import ruamel_yaml as yaml
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 = '/home/notebook/code/personal/S9049611/BLIP/output/blip_tagtotext_14m/blip_tagtotext_encoderdiv_tar_random_swin/caption_coco_finetune_tagparse_tagfinetune_threshold075_bceloss_tagsingle_5e6_epoch19_negative_1_05_pos_1_10/checkpoint_05.pth'

config_file = 'configs/tag2text_caption.yaml'
config = yaml.load(open(config_file, 'r'), Loader=yaml.Loader)


model = tag2text_caption(pretrained=pretrained, image_size=image_size, vit=config['vit'], 
                    vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'],
                    prompt=config['prompt'],config=config,threshold = 0.75 )

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


def inference(raw_image, model_n, input_tag, strategy):
    if model_n == 'Image Captioning':
        raw_image = raw_image.resize((image_size, image_size))
        image = transform(raw_image).unsqueeze(0).to(device)   
        model.threshold = 0.7
        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():
            if strategy == "Beam search":
                

                caption, tag_predict = model.generate(image,tag_input = input_tag_list, 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, return_tag_predict = True)
                    tag_2 = tag_predict

            else:

                caption,tag_predict = model.generate(image,  tag_input = input_tag_list,sample=True, top_p=0.9, max_length=20, min_length=5, 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, return_tag_predict = True)
                    tag_2 = tag_predict
            return tag_1[0],tag_2[0],caption[0]
            

    else:   
        image_vq = transform_vq(raw_image).unsqueeze(0).to(device)  
        with torch.no_grad():
            answer = model_vq(image_vq, question, train=False, inference='generate') 
        return  'answer: '+answer[0]
    
inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning'], type="value", default="Image Captioning", label="Task"),gr.inputs.Textbox(lines=2, label="User Identified Tags (Optional, Enter with commas)"),gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type="value", default="Beam search", label="Caption Decoding Strategy")]

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

title = "Tag2Text"

description = "Gradio demo for Tag2Text: Guiding Language-Image Model via Image Tagging (Fudan University, OPPO Research Institute, International Digital Economy Academy)."

article = "<p style='text-align: center'><a href='' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | <a href='' target='_blank'>Github Repo</a></p>"

demo = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['images/COCO_val2014_000000551338.jpg',"Image Captioning","none","Beam search"], 
                                                                                                                ['images/COCO_val2014_000000551338.jpg',"Image Captioning","fence, sky","Beam search"],
                                                                                                                # ['images/COCO_val2014_000000551338.jpg',"Image Captioning","grass","Beam search"],
                                                                                                                 ['images/COCO_val2014_000000483108.jpg',"Image Captioning","none","Beam search"],
                                                                                                                 ['images/COCO_val2014_000000483108.jpg',"Image Captioning","electric cable","Beam search"],
                                                                                                                  # ['images/COCO_val2014_000000483108.jpg',"Image Captioning","sky, train","Beam search"],
                                                                                                                 ['images/COCO_val2014_000000483108.jpg',"Image Captioning","track, train","Beam search"] ,    
                                                                                                                 ['images/COCO_val2014_000000483108.jpg',"Image Captioning","grass","Beam search"]     
                                                                                                                ])