import torch import gradio as gr from huggingface_hub import hf_hub_download from PIL import Image yolov7_weights = hf_hub_download(repo_id="LailaMB/visual_pollution_detection", filename="best_640_rpoch56.pt") model = torch.hub.load('WongKinYiu/yolov7:main', 'custom', yolov7_weights, force_reload=True) # local repo def object_detection(im, size=640): results = model(im) # inference #results.print() # print results to screen #results.show() # display results #results.save() # save as results1.jpg, results2.jpg... etc. results.render() # updates results.imgs with boxes and labels return Image.fromarray(results.imgs[0]) image = gr.inputs.Image(shape=(640, 640), image_mode="RGB", source="upload", label="Imagem", optional=False) outputs = gr.outputs.Image(type="pil", label="Output Image") gr.Interface( fn=object_detection, inputs=image, outputs=outputs, title="Visual Pollution Detection", description="Demo for Visual Pollution Detection Model. The model which was developed by AICAS_KSU team to solve the Theme1 problem of the Smartathon .", examples=[],cache_examples=False).launch()