Update app.py

app.py

@@ -6,7 +6,8 @@ from transformers import Owlv2Processor, Owlv2ForObjectDetection
 import numpy as np
 import os
 
-device = 'cuda'
+# Check if CUDA is available, otherwise use CPU
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16")
 model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16").to(device)
@@ -57,7 +58,6 @@ def process_video(video_path, target, progress=gr.Progress()):
 
     frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
     original_fps = int(cap.get(cv2.CAP_PROP_FPS))
-    original_duration = frame_count / original_fps
     output_fps = 5
 
     output_path = "output_video.mp4"
@@ -113,13 +113,8 @@ def gradio_app():
         use_sample_button = gr.Button("Use Sample Video")
         progress_bar = gr.Progress()
 
-        video_path = gr.State(None)
         def process_and_update(video, target):
             output_video_path, error = process_video(video, target, progress_bar)
-            if error:
-                error_output.visible = True
-            else:
-                error_output.visible = False
             return output_video_path, error
 
         video_input.upload(process_and_update, 
@@ -128,7 +123,8 @@ def gradio_app():
 
         def use_sample_video():
             sample_video_path = "Drone Video of African Wildlife Wild Botswan.mp4"
-            return process_and_update(sample_video_path, "animal")
+            output_video_path, error = process_and_update(sample_video_path, "animal")
+            return output_video_path, error
 
         use_sample_button.click(use_sample_video, 
                                 inputs=None,