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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): | |
if isinstance(im, dict): # For sketchpad input | |
im = im['composite'] | |
# Convert to grayscale and resize to 28x28 | |
import cv2 | |
im_gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY) | |
im_resized = cv2.resize(im_gray, (28, 28)) | |
# Convert to tensor and normalize | |
x = torch.tensor(im_resized, 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) |