import numpy as np
import random

import torch
import torchvision.transforms as transforms

from PIL import Image
from models.tag2text import tag2text_caption

import gradio as gr

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

image_size = 384

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                std=[0.229, 0.224, 0.225])
transform = transforms.Compose([transforms.Resize((image_size, image_size)),transforms.ToTensor(),normalize])


#######Swin Version
pretrained = 'tag2text_swin_14m.pth'

model = tag2text_caption(pretrained=pretrained, image_size=image_size, vit='swin_b' )

model.eval()
model = model.to(device)


def inference(raw_image, input_tag):
    raw_image = raw_image.resize((image_size, image_size))

    image = transform(raw_image).unsqueeze(0).to(device)   
    model.threshold = 0.68
    if input_tag == '' or input_tag == 'none' or input_tag == 'None':
        input_tag_list = None
    else:
        input_tag_list = []
        input_tag_list.append(input_tag.replace(',',' | '))
    with torch.no_grad():


        caption, tag_predict = model.generate(image,tag_input = input_tag_list,max_length = 50, return_tag_predict = True)
        if input_tag_list == None:
            tag_1 = tag_predict
            tag_2 = ['none']
        else:
            _, tag_1 = model.generate(image,tag_input = None, max_length = 50, return_tag_predict = True)
            tag_2 = tag_predict

        return tag_1[0],tag_2[0],caption[0]


inputs = [gr.inputs.Image(type='pil'),gr.inputs.Textbox(lines=2, label="User Specified Tags (Optional, Enter with commas)")]

outputs = [gr.outputs.Textbox(label="Model Identified Tags"),gr.outputs.Textbox(label="User Specified Tags"), gr.outputs.Textbox(label="Image Caption") ]

title = "Tag2Text"
description = "Welcome to Tag2Text demo! (Supported by Fudan University, OPPO Research Institute, International Digital Economy Academy) <br/> Upload your image to get the <b>tags</b> and <b>caption</b> of the image. Optional: You can also input specified tags to get the corresponding caption."

article = "<p style='text-align: center'>Tag2text training on open-source datasets, and we are persisting in refining and iterating upon it.<br/><a href='https://arxiv.org/abs/2303.05657' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | <a href='https://github.com/xinyu1205/Tag2Text' target='_blank'>Github Repo</a></p>"

demo = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['images/COCO_val2014_000000483108.jpg',"none"],
                                                                                                                 ['images/COCO_val2014_000000483108.jpg',"power line"],
                                                                                                                 ['images/COCO_val2014_000000483108.jpg',"track, train"] ,   
                                                                                                                 ['images/bdf391a6f4b1840a.jpg',"none"],
                                                                                                                 ['images/64891_194270823.jpg',"none"],
                                                                                                                 ['images/2800737_834897251.jpg',"none"],
                                                                                                                 ['images/1641173_2291260800.jpg',"none"],
                                                                                                                ])

demo.launch(enable_queue=True)