app.py

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).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, k=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()