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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.
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)
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