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| import os | |
| import copy | |
| import torch | |
| import gradio | |
| import gradio as gr | |
| from PIL import Image | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| os.system("wget https://www.dropbox.com/s/grcragozd4x79zc/model_hello1.pth") | |
| model = torch.load("./model_hello1.pth", map_location=device) | |
| # img = Image.open(path).convert('RGB') | |
| from torchvision import transforms | |
| transforms2 = transforms.Compose([ | |
| transforms.Resize(256), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| # img = transforms(img) | |
| # img = img.unsqueeze(0) | |
| model.eval() | |
| labels = ['pneumonia','normal'] | |
| # with torch.no_grad(): | |
| # # preds = | |
| # preds = model(img) | |
| # score, indices = torch.max(preds, 1) | |
| def recognize_digit(image): | |
| image = transforms2(image) | |
| image = image.unsqueeze(0) | |
| # image = image.unsqueeze(0) | |
| # image = image.reshape(1, -1) | |
| # with torch.no_grad(): | |
| # preds = | |
| # img = image.reshape((-1, 3, 256, 256)) | |
| preds = model(image).flatten() | |
| # prediction = model.predict(image).tolist()[0] | |
| # score, indices = torch.max(preds, 1) | |
| # return {str(indices.item())} | |
| return {labels[i]: float(preds[i]) for i in range(2)} | |
| im = gradio.inputs.Image( | |
| shape=(256, 256), image_mode="RGB", type="pil") | |
| iface = gr.Interface( | |
| recognize_digit, | |
| im, | |
| gradio.outputs.Label(num_top_classes=2), | |
| live=True, | |
| interpretation="default", | |
| examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]], | |
| capture_session=True, | |
| ) | |
| iface.test_launch() | |
| iface.launch() |