added seperate tab for tracking

app.py

@@ -175,9 +175,9 @@ def inference2(video,model_link,iou_threshold,confidence_threshold):
     return 'output.mp4',np.mean(fps_video)
 
 
-def MODT(sourceVideo, model_link, trackingmethod):    
-    model_path = 'weights/'+str(model_link)+'.pt'
-    print("Inside function MODT. Model : {model_link}, model path : {model_path}")
+def MODT(sourceVideo, trackingmethod):    
+    #model_path = 'weights/'+str(model_link)+'.pt'
+    model_path = 'weights/yolov8m.pt'
     return MOT(model_path, trackingmethod, sourceVideo), 30
 
 
@@ -192,7 +192,7 @@ trackers = ['strongsort', 'bytetrack', 'ocsort']
 
 with gr.Blocks() as demo:
     gr.Markdown("## IDD Inference on Yolo V7 and V8 ")
-    with gr.Tab("Image"):
+    with gr.Tab("Image Detection"):
         gr.Markdown("## Yolo V7 and V8 Inference on Image")
         with gr.Row():
             image_input = gr.Image(type='pil', label="Input Image", source="upload")
@@ -203,19 +203,30 @@ with gr.Blocks() as demo:
         image_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
         gr.Examples(examples=examples_images,inputs=image_input,outputs=image_output)
         text_button = gr.Button("Detect")
-    with gr.Tab("Video"):
+    with gr.Tab("Video Detection"):
         gr.Markdown("## Yolo V7 and V8 Inference on Video")
         with gr.Row():
             video_input = gr.Video(type='pil', label="Input Video", source="upload")
             video_output = gr.Video(type="pil", label="Output Video",format="mp4")
         fps_video = gr.Number(0,label='FPS')
-        video_drop = gr.Dropdown(choices=models,value=models[0],label = "Select the model")
-        tracking_drop = gr.Dropdown(choices=trackers,value=trackers[0], label="Select the tracking method")
+        video_drop = gr.Dropdown(choices=models,value=models[0],label = "Select the model")       
         video_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
         video_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
         gr.Examples(examples=examples_videos,inputs=video_input,outputs=video_output)
-        video_button_detect = gr.Button("Detect")
+        video_button_detect = gr.Button("Detect")        
+    with gr.Tab("Video Tacking"):
+        gr.Markdown("## MOT using YoloV8 detection with tracking")
+        with gr.Row():
+            videotr_input = gr.Video(type='pil', label="Input Video", source="upload")
+            videotr_output = gr.Video(type="pil", label="Output Video",format="mp4")
+        fpstr_video = gr.Number(0,label='FPS')
+        videotr_input = gr.Dropdown(choices=models,value=models[0],label = "Select the model")
+        tracking_drop = gr.Dropdown(choices=trackers,value=trackers[0], label="Select the tracking method")
+        videotr_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
+        videotr_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
+        gr.Examples(examples=examples_videos,inputs=video_input,outputs=video_output)
         video_button_track = gr.Button("Track")
+        
     # with gr.Tab("Webcam Video"):
     #     gr.Markdown("## YOLOv7 Inference on Webcam Video")
     #     gr.Markdown("Coming Soon")
@@ -226,7 +237,8 @@ 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=[video_input,video_drop, tracking_drop],          
+    video_button_track.click(MODT,inputs=[videotr_input, tracking_drop],          
                              outputs=[video_output, fps_video])
+   
 
 demo.launch(debug=True,enable_queue=True)