app.py

import os
import numpy as np
import datetime
import json
from typing import Optional
import transformers
from dataclasses import dataclass, field
import io
import spaces
import base64
from PIL import Image
import gradio as gr
import time
import hashlib

from utils import build_logger
from conversation import conv_seed_llama2

import hydra
import pyrootutils
import torch
import re
import time
from omegaconf import OmegaConf
from flask import Flask
import json
from typing import Optional
import cv2
from diffusers import AutoencoderKL, UNet2DConditionModel, EulerDiscreteScheduler, StableDiffusionImg2ImgPipeline

pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)

BOI_TOKEN = '<img>'
EOI_TOKEN = '</img>'
IMG_TOKEN = '<img_{:05d}>'

IMG_FLAG = '<image>'
num_img_in_tokens = 64
num_img_out_tokens = 64
instruction_prompt = '{instruction}'

resolution_grids = ['1x1', '1x2', '1x3', '1x4', '1x5', '1x6', '1x10', '2x1', '3x1', '4x1', '5x1', '6x1', '10x1', '2x2',
                    '2x3', '3x2', '2x4', '4x2']
base_resolution = 448

app = Flask(__name__)


def decode_image(encoded_image: str) -> Image:
    decoded_bytes = base64.b64decode(encoded_image.encode('utf-8'))
    buffer = io.BytesIO(decoded_bytes)
    image = Image.open(buffer)
    return image


def encode_image(image: Image.Image, format: str = 'PNG') -> str:
    with io.BytesIO() as buffer:
        image.save(buffer, format=format)
        encoded_image = base64.b64encode(buffer.getvalue()).decode('utf-8')
        return encoded_image


@dataclass
class Arguments:
    image_transform: Optional[str] = field(default='configs/processer/qwen_448_transform.yaml',
                                           metadata={"help": "config path of image transform"})
    tokenizer: Optional[str] = field(default='configs/tokenizer/clm_llama_tokenizer.yaml',
                                     metadata={"help": "config path of tokenizer used to initialize tokenizer"})
    llm: Optional[str] = field(default='configs/clm_models/llama2chat7b_lora.yaml', metadata={"help": "config path of llm"})
    visual_encoder: Optional[str] = field(default='configs/visual_tokenizer/qwen_vitg_448.yaml',
                                          metadata={"help": "config path of visual encoder"})
    sd_adapter: Optional[str] = field(
        default='configs/detokenizer/detokenizer_sdxl_qwen_vit_adapted.yaml',
        metadata={"help": "config path of sd adapter"})
    agent: Optional[str] = field(default='configs/clm_models/agent_7b_sft.yaml',
                                 metadata={"help": "config path of agent model"})
    diffusion_path: Optional[str] = field(default='stabilityai/stable-diffusion-xl-base-1.0',
                                          metadata={"help": "diffusion model path"})
    port: Optional[str] = field(default=80, metadata={"help": "network port"})
    llm_device: Optional[str] = field(default='cuda:0', metadata={"help": "llm device"})
    vit_sd_device: Optional[str] = field(default='cuda:0', metadata={"help": "sd and vit device"})
    dtype: Optional[str] = field(default='fp16', metadata={"help": "mix percision"})


parser = transformers.HfArgumentParser(Arguments)
args, = parser.parse_args_into_dataclasses()


class LLMService:

    def __init__(self, args) -> None:

        self.llm_device = args.llm_device
        self.vit_sd_device = args.vit_sd_device

        dtype = args.dtype
        if dtype == 'fp16':
            self.dtype = torch.float16
        elif dtype == 'bf16':
            self.dtype = torch.bfloat16
        else:
            raise ValueError

        image_transform_cfg = OmegaConf.load(args.image_transform)
        self.image_transform = hydra.utils.instantiate(image_transform_cfg)

        tokenizer_cfg = OmegaConf.load(args.tokenizer)
        self.tokenizer = hydra.utils.instantiate(tokenizer_cfg)

        visual_encoder_cfg = OmegaConf.load(args.visual_encoder)
        self.visual_encoder = hydra.utils.instantiate(visual_encoder_cfg)
        self.visual_encoder.eval().to(self.vit_sd_device, dtype=self.dtype)
        print('Init visual encoder done')

        llm_cfg = OmegaConf.load(args.llm)
        llm = hydra.utils.instantiate(llm_cfg, torch_dtype=self.dtype)
        print('Init llm done.')

        agent_cfg = OmegaConf.load(args.agent)
        self.agent = hydra.utils.instantiate(agent_cfg, llm=llm)

        self.agent.eval().to(self.llm_device, dtype=self.dtype)
        self.agent.llm.base_model.model.use_kv_cache_head = False
        print('Init agent mdoel Done')

        noise_scheduler = EulerDiscreteScheduler.from_pretrained(args.diffusion_path, subfolder="scheduler")

        vae = AutoencoderKL.from_pretrained(args.diffusion_path, subfolder="vae").to(self.vit_sd_device,
                                                                                     dtype=self.dtype)

        unet = UNet2DConditionModel.from_pretrained(args.diffusion_path, subfolder="unet").to(self.vit_sd_device,
                                                                                              dtype=self.dtype)

        sd_adapter_cfg = OmegaConf.load(args.sd_adapter)

        self.sd_adapter = hydra.utils.instantiate(sd_adapter_cfg, unet=unet).eval().to(self.vit_sd_device,
                                                                                       dtype=self.dtype)

        # self.sd_adapter.init_pipe(vae=vae,
        #                           scheduler=noise_scheduler,
        #                           visual_encoder=self.visual_encoder.cpu(),
        #                           image_transform=self.image_transform,
        #                           discrete_model=None,
        #                           dtype=self.dtype,
        #                           device="cpu")

        self.sd_adapter.init_pipe(vae=vae,
                                  scheduler=noise_scheduler,
                                  visual_encoder=self.visual_encoder,
                                  image_transform=self.image_transform,
                                  discrete_model=None,
                                  dtype=self.dtype,
                                  device=self.vit_sd_device)

        print('Init sd adapter pipe done.')

        self.visual_encoder.to(self.vit_sd_device, dtype=self.dtype)

        # model_id_or_path = "stablediffusionapi/realistic-vision-v51"
        # self.vae_pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, safety_checker=None,
        #                                                                torch_dtype=torch.float16)

        self.boi_token_id = self.tokenizer.encode(BOI_TOKEN, add_special_tokens=False)[0]
        self.eoi_token_id = self.tokenizer.encode(EOI_TOKEN, add_special_tokens=False)[0]


service = LLMService(args)


@spaces.GPU
def generate(text_list, image_list, image_embed_list, max_new_tokens):
    with torch.no_grad():
        print('text_list: {}'.format(text_list))
        text_list = text_list.split(IMG_FLAG)
        text_list = [text_list[0]] + ["[INST]"+item for item in text_list[1:-1]] + [text_list[-1]]
        top_p = 0.5
        window_size = 8
        assert len(text_list) == len(image_list) + 1

        image_tokens = BOI_TOKEN + ''.join(
            [IMG_TOKEN.format(int(item)) for item in range(num_img_in_tokens)]) + EOI_TOKEN

        input_images = []
        if len(image_list) > 0:
            image_tensor_list = []
            embeds_cmp_mask = []
            embeds_gen_mask = []

            for idx, image_item in enumerate(image_list):
                if isinstance(image_item, str):
                    image = decode_image(image_item)
                    print('after decode image size:', image.size)
                    input_images.append(image)

                    image_tensor = service.image_transform(image)
                    image_tensor_list.append(image_tensor)
                    embeds_cmp_mask.append(True)
                    embeds_gen_mask.append(False)
                else:
                    raise ValueError

            # pixel_values = torch.stack(image_tensor_list).to(service.vit_sd_device, dtype=service.dtype)
            # 
            # image_embeds = service.visual_encoder(pixel_values)
            # image_embeds = image_embeds.to(service.llm_device)
            print(image_embed_list)
            image_embed_list = [t.squeeze(0) for t in image_embed_list]
            image_embeds = torch.stack(image_embed_list, dim=0)
            image_embeds = image_embeds.to(service.llm_device)

            embeds_cmp_mask = torch.tensor(embeds_cmp_mask, dtype=torch.bool).to(service.llm_device)
            embeds_gen_mask = torch.tensor(embeds_gen_mask, dtype=torch.bool).to(service.llm_device)

        else:
            image_embeds = None
            patch_position = 0
            embeds_cmp_mask = None
            embeds_gen_mask = None


        input_text = image_tokens.join(text_list)

        print('input_text fed to LLM:', input_text)
        input_ids = service.tokenizer.encode(input_text, add_special_tokens=False)

        while image_embeds.shape[0] > window_size:
            eoi_prompt_idx = input_text.index(EOI_TOKEN)
            input_text = input_text[eoi_prompt_idx + len(EOI_TOKEN) + len('[INST]'):]
            image_embeds = image_embeds[1:]
            input_ids = service.tokenizer.encode(input_text, add_special_tokens=False)

        if image_embeds is not None:
            embeds_cmp_mask = torch.tensor([True] * image_embeds.shape[0]).to(service.llm_device, dtype=torch.bool)

        input_ids = [service.tokenizer.bos_token_id] + input_ids

        input_ids = torch.tensor(input_ids).to(service.llm_device, dtype=torch.long)
        ids_cmp_mask = torch.zeros_like(input_ids, dtype=torch.bool).to(service.llm_device)
        ids_gen_mask = torch.zeros_like(input_ids, dtype=torch.bool).to(service.llm_device)

        boi_indices = torch.where(input_ids == service.boi_token_id)[0].tolist()
        eoi_indices = torch.where(input_ids == service.eoi_token_id)[0].tolist()

        for boi_idx, eoi_idx in zip(boi_indices, eoi_indices):
            ids_cmp_mask[boi_idx + 1:eoi_idx] = True

        input_ids = input_ids.unsqueeze(0)
        ids_cmp_mask = ids_cmp_mask.unsqueeze(0)
        ids_gen_mask = ids_gen_mask.unsqueeze(0)

        error_msg = []
        print('image_embeds_shape: ' + str(image_embeds.shape))
        print('image_embeds: {}'.format(image_embeds))
        print('input_ids: ' + str(input_ids))
        print('ids_cmp_mask: ' + str(ids_cmp_mask))
        output = service.agent.generate(
            tokenizer=service.tokenizer,
            input_ids=input_ids,
            image_embeds=image_embeds,
            embeds_cmp_mask=embeds_cmp_mask,
            ids_cmp_mask=ids_cmp_mask,
            num_img_gen_tokens=num_img_out_tokens,
            max_new_tokens=max_new_tokens,
            dtype=service.dtype,
            device=service.llm_device,
            top_p=top_p,
        )

        gen_imgs_base64_list = []
        generated_text = output['text']

        torch.cuda.empty_cache()

        if output['has_img_output']:
            # print('loading visual encoder and llm to CPU, and sd to GPU')
            # a = time.time()
            # service.agent = service.agent.cpu()
            # service.sd_adapter = service.sd_adapter.to(service.vit_sd_device, dtype=service.dtype)
            # print("Loading finished: ", time.time() - a)

            img_gen_feat = output['img_gen_feat'].to(service.vit_sd_device, dtype=service.dtype)

            for img_idx in range(output['num_gen_imgs']):
                img_feat = img_gen_feat[img_idx:img_idx + 1]
                generated_image = service.sd_adapter.generate(image_embeds=img_feat, num_inference_steps=50)[0]
                gen_imgs_base64_list.append(generated_image)

            # a = time.time()
            # service.sd_adapter = service.sd_adapter.cpu()
            # service.visual_encoder = service.visual_encoder.to(service.vit_sd_device, dtype=service.dtype)
            # service.agent = service.agent.to(service.vit_sd_device, dtype=service.dtype)
            # print("Loading finished: ", time.time() - a)

        print('[func generate inout+output]: {}'.format(input_text + generated_text))
        return {'text': generated_text, 'images': gen_imgs_base64_list, 'image_embeds': img_feat.detach().clone(), 'error_msg': error_msg}


def http_bot(dialog_state, input_state, max_new_tokens, max_length,
             request: gr.Request):
    print('input_state:', input_state)
    print(dialog_state.messages)
    if len(dialog_state.messages) == 0 or len(
            dialog_state.messages[-1]['message']['text'].strip(' ?.;!/')) == 0:
        return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (no_change_btn,) * 4

    if len(dialog_state.messages) >= max_length:
        output_state = init_input_state()
        output_state['text'] = 'Error: History exceeds maximum rounds, please clear history and restart.'
        dialog_state.messages.append({'role': dialog_state.roles[1], 'message': output_state})
        input_state = init_input_state()
        return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (disable_btn,) * 3 + (enable_btn,)

    prompt = dialog_state.get_prompt()
    text = prompt['text']
    print('text from http_bot: {}'.format(text))
    max_new_tokens = int(max_new_tokens)
    images = prompt['images']
    image_embeds = prompt['image_embeds']

    results = generate(text, images, image_embeds, max_new_tokens)
    generated_text = results['text']
    pattern = r' <img_000\d{2}>'
    # Replace all occurrences of the pattern with the replacement text
    generated_text = re.sub(pattern, '', generated_text)

    generated_text = generated_text.replace(' '+service.tokenizer.eos_token, '')\
        .replace('[INST]', '').replace(' '+BOI_TOKEN, '').replace(' '+EOI_TOKEN, IMG_FLAG)

    results['text'] = generated_text

    print('response: ', {'text': results['text'], 'error_msg': results['error_msg']})

    output_state = init_input_state()
    image_dir = get_conv_image_dir()
    output_state['text'] = results['text']
    output_state['image_embeds'].append(results['image_embeds'])

    for image_base64 in results['images']:
        if image_base64 == '':
            image_path = ''
        else:
            if isinstance(image_base64, Image.Image):
                print('generated image is in Image.Image')
                image = image_base64
            else:
                print('generated image is in Image_base64')
                image = decode_image(image_base64)
                image = image.convert('RGB')
            image_path = get_image_name(image=image, image_dir=image_dir)
            if not os.path.exists(image_path):
                image.save(image_path)
        output_state['images'].append(image_path)

    dialog_state.messages.append({'role': dialog_state.roles[1], 'message': output_state})

    vote_last_response(dialog_state, 'common', request)
    input_state = init_input_state()
    chatbot = update_error_msg(dialog_state.to_gradio_chatbot(), results['error_msg'])
    return (dialog_state, input_state, chatbot) + (enable_btn,) * 4


IMG_FLAG = '<image>'
LOGDIR = 'log'

logger = build_logger("gradio_seed_story", LOGDIR)
headers = {"User-Agent": "SEED-Story Client"}

no_change_btn = gr.Button()
enable_btn = gr.Button(interactive=True)
disable_btn = gr.Button(interactive=False)

conv_seed_llama = conv_seed_llama2


def get_conv_log_filename():
    t = datetime.datetime.now()
    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
    return name


def get_conv_image_dir():
    name = os.path.join(LOGDIR, 'images')
    os.makedirs(name, exist_ok=True)
    return name


def get_image_name(image, image_dir=None):
    buffer = io.BytesIO()
    image.save(buffer, format='PNG')
    image_bytes = buffer.getvalue()
    md5 = hashlib.md5(image_bytes).hexdigest()

    if image_dir is not None:
        image_name = os.path.join(image_dir, md5 + '.png')
    else:
        image_name = md5 + '.png'

    return image_name


def resize_image_square(image, target_size=448):
    resized_image = image.resize((target_size, target_size))
    return resized_image


def resize_image(image, max_size=512):
    width, height = image.size
    aspect_ratio = float(width) / float(height)

    if width > height:
        new_width = max_size
        new_height = int(new_width / aspect_ratio)
    else:
        new_height = max_size
        new_width = int(new_height * aspect_ratio)

    resized_image = image.resize((new_width, new_height))
    return resized_image


def center_crop_image(image, max_aspect_ratio=1.5):
    width, height = image.size
    aspect_ratio = max(width, height) / min(width, height)

    if aspect_ratio >= max_aspect_ratio:
        if width > height:
            new_width = int(height * max_aspect_ratio)
            left = (width - new_width) // 2
            right = (width + new_width) // 2
            top = 0
            bottom = height
        else:
            new_height = int(width * max_aspect_ratio)
            left = 0
            right = width
            top = (height - new_height) // 2
            bottom = (height + new_height) // 2

        cropped_image = image.crop((left, top, right, bottom))
        return cropped_image
    else:
        return image


def vote_last_response(state, vote_type, request: gr.Request):
    with open(get_conv_log_filename(), "a") as fout:
        print(state)
        print(state.dict())
        dic = state.dict()
        for i in range(len(dic['messages'])):
            dic['messages'][i]['message'].pop('image_embeds')
        print(dic)
        data = {
            "tstamp": round(time.time(), 4),
            "type": vote_type,
            "state": dic,
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


def upvote_last_response(state, request: gr.Request):
    logger.info(f"upvote. ip: {request.client.host}")
    vote_last_response(state, "upvote", request)
    return (disable_btn,) * 2


def downvote_last_response(state, request: gr.Request):
    logger.info(f"downvote. ip: {request.client.host}")
    vote_last_response(state, "downvote", request)
    return (disable_btn,) * 2


def regenerate(dialog_state, request: gr.Request):
    logger.info(f"regenerate. ip: {request.client.host}")
    if dialog_state.messages[-1]['role'] == dialog_state.roles[1]:
        dialog_state.messages.pop()
    return (
               dialog_state,
               dialog_state.to_gradio_chatbot(),
           ) + (disable_btn,) * 4


def clear_history(request: gr.Request):
    logger.info(f"clear_history. ip: {request.client.host}")
    dialog_state = conv_seed_llama.copy()
    input_state = init_input_state()
    return (dialog_state, input_state, dialog_state.to_gradio_chatbot()) + (disable_btn,) * 4


def init_input_state():
    return {'images': [], 'text': '', 'image_embeds': []}


def add_text(dialog_state, input_state, text, request: gr.Request):
    logger.info(f"add_text. ip: {request.client.host}.")
    if text is None or len(text) == 0:
        return (dialog_state, input_state, "", dialog_state.to_gradio_chatbot()) + (no_change_btn,) * 4
    input_state['text'] += text

    if len(dialog_state.messages) > 0 and dialog_state.messages[-1]['role'] == dialog_state.roles[0]:
        dialog_state.messages[-1]['message'] = input_state
    else:
        dialog_state.messages.append({'role': dialog_state.roles[0], 'message': input_state})
    print('add_text: ', dialog_state.to_gradio_chatbot())

    return (dialog_state, input_state, "", dialog_state.to_gradio_chatbot()) + (disable_btn,) * 4


def is_blank(image):
    image_array = np.array(image)
    unique_colors = np.unique(image_array)
    print('unique_colors', len(unique_colors))
    return len(unique_colors) == 1


def add_image(dialog_state, input_state, image, request: gr.Request):
    logger.info(f"add_image. ip: {request.client.host}.")
    if image is None:
        return (dialog_state, input_state, None, dialog_state.to_gradio_chatbot()) + (no_change_btn,) * 4

    image = image.convert('RGB')

    print('image size:', image.size)

    # image = center_crop_image(image, max_aspect_ratio=10)

    image_dir = get_conv_image_dir()
    image_path = get_image_name(image=image, image_dir=image_dir)
    if not os.path.exists(image_path):
        image.save(image_path)
    input_state['images'].append(image_path)
    image_tensor = service.image_transform(image).unsqueeze(0).to(service.llm_device, dtype=service.dtype)
    image_embeds = service.visual_encoder(image_tensor).detach().clone()
    image_embeds = image_embeds.to(service.llm_device)
    input_state['image_embeds'].append(image_embeds)
    input_state['text'] += IMG_FLAG

    if len(dialog_state.messages) > 0 and dialog_state.messages[-1]['role'] == dialog_state.roles[0]:
        dialog_state.messages[-1]['message'] = input_state
    else:
        dialog_state.messages.append({'role': dialog_state.roles[0], 'message': input_state})

    print('add_image:', dialog_state)

    return (dialog_state, input_state, None, dialog_state.to_gradio_chatbot()) + (disable_btn,) * 4


def update_error_msg(chatbot, error_msg):
    if len(error_msg) > 0:
        info = '\n-------------\nSome errors occurred during response, please clear history and restart.\n' + '\n'.join(
            error_msg)
        chatbot[-1][-1] = chatbot[-1][-1] + info

    return chatbot


def load_demo(request: gr.Request):
    logger.info(f"load_demo. ip: {request.client.host}")
    dialog_state = conv_seed_llama.copy()
    input_state = init_input_state()
    return dialog_state, input_state


title = ("""
# SEED-Story
[[Paper]](https://arxiv.org/abs/2407.08683) [[Code]](https://github.com/TencentARC/SEED-Story)

Demo of the multimodal story generation model SEED-Story-George. It is trained on StoryStream-Curious George subset.
SEED-Story is a MLLM capable of generating multimodal long stories consisting of rich and coherent narrative texts, along with images that are consistent in characters and style.

## Tips:
* Check out the conversation examples (at the bottom) for inspiration.
* Our demo requires a mix of an image and a starting sentence as input. You can freely upload an image or enter text, and then click on "Submit". Then, The model generates the next story image and text.
* You can click on "Continue Generation" to make the model generate a next story image and text based on all previous story boards.
* SEED-Story was trained with English-only data. It may process with other languages due to the inherent capabilities from LLaMA, but might not stable.
""")

css = """
img {
  font-family: 'Helvetica';
  font-weight: 300;
  line-height: 2;  
  text-align: center;

  width: auto;
  height: auto;
  display: block;
  position: relative;
}
img:before { 
  content: " ";
  display: block;
  position: absolute;
  top: -10px;
  left: 0;
  height: auto;
  width: 100%;
  background-color: rgb(230, 230, 230);
  border: 2px dotted rgb(200, 200, 200);
  border-radius: 5px;
}
img:after { 
  content: " ";
  display: block;
  font-size: 16px;
  font-style: normal;
  font-family: FontAwesome;
  color: rgb(100, 100, 100);

  position: absolute;
  top: 5px;
  left: 0;
  width: 100%;
  text-align: center;
}
"""

if __name__ == '__main__':
    examples_mix = [
        ['https://github.com/TencentARC/SEED-Story/blob/master/assets/demo_examples/2.jpg?raw=true',
         'One day, George, the curious brown monkey, decided to explore a new room. He peeked out from behind a dresser, looking both curious and cautious. The dresser had three drawers, each with a round handle. An electrical outlet was visible on the wall.'],
        ['https://github.com/TencentARC/SEED-Story/blob/master/assets/demo_examples/4.jpg?raw=true',
         'In the bustling city, a beautiful blue and yellow bird took flight, soaring high above the buildings. Among the clouds, a heart-shaped formation appeared, as if nature was sending a love note to the world below. Other birds joined, their silhouettes dancing in the distance.'],
    ]
    with gr.Blocks(css=css) as demo:
        gr.Markdown(title)
        dialog_state = gr.State()
        input_state = gr.State()
        with gr.Row():
            with gr.Column(scale=3):
                with gr.Row():
                    image = gr.Image(type='pil', label='input_image')
                with gr.Row():
                    text = gr.Textbox(lines=5,
                                      show_label=False,
                                      label='input_text',
                                      elem_id='textbox',
                                      placeholder="Enter text and image, and press submit,", container=False)
                with gr.Row():
                    # add_image_btn = gr.Button("Add Image")
                    # add_text_btn = gr.Button("Add Text")
                    submit_btn = gr.Button("Submit")
                    continue_btn = gr.Button("Continue Generation")

                with gr.Row():
                    max_new_tokens = gr.Slider(minimum=64,
                                               maximum=1024,
                                               value=768,
                                               step=64,
                                               interactive=True,
                                               label="Max Output Tokens")
                    max_length = gr.Slider(minimum=1, maximum=30, value=10, step=1, interactive=True,
                                          label="Max Story Length")

            with gr.Column(scale=7):
                chatbot = gr.Chatbot(elem_id='chatbot', label="SEED-Story", height=700)
                with gr.Row():
                    upvote_btn = gr.Button(value="👍  Upvote", interactive=False)
                    downvote_btn = gr.Button(value="👎  Downvote", interactive=False)
                    regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
                    clear_btn = gr.Button(value="🗑️  Clear history", interactive=False)

        with gr.Row():
            with gr.Column(scale=1.0):
                gr.Examples(examples=examples_mix, label='Input examples', inputs=[image, text], cache_examples=False)

        # Register listeners
        btn_list = [upvote_btn, downvote_btn, regenerate_btn, clear_btn]
        upvote_btn.click(upvote_last_response, [dialog_state], [upvote_btn, downvote_btn])
        downvote_btn.click(downvote_last_response, [dialog_state], [upvote_btn, downvote_btn])

        regenerate_btn.click(regenerate, [dialog_state], [dialog_state, chatbot] + btn_list).then(
            http_bot, [dialog_state, input_state, max_new_tokens, max_length],
            [dialog_state, input_state, chatbot] + btn_list)
        # add_image_btn.click(add_image, [dialog_state, input_state, image],
        #                     [dialog_state, input_state, image, chatbot] + btn_list)
        #
        # add_text_btn.click(add_text, [dialog_state, input_state, text],
        #                    [dialog_state, input_state, text, chatbot] + btn_list)

        submit_btn.click(
            add_text, [dialog_state, input_state, text],
            [dialog_state, input_state, text, chatbot, upvote_btn, downvote_btn, regenerate_btn, clear_btn]).then(
            add_image, [dialog_state, input_state, image],
            [dialog_state, input_state, image, chatbot] + btn_list).then(
            http_bot,
            [dialog_state, input_state, max_new_tokens, max_length],
            [dialog_state, input_state, chatbot] + btn_list)
        continue_btn.click(
            http_bot,
            [dialog_state, input_state, max_new_tokens, max_length],
            [dialog_state, input_state, chatbot] + btn_list)
        clear_btn.click(clear_history, None, [dialog_state, input_state, chatbot] + btn_list)

        demo.load(load_demo, None, [dialog_state, input_state])

    demo.launch(debug=True)