app.py

!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)