app.py

import json
import os
import zipfile
from pathlib import Path
import io
from tempfile import NamedTemporaryFile

from PIL import Image
import gradio as gr
import torch
from torchvision.transforms import transforms
from torch.utils.data import Dataset, DataLoader
import spaces

torch.jit.script = lambda f: f
# torch.cuda.amp.autocast(enabled=True)

caption_ext = ".txt"
exclude_tags = ("explicit", "questionable", "safe")

transform = transforms.Compose([
    transforms.Resize((384, 384)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

class ZipImageDataset(Dataset):
    def __init__(self, zip_file, dtype):
        self.zip_file = zip_file
        self.dtype = dtype
        self.image_files = [file_info for file_info in zip_file.infolist() if file_info.filename.lower().endswith(('.png', '.jpg', '.jpeg', '.webp'))]

    def __len__(self):
        return len(self.image_files)

    def __getitem__(self, index):
        file_info = self.image_files[index]
        with self.zip_file.open(file_info) as file:
            image = Image.open(file).convert("RGB")
        image = transform(image).to(self.dtype)
        return {
            "image": image,
            "image_name": file_info.filename,
        }

model = torch.load("./model.pth", map_location=torch.device('cpu'))
model.eval()

with open("tags_9940.json", "r") as file:
    tags = json.load(file)
allowed_tags = sorted(tags) + ["explicit", "questionable", "safe"]

@spaces.GPU(duration=5)
def create_tags(image, threshold):
    img = image.convert('RGB')
    tensor = transform(img).unsqueeze(0)

    with torch.no_grad():
        logits = model(tensor)
        probabilities = torch.nn.functional.sigmoid(logits[0])
        indices = torch.where(probabilities > threshold)[0]
        values = probabilities[indices]

    temp = []
    tag_score = dict()
    for i in range(indices.size(0)):
        temp.append([allowed_tags[indices[i]], values[i].item()])
        tag_score[allowed_tags[indices[i]]] = values[i].item()
    temp = [t[0] for t in temp]
    text_no_impl = ", ".join(temp)
    return text_no_impl, tag_score

@spaces.GPU(duration=180)
def process_zip(zip_file, threshold):
    with zipfile.ZipFile(zip_file.name) as zip_ref:
        dataset = ZipImageDataset(zip_ref, next(model.parameters()).dtype)
        dataloader = DataLoader(
            dataset,
            batch_size=64,
            shuffle=False,
            num_workers=0,
            pin_memory=True,
            drop_last=False,
        )
        all_image_names = []
        all_probabilities = []
        with torch.no_grad():
            for i, batch in enumerate(dataloader):
                images = batch["image"]
                with torch.autocast(device_type="cuda", dtype=torch.float16):
                    outputs = model(images)
                    probabilities = torch.nn.functional.sigmoid(outputs)
                    for image_name, prob in zip(batch["image_name"], probabilities):
                        indices = torch.where(prob > threshold)[0]
                        values = prob[indices]
                        temp = []
                        tag_score = dict()
                        for j in range(indices.size(0)):
                            temp.append([allowed_tags[indices[j]], values[j].item()])
                            tag_score[allowed_tags[indices[j]]] = values[j].item()
                        temp = [t[0] for t in temp]
                        text_no_impl = ", ".join(temp)
                        all_image_names.append(image_name)
                        all_probabilities.append(text_no_impl)

    temp_file = NamedTemporaryFile(delete=False, suffix=".zip")
    with zipfile.ZipFile(temp_file, "w") as zip_ref:
        for image_name, text_no_impl in zip(all_image_names, all_probabilities):
            with zip_ref.open(image_name + caption_ext, "w") as file:
                file.write(text_no_impl.encode())
    temp_file.seek(0)
    return temp_file.name

with gr.Blocks() as demo:
    with gr.Tab("Single Image"):
        gr.Interface(
            create_tags,
            inputs=[gr.Image(label="Source", sources=['upload', 'webcam'], type='pil'), gr.Slider(minimum=0.00, maximum=1.00, step=0.01, value=0.30, label="Threshold")],
            outputs=[
                gr.Textbox(label="Tag String"),
                gr.Label(label="Tag Predictions", num_top_classes=200),
            ],
            allow_flagging="never",
        )
    with gr.Tab("Multiple Images"):
        gr.Interface(fn=process_zip, inputs=[gr.File(label="Zip File", file_types=[".zip"]), gr.Slider(minimum=0, maximum=1, value=0.3, step=0.01, label="Threshold")],
                      outputs=gr.File(type="binary"))

if __name__ == "__main__":
    demo.launch()