mirror of
https://github.com/gradio-app/gradio.git
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597337dcb8
* added playground with 12 demos * change name to recipes, restyle navbar * add explanatory text to page * fix demo mapping * categorize demos, clean up design * styling * cateogry naming and emojis * refactor and add text demos * add view code button * remove opening slash in embed * styling * add image demos * adding plot demos * remove see code button * removed submodules * changes * add audio models * remove fun section * remove tests in image semgentation demo repo * requested changes * add outbreak_forecast * fix broken demos * remove images and models, add new demos * remove readmes, change to run.py, add description as comment * move to /demos folder, clean up dict * add upload_to_spaces script * fix script, clean repos, and add to docker file * fix python versioning issue * env variable * fix * env fixes * spaces instead of tabs * revert to original networking.py * fix rate limiting in asr and autocomplete * change name to demos * clean up navbar * move url and description, remove code comments * add tabs to demos * remove margins and footer from embedded demo * font consistency Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
23 lines
732 B
Python
23 lines
732 B
Python
import gradio as gr
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import torch
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import requests
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from torchvision import transforms
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model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
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response = requests.get("https://git.io/JJkYN")
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labels = response.text.split("\n")
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def predict(inp):
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inp = transforms.ToTensor()(inp).unsqueeze(0)
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with torch.no_grad():
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prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
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confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
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return confidences
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demo = gr.Interface(fn=predict,
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inputs=gr.inputs.Image(type="pil"),
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outputs=gr.outputs.Label(num_top_classes=3),
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examples=[["cheetah.jpg"]],
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)
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demo.launch() |