app.py

import json
import random
random.seed(999)
import torch
from torchvision.transforms import transforms
import gradio as gr
from datetime import datetime

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

with open("tags_9940.json", "r") as file:
    allowed_tags = json.load(file)

allowed_tags = sorted(allowed_tags)
allowed_tags.append("explicit")
allowed_tags.append("questionable")
allowed_tags.append("safe")

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 = sorted(temp, key=lambda x: x[1], reverse=True)
    # print("Before adding implicated tags, there are " + str(len(temp)) + " tags")
    temp = [t[0] for t in temp]
    text_no_impl = " ".join(temp)
    current_datetime = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    print(f"{current_datetime}: finished.")
    return text_no_impl, tag_score

demo = 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",
)

demo.launch()