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.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, ", "")
  text.replace(r"_", " ")
  text.replace("(", "\\(").replace(")", "\\)")

  return text

demo = gr.Interface(
    fn=create_tags,
    inputs=[gr.Image(type="pil")],
    outputs=["text"],
)
demo.launch()