add imports

app.py

@@ -5,9 +5,16 @@ import torchvision
 import numpy as np
 from PIL import Image
 
-# Load MegaDetector v5a model
+#script load
+import json
+import os
+import numpy as np
+import tensorflow.compat.v1 as tf 
+tf.disable_v2_behavior()
+from dlclive import DLCLive, Processor
+from numpy import savetxt
 
-# models = ["model_weights/md_v5a.0.0.pt","model_weights/md_v5b.0.0.pt"]
+# Load MegaDetector v5a model
 model = torch.hub.load('ultralytics/yolov5', 'custom', "model_weights/md_v5a.0.0.pt")
 
 def yolo(im, size=640):
@@ -20,18 +27,15 @@ def yolo(im, size=640):
     results.render()  # updates results.imgs with boxes and labels
     return Image.fromarray(results.imgs[0])
 
-#image = gr.inputs.Image(type="pil", label="Input Image")
-#chosen_model = gr.inputs.Dropdown(choices = models, value = "model_weights/md_v5a.0.0.pt",type = "value", label="Model Weight")
-#size = 640
-
-#inputs = [image, chosen_model, size]
-inputs = gr.inputs.Image(type="pil", label="Input Image")
-outputs = gr.outputs.Image(type="pil", label="Output Image")
-
+#Layouts and descriptions
 title = "MegaDetector and DeepLabcutLive"
 description = "Interact with MegaDetector and DeeplabCutLive for pose estimation"
-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>"
+article = "<p style='text-align: center'>This app uses MegaDetector 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>. We have also integrated DeepLabCut Live for pose estimation <a href='https://github.com/DeepLabCut/DeepLabCut-live'></a>.</p>"
 
+# input image and output image 
+inputs = gr.inputs.Image(type="pil", label="Input Image")
+outputs = gr.outputs.Image(type="pil", label="Output Image")
 
+#data images stored
 examples = [['data/owl.jpg'], ['data/snake.jpg'],['data/beluga.jpg'],['data/rhino.jpg']]
 gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(enable_queue=True)