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| 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) / 255.0 | |
| 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", | |
| theme="huggingface", | |
| title="Sketch Recognition", | |
| description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!", | |
| article="<p style='text-align: center'>Sketch Recognition | Demo Model</p>", | |
| live=True, | |
| ) | |
| interface.launch(share=True) |