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MiniGPT-4/demo_v2.py

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init version of v2
2023-10-12 21:14:34 +00:00
import argparse
import os
import random
import requests
from io import BytesIO
from threading import Thread
from collections import defaultdict
import cv2
from termcolor import colored
from textwrap import wrap
from torchvision.transforms import functional as F
import re
import numpy as np
from PIL import Image
import torch
import torch.backends.cudnn as cudnn
import html
import gradio as gr
from transformers import TextIteratorStreamer
import minigpt4.tasks as tasks
from minigpt4.common.config import Config
from minigpt4.common.dist_utils import get_rank, init_distributed_mode
from minigpt4.common.logger import setup_logger
from minigpt4.common.optims import (
LinearWarmupCosineLRScheduler,
LinearWarmupStepLRScheduler,
)
from minigpt4.common.registry import registry
from minigpt4.common.utils import now
from minigpt4.conversation.conversation import Conversation, SeparatorStyle, StoppingCriteriaList, StoppingCriteriaSub
# imports modules for registration
from minigpt4.datasets.builders import *
from minigpt4.models import *
from minigpt4.processors import *
from minigpt4.runners import *
from minigpt4.tasks import *
parser = argparse.ArgumentParser(description="Demo")
parser.add_argument("--cfg-path", required=True, help="path to configuration file.")
parser.add_argument(
"--options",
nargs="+",
help="override some settings in the used config, the key-value pair "
"in xxx=yyy format will be merged into config file (deprecate), "
"change to --cfg-options instead.",
)
import torch.backends.cudnn as cudnn
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
cudnn.benchmark = False
cudnn.deterministic = True
print('Initializing Chat')
cfg = Config(parser.parse_args(['--cfg-path', 'eval_configs/minigpt4_object_detection_448x448_llama2.yaml']))
cfg.model_cfg.ckpt = "/home/zhud/c2090/minigpt4_ckpt/448_conversation_correct_best_v7_ablation1_v5_v6/20231007035/checkpoint_35.pth"
cfg.model_cfg.lora_r = 64
cfg.model_cfg.lora_alpha = 16
device = 'cuda'
model_config = cfg.model_cfg
model_cls = registry.get_model_class(model_config.arch)
model = model_cls.from_config(model_config).to(device)
bounding_box_size = 100
vis_processor_cfg = cfg.datasets_cfg.coco.vis_processor.train
vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
model = model.eval()
CONV_VISION = Conversation(
system="",
roles=(r"<s>[INST] ", r" [/INST]"),
messages=[],
offset=2,
sep_style=SeparatorStyle.SINGLE,
sep="</s>",
)
def extract_substrings(string):
# first check if there is no-finished bracket
index = string.rfind('}')
if index != -1:
string = string[:index + 1]
pattern = r'<p>(.*?)\}(?!<)'
matches = re.findall(pattern, string)
substrings = [match for match in matches]
return substrings
def is_overlapping(rect1, rect2):
x1, y1, x2, y2 = rect1
x3, y3, x4, y4 = rect2
return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)
def computeIoU(bbox1, bbox2):
x1, y1, x2, y2 = bbox1
x3, y3, x4, y4 = bbox2
intersection_x1 = max(x1, x3)
intersection_y1 = max(y1, y3)
intersection_x2 = min(x2, x4)
intersection_y2 = min(y2, y4)
intersection_area = max(0, intersection_x2 - intersection_x1 + 1) * max(0, intersection_y2 - intersection_y1 + 1)
bbox1_area = (x2 - x1 + 1) * (y2 - y1 + 1)
bbox2_area = (x4 - x3 + 1) * (y4 - y3 + 1)
union_area = bbox1_area + bbox2_area - intersection_area
iou = intersection_area / union_area
return iou
def save_tmp_img(visual_img):
file_name = "".join([str(random.randint(0, 9)) for _ in range(5)]) + ".jpg"
file_path = "/tmp/" + file_name
visual_img.save(file_path)
return file_path
def mask2bbox(mask):
if mask is None:
return ''
mask = mask.resize([100, 100], resample=Image.NEAREST)
mask = np.array(mask)[:, :, 0]
rows = np.any(mask, axis=1)
cols = np.any(mask, axis=0)
if rows.sum():
# Get the top, bottom, left, and right boundaries
rmin, rmax = np.where(rows)[0][[0, -1]]
cmin, cmax = np.where(cols)[0][[0, -1]]
bbox = '{{<{}><{}><{}><{}>}}'.format(cmin, rmin, cmax, rmax)
else:
bbox = ''
return bbox
def escape_markdown(text):
# List of Markdown special characters that need to be escaped
md_chars = ['<', '>']
# Escape each special character
for char in md_chars:
text = text.replace(char, '\\' + char)
return text
def reverse_escape(text):
md_chars = ['\\<', '\\>']
for char in md_chars:
text = text.replace(char, char[1:])
return text
colors = [
(255, 0, 0),
(0, 255, 0),
(0, 0, 255),
(210, 210, 0),
(255, 0, 255),
(0, 255, 255),
(114, 128, 250),
(0, 165, 255),
(0, 128, 0),
(144, 238, 144),
(238, 238, 175),
(255, 191, 0),
(0, 128, 0),
(226, 43, 138),
(255, 0, 255),
(0, 215, 255),
]
color_map = {
f"{color_id}": f"#{hex(color[2])[2:].zfill(2)}{hex(color[1])[2:].zfill(2)}{hex(color[0])[2:].zfill(2)}" for
color_id, color in enumerate(colors)
}
used_colors = colors
def visualize_all_bbox_together(image, generation):
if image is None:
return None, ''
generation = html.unescape(generation)
print('gen begin', generation)
image_width, image_height = image.size
image = image.resize([500, int(500 / image_width * image_height)])
image_width, image_height = image.size
string_list = extract_substrings(generation)
if string_list: # it is grounding or detection
mode = 'all'
entities = defaultdict(list)
i = 0
j = 0
for string in string_list:
try:
obj, string = string.split('</p>')
except ValueError:
print('wrong string: ', string)
continue
bbox_list = string.split('<delim>')
flag = False
for bbox_string in bbox_list:
integers = re.findall(r'-?\d+', bbox_string)
if len(integers) == 4:
x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
left = x0 / bounding_box_size * image_width
bottom = y0 / bounding_box_size * image_height
right = x1 / bounding_box_size * image_width
top = y1 / bounding_box_size * image_height
entities[obj].append([left, bottom, right, top])
j += 1
flag = True
if flag:
i += 1
else:
integers = re.findall(r'-?\d+', generation)
if len(integers) == 4: # it is refer
mode = 'single'
entities = list()
x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3])
left = x0 / bounding_box_size * image_width
bottom = y0 / bounding_box_size * image_height
right = x1 / bounding_box_size * image_width
top = y1 / bounding_box_size * image_height
entities.append([left, bottom, right, top])
else:
# don't detect any valid bbox to visualize
return None, ''
if len(entities) == 0:
return None, ''
if isinstance(image, Image.Image):
image_h = image.height
image_w = image.width
image = np.array(image)
elif isinstance(image, str):
if os.path.exists(image):
pil_img = Image.open(image).convert("RGB")
image = np.array(pil_img)[:, :, [2, 1, 0]]
image_h = pil_img.height
image_w = pil_img.width
else:
raise ValueError(f"invaild image path, {image}")
elif isinstance(image, torch.Tensor):
image_tensor = image.cpu()
reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
pil_img = T.ToPILImage()(image_tensor)
image_h = pil_img.height
image_w = pil_img.width
image = np.array(pil_img)[:, :, [2, 1, 0]]
else:
raise ValueError(f"invaild image format, {type(image)} for {image}")
indices = list(range(len(entities)))
new_image = image.copy()
previous_bboxes = []
# size of text
text_size = 0.5
# thickness of text
text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1))
box_line = 2
(c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
base_height = int(text_height * 0.675)
text_offset_original = text_height - base_height
text_spaces = 2
# num_bboxes = sum(len(x[-1]) for x in entities)
used_colors = colors # random.sample(colors, k=num_bboxes)
color_id = -1
for entity_idx, entity_name in enumerate(entities):
if mode == 'single' or mode == 'identify':
bboxes = entity_name
bboxes = [bboxes]
else:
bboxes = entities[entity_name]
color_id += 1
for bbox_id, (x1_norm, y1_norm, x2_norm, y2_norm) in enumerate(bboxes):
skip_flag = False
orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm), int(y1_norm), int(x2_norm), int(y2_norm)
color = used_colors[entity_idx % len(used_colors)] # tuple(np.random.randint(0, 255, size=3).tolist())
new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)
if mode == 'all':
l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1
x1 = orig_x1 - l_o
y1 = orig_y1 - l_o
if y1 < text_height + text_offset_original + 2 * text_spaces:
y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
x1 = orig_x1 + r_o
# add text background
(text_width, text_height), _ = cv2.getTextSize(f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size,
text_line)
text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (
text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1
for prev_bbox in previous_bboxes:
if computeIoU((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']) > 0.95 and \
prev_bbox['phrase'] == entity_name:
skip_flag = True
break
while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']):
text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
y1 += (text_height + text_offset_original + 2 * text_spaces)
if text_bg_y2 >= image_h:
text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
text_bg_y2 = image_h
y1 = image_h
break
if not skip_flag:
alpha = 0.5
for i in range(text_bg_y1, text_bg_y2):
for j in range(text_bg_x1, text_bg_x2):
if i < image_h and j < image_w:
if j < text_bg_x1 + 1.35 * c_width:
# original color
bg_color = color
else:
# white
bg_color = [255, 255, 255]
new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(
np.uint8)
cv2.putText(
new_image, f" {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces),
cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
)
previous_bboxes.append(
{'bbox': (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), 'phrase': entity_name})
if mode == 'all':
def color_iterator(colors):
while True:
for color in colors:
yield color
color_gen = color_iterator(colors)
# Add colors to phrases and remove <p></p>
def colored_phrases(match):
phrase = match.group(1)
color = next(color_gen)
return f'<span style="color:rgb{color}">{phrase}</span>'
print('gen before', generation)
generation = re.sub(r'{<\d+><\d+><\d+><\d+>}|<delim>', '', generation)
print('gen after', generation)
generation_colored = re.sub(r'<p>(.*?)</p>', colored_phrases, generation)
else:
generation_colored = ''
pil_image = Image.fromarray(new_image)
return pil_image, generation_colored
def gradio_reset(chat_state, img_list):
if chat_state is not None:
chat_state.messages = []
if img_list is not None:
img_list = []
return None, gr.update(value=None, interactive=True), gr.update(placeholder='Upload your image and chat',
interactive=True), chat_state, img_list
def image_upload_trigger(upload_flag, replace_flag, img_list):
# set the upload flag to true when receive a new image.
# if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
print('flag', upload_flag, replace_flag)
print("SET UPLOAD FLAG!")
upload_flag = 1
if img_list:
print("SET REPLACE FLAG!")
replace_flag = 1
print('flag', upload_flag, replace_flag)
return upload_flag, replace_flag
def example_trigger(text_input, image, upload_flag, replace_flag, img_list):
# set the upload flag to true when receive a new image.
# if there is an old image (and old conversation), set the replace flag to true to reset the conv later.
print('flag', upload_flag, replace_flag)
print("SET UPLOAD FLAG!")
upload_flag = 1
if img_list or replace_flag == 1:
print("SET REPLACE FLAG!")
replace_flag = 1
print('flag', upload_flag, replace_flag)
return upload_flag, replace_flag
def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag, replace_flag):
if isinstance(gr_img, dict):
gr_img, mask = gr_img['image'], gr_img['mask']
else:
mask = None
if '[identify]' in user_message:
# check if user provide bbox in the text input
integers = re.findall(r'-?\d+', user_message)
if len(integers) != 4: # no bbox in text
bbox = mask2bbox(mask)
user_message = user_message + bbox
if len(user_message) == 0:
return gr.update(interactive=True, placeholder='Input should not be empty!'), chatbot, chat_state
if chat_state is None:
chat_state = CONV_VISION.copy()
print('upload flag: {}'.format(upload_flag))
if upload_flag:
if replace_flag:
print('RESET!!!!!!!')
chat_state = CONV_VISION.copy() # new image, reset everything
replace_flag = 0
chatbot = []
print('UPLOAD IMAGE!!')
img_list = []
llm_message = chat.upload_img(gr_img, chat_state, img_list)
upload_flag = 0
chat.ask(user_message, chat_state)
chatbot = chatbot + [[user_message, None]]
if '[identify]' in user_message:
visual_img, _ = visualize_all_bbox_together(gr_img, user_message)
if visual_img is not None:
print('Visualizing the input')
file_path = save_tmp_img(visual_img)
chatbot = chatbot + [[(file_path,), None]]
return '', chatbot, chat_state, img_list, upload_flag, replace_flag
def gradio_answer(chatbot, chat_state, img_list, temperature):
llm_message = chat.answer(conv=chat_state,
img_list=img_list,
temperature=temperature,
max_new_tokens=500,
max_length=2000)[0]
chatbot[-1][1] = llm_message
return chatbot, chat_state
def gradio_stream_answer(chatbot, chat_state, img_list, temperature):
if not isinstance(img_list[0], torch.Tensor):
chat.encode_img(img_list)
streamer = chat.stream_answer(conv=chat_state,
img_list=img_list,
temperature=temperature,
max_new_tokens=500,
max_length=2000)
output = ''
for new_output in streamer:
escapped = escape_markdown(new_output)
output += escapped
chatbot[-1][1] = output
yield chatbot, chat_state
# print('message: ', chat_state.messages)
chat_state.messages[-1][1] = reverse_escape(output) + '</s>'
return chatbot, chat_state
def gradio_visualize(chatbot, gr_img):
if isinstance(gr_img, dict):
gr_img, mask = gr_img['image'], gr_img['mask']
unescaped = reverse_escape(chatbot[-1][1])
visual_img, generation_color = visualize_all_bbox_together(gr_img, unescaped)
if visual_img is not None:
print('Visualizing the output')
if len(generation_color):
chatbot[-1][1] = generation_color
file_path = save_tmp_img(visual_img)
chatbot = chatbot + [[None, (file_path,)]]
return chatbot
def gradio_taskselect(idx):
prompt_list = [
'',
'[grounding] describe this image in detail',
'[refer] ',
'[detection] ',
'[identify] what is this ',
'[vqa] '
]
instruct_list = [
'**Hint:** Type in whatever you want',
'**Hint:** Send the command to generate a grounded image description',
'**Hint:** Type in a phrase about an object in the image and send the command',
'**Hint:** Type in a caption or phrase, and see object locations in the image',
'**Hint:** Draw a bounding box on the uploaded image then send the command. Click the "clear" botton on the top right of the image before redraw',
'**Hint:** Send a question to get a short answer',
]
return prompt_list[idx], instruct_list[idx]
class Chat:
def __init__(self, model, vis_processor, device='cuda:0'):
self.device = device
self.model = model
self.vis_processor = vis_processor
stop_words_ids = [torch.tensor([2]).to(self.device)]
self.stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])
def ask(self, text, conv):
if len(conv.messages) > 0 and conv.messages[-1][0] == conv.roles[0] \
and conv.messages[-1][1][-6:] == '</Img>': # last message is image.
conv.messages[-1][1] = ' '.join([conv.messages[-1][1], text])
else:
conv.append_message(conv.roles[0], text)
def answer_prepare(self, conv, img_list, max_new_tokens=300, num_beams=1, min_length=1, top_p=0.9,
repetition_penalty=1.05, length_penalty=1, temperature=1.0, max_length=2000):
conv.append_message(conv.roles[1], None)
embs = self.get_context_emb(conv, img_list)
current_max_len = embs.shape[1] + max_new_tokens
if current_max_len - max_length > 0:
print('Warning: The number of tokens in current conversation exceeds the max length. '
'The model will not see the contexts outside the range.')
begin_idx = max(0, current_max_len - max_length)
embs = embs[:, begin_idx:]
generation_kwargs = dict(
inputs_embeds=embs,
max_new_tokens=max_new_tokens,
stopping_criteria=self.stopping_criteria,
num_beams=num_beams,
do_sample=True,
min_length=min_length,
top_p=top_p,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
temperature=temperature,
)
return generation_kwargs
def answer(self, conv, img_list, **kargs):
generation_kwargs = self.answer_prepare(conv, img_list, **kargs)
output_token = self.model.llama_model.generate(**generation_dict)[0]
output_text = self.model.llama_tokenizer.decode(output_token, skip_special_tokens=True)
conv.messages[-1][1] = output_text
return output_text, output_token.cpu().numpy()
def stream_answer(self, conv, img_list, **kargs):
generation_kwargs = self.answer_prepare(conv, img_list, **kargs)
streamer = TextIteratorStreamer(self.model.llama_tokenizer, skip_special_tokens=True)
generation_kwargs['streamer'] = streamer
thread = Thread(target=self.model.llama_model.generate, kwargs=generation_kwargs)
thread.start()
return streamer
def encode_img(self, img_list):
image = img_list[0]
img_list.pop(0)
if isinstance(image, str): # is a image path
raw_image = Image.open(image).convert('RGB')
image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
elif isinstance(image, Image.Image):
raw_image = image
image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
elif isinstance(image, torch.Tensor):
if len(image.shape) == 3:
image = image.unsqueeze(0)
image = image.to(self.device)
image_emb, _ = self.model.encode_img(image)
img_list.append(image_emb)
def upload_img(self, image, conv, img_list):
conv.append_message(conv.roles[0], "<Img><ImageHere></Img>")
img_list.append(image)
msg = "Received."
return msg
def get_context_emb(self, conv, img_list):
prompt = conv.get_prompt()
prompt_segs = prompt.split('<ImageHere>')
assert len(prompt_segs) == len(img_list) + 1, "Unmatched numbers of image placeholders and images."
seg_tokens = [
self.model.llama_tokenizer(
seg, return_tensors="pt", add_special_tokens=i == 0).to(self.device).input_ids
# only add bos to the first seg
for i, seg in enumerate(prompt_segs)
]
seg_embs = [self.model.embed_tokens(seg_t) for seg_t in seg_tokens]
mixed_embs = [emb for pair in zip(seg_embs[:-1], img_list) for emb in pair] + [seg_embs[-1]]
mixed_embs = torch.cat(mixed_embs, dim=1)
return mixed_embs
chat = Chat(model, vis_processor, device=device)
title = '**MiniGPT-v2 Demo**'
description = 'Welcome to Our MiniGPT-v2 Chatbot Demo!'
article = 'demo'
introduction = '''
For Abilities Involving Visual Grounding:
1. Grounding: CLICK **Send** to generate a grounded image description.
2. Refer: Input a referring object and CLICK **Send**.
3. Detection: Write a caption or phrase, and CLICK **Send**.
4. Identify: Draw the bounding box on the uploaded image window and CLICK **Send** to generate the bounding box. (CLICK "clear" button before re-drawing next time).
5. VQA: Input a visual question and CLICK **Send**.
6. No Tag: Input whatever you want and CLICK **Send** without any tagging
You can also simply chat in free form!
'''
text_input = gr.Textbox(placeholder='Upload your image and chat', interactive=True, show_label=False, container=False,
scale=8)
with gr.Blocks() as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown(article)
with gr.Row():
with gr.Column(scale=0.5):
image = gr.Image(type="pil", tool='sketch', brush_radius=20)
temperature = gr.Slider(
minimum=0.1,
maximum=2.0,
value=1.0,
step=0.1,
interactive=True,
label="Temperature",
)
clear = gr.Button("Restart")
gr.Markdown(introduction)
with gr.Column():
chat_state = gr.State(value=None)
img_list = gr.State(value=[])
chatbot = gr.Chatbot(label='MiniGPT-v2')
dataset = gr.Dataset(
components=[gr.Textbox(visible=False)],
samples=[['No Tag'], ['Grounding'], ['Refer'], ['Detection'], ['Identify'], ['VQA']],
type="index",
label='Task Shortcuts',
)
task_inst = gr.Markdown('**Hint:** Upload your image and chat')
with gr.Row():
text_input.render()
send = gr.Button("Send", variant='primary', size='sm', scale=1)
upload_flag = gr.State(value=0)
replace_flag = gr.State(value=0)
image.upload(image_upload_trigger, [upload_flag, replace_flag, img_list], [upload_flag, replace_flag])
with gr.Row():
with gr.Column():
gr.Examples(examples=[
["examples_v2/office.jpg", "[grounding] describe this image in detail", upload_flag, replace_flag,
img_list],
["examples_v2/sofa.jpg", "[detection] sofa", upload_flag, replace_flag, img_list],
["examples_v2/2000x1372_wmkn_0012149409555.jpg", "[refer] the world cup", upload_flag, replace_flag,
img_list],
["examples_v2/KFC-20-for-20-Nuggets.jpg", "[identify] what is this {<4><50><30><65>}", upload_flag,
replace_flag, img_list],
], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
outputs=[upload_flag, replace_flag])
with gr.Column():
gr.Examples(examples=[
["examples_v2/glip_test.jpg", "[vqa] where should I hide in this room when playing hide and seek",
upload_flag, replace_flag, img_list],
["examples_v2/float.png", "Please write a poem about the image", upload_flag, replace_flag, img_list],
["examples_v2/thief.png", "Is the weapon fateful", upload_flag, replace_flag, img_list],
["examples_v2/cockdial.png", "What might happen in this image in the next second", upload_flag,
replace_flag, img_list],
], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger,
outputs=[upload_flag, replace_flag])
dataset.click(
gradio_taskselect,
inputs=[dataset],
outputs=[text_input, task_inst],
show_progress="hidden",
postprocess=False,
queue=False,
)
text_input.submit(
gradio_ask,
[text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
[text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
).success(
gradio_stream_answer,
[chatbot, chat_state, img_list, temperature],
[chatbot, chat_state]
).success(
gradio_visualize,
[chatbot, image],
[chatbot],
queue=False,
)
send.click(
gradio_ask,
[text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag],
[text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False
).success(
gradio_stream_answer,
[chatbot, chat_state, img_list, temperature],
[chatbot, chat_state]
).success(
gradio_visualize,
[chatbot, image],
[chatbot],
queue=False,
)
clear.click(gradio_reset, [chat_state, img_list], [chatbot, image, text_input, chat_state, img_list], queue=False)
demo.launch(share=True, enable_queue=True)
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