change to live action

app.py

@@ -163,19 +163,10 @@ def predict_pipeline(img_input,
     ############################################################
     ## Get DLC model and labels as strings   
     if model_input_str == 'full_cat':
-        path_to_DLCmodel =  "model/DLC_Cat_resnet_50_iteration-0_shuffle-0"
+        path_to_DLCmodel =  "DLC_models/DLC_Cat_resnet_50_iteration-0_shuffle-0"
         pose_cfg_path = os.path.join(path_to_DLCmodel,'pose_cfg.yaml')
     elif model_input_str == 'full_dog':
-        path_to_DLCmodel =  "model/DLC_Dog_resnet_50_iteration-0_shuffle-0"
-        pose_cfg_path = os.path.join(path_to_DLCmodel,'pose_cfg.yaml')
-    elif model_input_str == 'full_monkey':
-        path_to_DLCmodel =  "model/DLC_monkey_resnet_50_iteration-0_shuffle-1"
-        pose_cfg_path = os.path.join(path_to_DLCmodel,'pose_cfg.yaml')
-    elif model_input_str == 'full_human':
-        path_to_DLCmodel =  "model/DLC_human_dancing_resnet_101_iteration-0_shuffle-1"
-        pose_cfg_path = os.path.join(path_to_DLCmodel,'pose_cfg.yaml')
-    elif model_input_str == 'monkey_face':
-        path_to_DLCmodel =  "model/DLC_FacialLandmarks_resnet_50_iteration-1_shuffle-1"
+        path_to_DLCmodel =  "DLC_models/DLC_Dog_resnet_50_iteration-0_shuffle-0"
         pose_cfg_path = os.path.join(path_to_DLCmodel,'pose_cfg.yaml')
 
     # read pose cfg as dict
@@ -247,14 +238,14 @@ def predict_pipeline(img_input,
 # Get MegaDetector model
 # TODO: Allow user selectable model?
 # models = ["model_weights/md_v5a.0.0.pt","model_weights/md_v5b.0.0.pt"]
-MD_model = torch.hub.load('ultralytics/yolov5', 'custom', "megadet_model/md_v5b.0.0.pt")
+MD_model = torch.hub.load('ultralytics/yolov5', 'custom', "model_weights/md_v5a.0.0.pt")
 
 
 ####################################################
 # Create user interface and launch
 gr_image_input = gr.inputs.Image(type="pil", label="Input Image")
 gr_image_output = gr.outputs.Image(type="pil", label="Output Image")
-gr_dlc_model_input = gr.inputs.Dropdown(choices=['full_cat','full_dog', 'full monkey', 'full_human', 'monkey_face'], # choices
+gr_dlc_model_input = gr.inputs.Dropdown(choices=['full_cat','full_dog'], # choices
                                         default='full_cat', # default option
                                         type='value', # Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected.
                                         label='Select DLC model')
@@ -275,7 +266,7 @@ gr_description = "Detect and estimate the pose of animals in camera trap images,
 # article = "<p style='text-align: center'>This app makes predictions using a YOLOv5x6 model that was trained to detect animals, humans, and vehicles in camera trap images; find out more about the project on <a href='https://github.com/microsoft/CameraTraps'>GitHub</a>. This app was built by Henry Lydecker but really depends on code and models developed by <a href='http://ecologize.org/'>Ecologize</a> and <a href='http://aka.ms/aiforearth'>Microsoft AI for Earth</a>. Find out more about the YOLO model from the original creator, <a href='https://pjreddie.com/darknet/yolo/'>Joseph Redmon</a>. YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset and developed by Ultralytics, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>"
 # examples = [['data/Macropod.jpg'], ['data/koala2.jpg'],['data/cat.jpg'],['data/BrushtailPossum.jpg']]
 
-gr.Interface(predict_pipeline, 
+demo = gr.Interface(predict_pipeline, 
              inputs=[gr_image_input,
                      gr_dlc_model_input,
                      gr_dlc_only_checkbox,
@@ -284,8 +275,10 @@ gr.Interface(predict_pipeline,
              outputs=gr_image_output, 
              title=gr_title, 
              description=gr_description,
-             theme="huggingface").launch(enable_queue=True)
+             theme="huggingface",
+             live=True)
 
+demo.launch(enable_queue=True)
 
 # def dlclive_pose(model, crop_np, crop, fname, index,dlc_proc):
 #     dlc_live = DLCLive(model, processor=dlc_proc)