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Runtime error
| from pathlib import Path | |
| import numpy as np | |
| import torch | |
| import gradio as gr | |
| from torch import nn | |
| import gdown | |
| url = 'https://drive.google.com/uc?id=1dsk2JNZLRDjC-0J4wIQX_FcVurPaXaAZ' | |
| output = 'pytorch_model.bin' | |
| gdown.download(url, output, quiet=False) | |
| LABELS = Path('class_names.txt').read_text().splitlines() | |
| model = nn.Sequential( | |
| nn.Conv2d(1, 32, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(32, 64, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, 3, padding='same'), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2), | |
| nn.Flatten(), | |
| nn.Linear(1152, 256), | |
| nn.ReLU(), | |
| nn.Linear(256, len(LABELS)), | |
| ) | |
| state_dict = torch.load('pytorch_model.bin', map_location='cpu') | |
| model.load_state_dict(state_dict, strict=False) | |
| model.eval() | |
| def predict(im): | |
| if im is None: | |
| return None | |
| im = np.asarray(im.resize((28, 28))) | |
| x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255. | |
| with torch.no_grad(): | |
| out = model(x) | |
| probabilities = torch.nn.functional.softmax(out[0], dim=0) | |
| values, indices = torch.topk(probabilities, 5) | |
| return {LABELS[i]: v.item() for i, v in zip(indices, values)} | |
| interface = gr.Interface(predict, | |
| inputs=gr.Sketchpad(label="Draw Here", brush_radius=5, type="pil", shape=(120, 120)), | |
| outputs=gr.Label(label="Guess"), | |
| live=True) | |
| interface.queue().launch() | |