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!pip3 install torch
import torch
from torch import nn
from pathlib import Path
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')
import gradio as gr
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',
theme="huggingface",
title="Sketch Recognition",
description="Sketch something for the model to guess in realtime",
article = "<p style='text-align: center'>Sketch Recognition | Demo Model</p>",
live=True)
interface.launch(share=True)
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