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import gradio as gr
from Models import VisionModel
import huggingface_hub
from PIL import Image
import torch.amp.autocast_mode
from pathlib import Path
import torch
import torchvision.transforms.functional as TVF
MODEL_REPO = "fancyfeast/joytag"
THRESHOLD = 0.4
DESCRIPTION = """
Demo for the JoyTag model: https://huggingface.co/fancyfeast/joytag
"""
def prepare_image(image: Image.Image, target_size: int) -> torch.Tensor:
# Pad image to square
image_shape = image.size
max_dim = max(image_shape)
pad_left = (max_dim - image_shape[0]) // 2
pad_top = (max_dim - image_shape[1]) // 2
padded_image = Image.new('RGB', (max_dim, max_dim), (255, 255, 255))
padded_image.paste(image, (pad_left, pad_top))
# Resize image
if max_dim != target_size:
padded_image = padded_image.resize((target_size, target_size), Image.BICUBIC)
# Convert to tensor
image_tensor = TVF.pil_to_tensor(padded_image) / 255.0
# Normalize
image_tensor = TVF.normalize(image_tensor, mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
return image_tensor
@torch.no_grad()
def predict(image: Image.Image):
image_tensor = prepare_image(image, model.image_size)
batch = {
'image': image_tensor.unsqueeze(0),
}
with torch.amp.autocast_mode.autocast('cpu', enabled=True):
preds = model(batch)
tag_preds = preds['tags'].sigmoid().cpu()
scores = {top_tags[i]: tag_preds[0][i] for i in range(len(top_tags))}
predicted_tags = [tag for tag, score in scores.items() if score > THRESHOLD]
tag_string = ', '.join(predicted_tags)
return tag_string, scores
print("Downloading model...")
path = huggingface_hub.snapshot_download(MODEL_REPO)
print("Loading model...")
model = VisionModel.load_model(path)
model.eval()
with open(Path(path) / 'top_tags.txt', 'r') as f:
top_tags = [line.strip() for line in f.readlines() if line.strip()]
print("Starting server...")
gradio_app = gr.Interface(
predict,
inputs=gr.Image(label="Source", sources=['upload', 'webcam'], type='pil'),
outputs=[
gr.Textbox(label="Tag String"),
gr.Label(label="Tag Predictions", num_top_classes=100),
],
title="JoyTag",
description=DESCRIPTION,
allow_flagging="never",
)
if __name__ == '__main__':
gradio_app.launch()
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