label="Deepfake Web Videos",
+                inputs=video_input,
+                examples=deepfake_web_videos
             )
-
-    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
-    )
-
+            predict_button = gr.Button("Predict")
+        with gr.Column():
+            output = gr.Textbox(label="Result")
+    
+    predict_button.click(fn=predict, inputs=video_input, outputs=output)
     demo.launch()