import gradio as gr import os import torch from PIL import Image #subprocess.run(["mv","content/custom_data.yaml","./yolov5/data"]) def load_model(): ''' Loading hub model & setting the preferences for the model ''' model = torch.hub.load('ultralytics/yolov5', 'custom', path='Content/cnn.pt') model.conf = 0.38 model.dnn=True model.agnostic=True return model model=load_model() #, force_reload=True def detect(inp): #g = (size / max(inp.size)) #gain #im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize results = model(inp,size=640) # inference print(results) results.render() # updates results.imgs with boxes and labels return Image.fromarray(results.imgs[0]) inp = gr.inputs.Image(type="pil", label="Original Image") output = gr.outputs.Image(type="pil", label="Output Image") io=gr.Interface(fn=detect, inputs=inp, outputs=output, title='Mosquito Habitat Identification',theme='peach') io.launch(debug=True,share=False) #examples=['Content/4.jpg','Content/10.jpg','Content/18.jpg']