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app.py

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+# Copied megadetector section from https://huggingface.co/spaces/hlydecker/MegaDetector_v5 
+
+# %%
+#all imports
+import gradio as gr
+import torch
+import torchvision
+import numpy as np
+from PIL import Image
+
+# %%
+# Loads a model from github repo, but you need to have the model 
+model = torch.hub.load('ultralytics/yolov5', 'custom', "/home/vic/vic_data/dlclive4mega/models/md_v5b.0.0.pt", force_reload=True)
+
+# %% 
+
+#not sure if we need to resize... maybe just the origin image and see? 
+""" def yolo(im): #size=640):
+g = (size / max(im.size))  # gain
+    im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS)  # resize 
+    
+    model = torch.hub.load('ultralytics/yolov5', 'custom', "/home/vic/vic_data/dlclive4mega/models/md_v5b.0.0.pt", force_reload=True)
+    
+    results = model(im)  # inference
+    results.render()  # updates results.imgs with boxes and labels
+    return Image.fromarray(results.imgs[0])  """
+
+def yolo(im): #size=640):
+    model = torch.hub.load('ultralytics/yolov5', 'custom', "/home/vic/vic_data/dlclive4mega/models/md_v5b.0.0.pt", force_reload=True)
+    results = model(im)  # inference
+    results.render()  # updates results.imgs with boxes and labels
+    return Image.fromarray(results.imgs[0])
+
+
+# %% 
+#inputs = [image, chosen_model, size]
+#this is where you show the image as interface 
+inputs = gr.inputs.Image(type="pil", label="Input Image")
+outputs = gr.outputs.Image(type="pil", label="Output Image")
+
+
+# %% 
+title = "MegaDetector with DeepLabCut pose estimation"
+description = "Detect and identify animals, people and vehicles in camera trap images followed by generating poses for humans and animals"
+article = "<p style='text-align: center'>Detect and identify animals, people and vehicles in camera trap images followed by generating poses for humans and animals</a></p>"
+
+# %% 
+#running the actual 
+gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, theme="huggingface").launch(enable_queue=True)