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https://github.com/gradio-app/gradio.git
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52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
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from pathlib import Path
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import torch
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import gradio as gr
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from torch import nn
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import gdown
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url = 'https://drive.google.com/uc?id=1dsk2JNZLRDjC-0J4wIQX_FcVurPaXaAZ'
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output = 'pytorch_model.bin'
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gdown.download(url, output, quiet=False)
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LABELS = Path('class_names.txt').read_text().splitlines()
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model = nn.Sequential(
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nn.Conv2d(1, 32, 3, padding='same'),
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nn.ReLU(),
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nn.MaxPool2d(2),
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nn.Conv2d(32, 64, 3, padding='same'),
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nn.ReLU(),
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nn.MaxPool2d(2),
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nn.Conv2d(64, 128, 3, padding='same'),
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nn.ReLU(),
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nn.MaxPool2d(2),
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nn.Flatten(),
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nn.Linear(1152, 256),
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nn.ReLU(),
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nn.Linear(256, len(LABELS)),
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)
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state_dict = torch.load('pytorch_model.bin', map_location='cpu')
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model.load_state_dict(state_dict, strict=False)
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model.eval()
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def predict(input):
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im = input
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if im is None:
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return None
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x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
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with torch.no_grad():
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out = model(x)
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probabilities = torch.nn.functional.softmax(out[0], dim=0)
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values, indices = torch.topk(probabilities, 5)
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return {LABELS[i]: v.item() for i, v in zip(indices, values)}
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interface = gr.Interface(predict, inputs=gr.templates.Sketchpad(label="Draw Here"), outputs=gr.Label(label="Guess"), theme="default", css=".footer{display:none !important}", live=True)
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interface.launch(enable_queue=False)
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