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Runtime error
| import os | |
| os.system("pip install --upgrade httpx") | |
| os.system("pip install --upgrade gradio") | |
| os.system("pip install opencv-python") | |
| os.system("pip install torch") | |
| os.system("pip install --upgrade pillow") | |
| os.system("pip install torchvision") | |
| import gradio as gr | |
| import torch | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| def predict(image): | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| model = torchvision.models.resnet50(pretrained=True).to(device) | |
| model.eval() | |
| transform = transforms.Compose([ | |
| transforms.Resize(256), | |
| transforms.CenterCrop(224), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| img = Image.fromarray(image.astype('uint8'), 'RGB') | |
| img = transform(img).unsqueeze(0).to(device) | |
| with torch.no_grad(): | |
| output = model(img) | |
| _, predicted = output.max(1) | |
| return predicted.item() | |
| input_image = gr.component.Image(type="filepath", label="Input") | |
| output_text = gr.outputs.Textbox() | |
| gr.Interface(fn=predict, inputs=input_image, outputs=output_text).launch() | |