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| import gradio as gr | |
| import torch | |
| import requests | |
| from timm import create_model | |
| from timm.data import resolve_data_config | |
| from timm.data.transforms_factory import create_transform | |
| title = "Image Classifier Four -- Timm Resnet-50" | |
| description = """This machine has vision. It can see objects and concepts in an image. To test the machine, upload or drop an image, submit and read the results. The results comprise a list of words that the machine sees in the image. Beside a word, the length of the bar indicates the confidence with which the machine sees the word. The longer the bar, the more confident the machine is. | |
| """ | |
| article = "This app was made by following [this guys' space](https://huggingface.co/spaces/nateraw/gradio-demo)." | |
| IMAGENET_1K_URL = "https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt" | |
| LABELS = requests.get(IMAGENET_1K_URL).text.strip().split("\n") | |
| model = create_model('resnet50', pretrained=True) | |
| transform = create_transform( | |
| **resolve_data_config({}, model=model) | |
| ) | |
| model.eval() | |
| def predict_fn(img): | |
| img = img.convert('RGB') | |
| img = transform(img).unsqueeze(0) | |
| with torch.no_grad(): | |
| out = model(img) | |
| probabilities = torch.nn.functional.softmax(out[0], dim=0) | |
| values, indices = torch.topk(probabilities, k=3) | |
| return {LABELS[i]: v.item() for i, v in zip(indices, values)} | |
| gr.Interface(predict_fn, | |
| gr.inputs.Image(type='pil'), | |
| outputs='label', | |
| title = title, | |
| description = description, | |
| article = article).launch() | |