| from pathlib import Path | |
| import torch | |
| import gradio as gr | |
| from torch import nn | |
| 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): | |
| 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='sketchpad', outputs='label', live=True) | |
| interface.launch(debug=True) | |