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import json
import time
from PIL import Image
import torch
from torchvision.transforms import transforms
import gradio as gr
from huggingface_hub import snapshot_download
snapshot_download(repo_id="Thouph/eva-vit-1b-224-8043", local_dir = "./")
model = torch.load('model.pth', map_location=torch.device('cpu'))
model.eval()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[
0.48145466,
0.4578275,
0.40821073
], std=[
0.26862954,
0.26130258,
0.27577711
])
])
with open("tags_8040.json", "r") as file:
tags = json.load(file)
allowed_tags = sorted(tags)
allowed_tags.insert(0, "placeholder0")
allowed_tags.append("placeholder1")
allowed_tags.append("explicit")
allowed_tags.append("questionable")
allowed_tags.append("safe")
def create_tags(image):
img = image.convert('RGB')
tensor = transform(img).unsqueeze(0)
with torch.no_grad():
out = model(tensor)
probabilities = torch.nn.functional.sigmoid(out[0])
indices = torch.where(probabilities > 0.3)[0]
values = probabilities[indices]
temp = []
for i in range(indices.size(0)):
temp.append([allowed_tags[indices[i]], values[i].item()])
text = ""
for i in range(len(temp)):
text += temp[i][0] + (', ' if i < len(temp) - 1 else '')
text.replace(r"placeholder1, ", "")
return text
demo = gr.Interface(
fn=create_tags,
inputs=[gr.Image(type="pil")],
outputs=["text"],
)
demo.launch() |