updated app.py for tracking

app.py

@@ -138,7 +138,7 @@ def inference(img,model_link,iou_threshold,confidence_threshold):
     device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
     # Load model
     model_path = 'weights/'+str(model_link)+'.pt'
-    if model_link== 'yolov8m':
+    if model_link== '.':
         model = YOLO(model_path)
         return detectv8(img,model,device,iou_threshold,confidence_threshold)
     else:
@@ -175,8 +175,9 @@ def inference2(video,model_link,iou_threshold,confidence_threshold):
     return 'output.mp4',np.mean(fps_video)
 
 
-def MODT():
-  return track.MOT()
+def MODT(sourceVideo, model_link, trackingmethod):
+    model_path = 'weights/'+str(model_link)+'.pt'
+    return track.MOT(model_path, trackingmethod, sourceVideo)
 
 
 examples_images = ['data/images/1.jpg',
@@ -222,7 +223,7 @@ with gr.Blocks() as demo:
     video_button_detect.click(inference2, inputs=[video_input,video_drop,
                                            video_iou_threshold,video_conf_threshold],            
                                         outputs=[video_output,fps_video])
-    video_button_track.click(MODT,inputs=[],          
-                             outputs=[video_output])
-
+    video_button_track.click(MODT,inputs=[video_input,video_drop, 'strongsort'],          
+                             outputs=[video_output, fps_video])
+, 
 demo.launch(debug=True,enable_queue=True)