import numpy as np
import random

import torch
import torchvision.transforms as transforms

from PIL import Image
from models.tag2text import tag2text_caption, ram

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])

#######Tag2Text Model
pretrained = 'tag2text_swin_14m.pth'

model_tag2text = tag2text_caption(pretrained=pretrained, image_size=image_size, vit='swin_b' )

model_tag2text.eval()
model_tag2text = model_tag2text.to(device)


#######RAM Model
pretrained = 'ram_swin_large_14m.pth'

model_ram = ram(pretrained=pretrained, image_size=image_size, vit='swin_l' )

model_ram.eval()
model_ram = model_ram.to(device)


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

    image = transform(raw_image).unsqueeze(0).to(device) 
    if model_n == 'Recognize Anything Model':  
        model = model_ram
        tags, tags_chinese = model.generate_tag(image)
        return tags[0],tags_chinese[0], 'none'
    else:
        model = model_tag2text
        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],'none',caption[0]


inputs = [
    gr.inputs.Image(type='pil'), 
    gr.inputs.Radio(choices=['Recognize Anything Model',"Tag2Text Model"],
    type="value",
    default="Recognize Anything Model",
    label="Model"          ),          
    gr.inputs.Textbox(lines=2, label="User Specified Tags (Optional, Enter with commas, Currently only Tag2Text is supported)")   
                ]

outputs = [gr.outputs.Textbox(label="Tags"),gr.outputs.Textbox(label="标签"), gr.outputs.Textbox(label="Caption (currently only Tag2Text is supported)")]

# title = "Recognize Anything Model"
title = "<font size='10'> Recognize Anything Model</font>"

description = "Welcome to the Recognize Anything Model (RAM) and Tag2Text Model demo! <li><b>Recognize Anything Model:</b>  Upload your image to get the <b>English and Chinese outputs of the image tags</b>!</li><li><b>Tag2Text Model:</b> 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.</li> "


article = "<p style='text-align: center'>RAM and Tag2Text is training on open-source datasets, and we are persisting in refining and iterating upon it.<br/><a href='https://recognize-anything.github.io/' target='_blank'>Recognize Anything: A Strong Image Tagging Model</a> | <a href='https://https://tag2text.github.io/' 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/demo1.jpg',"Recognize Anything Model","none"],
                                                                                                                ['images/demo2.jpg',"Recognize Anything Model","none"],
                                                                                                                 ['images/demo4.jpg',"Recognize Anything Model","none"],
                                                                                                                 ['images/demo4.jpg',"Tag2Text Model","power line"],
                                                                                                                 ['images/demo4.jpg',"Tag2Text Model","track, train"] ,   
                                                                                                                ])

demo.launch(enable_queue=True)