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#!/usr/bin/env python
# encoding: utf-8
import spaces
import torch
import argparse
from transformers import AutoModel, AutoTokenizer
import gradio as gr
from PIL import Image
from decord import VideoReader, cpu
import io
import os
import copy
import requests
import base64
import json
import traceback
import re
import modelscope_studio as mgr
# README, How to run demo on different devices
# For Nvidia GPUs.
# python web_demo_2.6.py --device cuda
# For Mac with MPS (Apple silicon or AMD GPUs).
# PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.6.py --device mps
# Argparser
parser = argparse.ArgumentParser(description='demo')
parser.add_argument('--device', type=str, default='cuda', help='cuda or mps')
parser.add_argument('--multi-gpus', action='store_true', default=False, help='use multi-gpus')
args = parser.parse_args()
device = args.device
assert device in ['cuda', 'mps']
# Load model
model_path = 'openbmb/MiniCPM-V-2_6'
if 'int4' in model_path:
if device == 'mps':
print('Error: running int4 model with bitsandbytes on Mac is not supported right now.')
exit()
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
else:
if False: #args.multi_gpus:
from accelerate import load_checkpoint_and_dispatch, init_empty_weights, infer_auto_device_map
with init_empty_weights():
#model = AutoModel.from_pretrained(model_path, trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16)
device_map = infer_auto_device_map(model, max_memory={0: "10GB", 1: "10GB"},
no_split_module_classes=['SiglipVisionTransformer', 'Qwen2DecoderLayer'])
device_id = device_map["llm.model.embed_tokens"]
device_map["llm.lm_head"] = device_id # firtt and last layer should be in same device
device_map["vpm"] = device_id
device_map["resampler"] = device_id
device_id2 = device_map["llm.model.layers.26"]
device_map["llm.model.layers.8"] = device_id2
device_map["llm.model.layers.9"] = device_id2
device_map["llm.model.layers.10"] = device_id2
device_map["llm.model.layers.11"] = device_id2
device_map["llm.model.layers.12"] = device_id2
device_map["llm.model.layers.13"] = device_id2
device_map["llm.model.layers.14"] = device_id2
device_map["llm.model.layers.15"] = device_id2
device_map["llm.model.layers.16"] = device_id2
#print(device_map)
#model = load_checkpoint_and_dispatch(model, model_path, dtype=torch.bfloat16, device_map=device_map)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map=device_map)
else:
#model = AutoModel.from_pretrained(model_path, trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16)
model = model.to(device=device)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model.eval()
ERROR_MSG = "Error, please retry"
model_name = 'MiniCPM-V 2.6'
MAX_NUM_FRAMES = 64
IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
VIDEO_EXTENSIONS = {'.mp4', '.mkv', '.mov', '.avi', '.flv', '.wmv', '.webm', '.m4v'}
def get_file_extension(filename):
return os.path.splitext(filename)[1].lower()
def is_image(filename):
return get_file_extension(filename) in IMAGE_EXTENSIONS
def is_video(filename):
return get_file_extension(filename) in VIDEO_EXTENSIONS
form_radio = {
'choices': ['Beam Search', 'Sampling'],
#'value': 'Beam Search',
'value': 'Sampling',
'interactive': True,
'label': 'Decode Type'
}
def create_component(params, comp='Slider'):
if comp == 'Slider':
return gr.Slider(
minimum=params['minimum'],
maximum=params['maximum'],
value=params['value'],
step=params['step'],
interactive=params['interactive'],
label=params['label']
)
elif comp == 'Radio':
return gr.Radio(
choices=params['choices'],
value=params['value'],
interactive=params['interactive'],
label=params['label']
)
elif comp == 'Button':
return gr.Button(
value=params['value'],
interactive=True
)
def create_multimodal_input(upload_image_disabled=False, upload_video_disabled=False):
return mgr.MultimodalInput(value=None, upload_image_button_props={'label': 'Upload Image', 'disabled': upload_image_disabled, 'file_count': 'multiple'},
upload_video_button_props={'label': 'Upload Video', 'disabled': upload_video_disabled, 'file_count': 'single'},
submit_button_props={'label': 'Submit'})
@spaces.GPU(duration=120)
def chat(img, msgs, ctx, params=None, vision_hidden_states=None):
try:
if msgs[-1]['role'] == 'assistant':
msgs = msgs[:-1] # remove last which is added for streaming
print('msgs:', msgs)
answer = model.chat(
image=None,
msgs=msgs,
tokenizer=tokenizer,
**params
)
if params['stream'] is False:
res = re.sub(r'(<box>.*</box>)', '', answer)
res = res.replace('<ref>', '')
res = res.replace('</ref>', '')
res = res.replace('<box>', '')
answer = res.replace('</box>', '')
print('answer:')
for char in answer:
print(char, flush=True, end='')
yield char
except Exception as e:
print(e)
traceback.print_exc()
yield ERROR_MSG
def encode_image(image):
if not isinstance(image, Image.Image):
if hasattr(image, 'path'):
image = Image.open(image.path).convert("RGB")
else:
image = Image.open(image.file.path).convert("RGB")
# resize to max_size
max_size = 448*16
if max(image.size) > max_size:
w,h = image.size
if w > h:
new_w = max_size
new_h = int(h * max_size / w)
else:
new_h = max_size
new_w = int(w * max_size / h)
image = image.resize((new_w, new_h), resample=Image.BICUBIC)
return image
## save by BytesIO and convert to base64
#buffered = io.BytesIO()
#image.save(buffered, format="png")
#im_b64 = base64.b64encode(buffered.getvalue()).decode()
#return {"type": "image", "pairs": im_b64}
def encode_video(video):
def uniform_sample(l, n):
gap = len(l) / n
idxs = [int(i * gap + gap / 2) for i in range(n)]
return [l[i] for i in idxs]
if hasattr(video, 'path'):
vr = VideoReader(video.path, ctx=cpu(0))
else:
vr = VideoReader(video.file.path, ctx=cpu(0))
sample_fps = round(vr.get_avg_fps() / 1) # FPS
frame_idx = [i for i in range(0, len(vr), sample_fps)]
if len(frame_idx)>MAX_NUM_FRAMES:
frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
video = vr.get_batch(frame_idx).asnumpy()
video = [Image.fromarray(v.astype('uint8')) for v in video]
video = [encode_image(v) for v in video]
print('video frames:', len(video))
return video
def check_mm_type(mm_file):
if hasattr(mm_file, 'path'):
path = mm_file.path
else:
path = mm_file.file.path
if is_image(path):
return "image"
if is_video(path):
return "video"
return None
def encode_mm_file(mm_file):
if check_mm_type(mm_file) == 'image':
return [encode_image(mm_file)]
if check_mm_type(mm_file) == 'video':
return encode_video(mm_file)
return None
def make_text(text):
#return {"type": "text", "pairs": text} # # For remote call
return text
def encode_message(_question):
files = _question.files
question = _question.text
pattern = r"\[mm_media\]\d+\[/mm_media\]"
matches = re.split(pattern, question)
message = []
if len(matches) != len(files) + 1:
gr.Warning("Number of Images not match the placeholder in text, please refresh the page to restart!")
assert len(matches) == len(files) + 1
text = matches[0].strip()
if text:
message.append(make_text(text))
for i in range(len(files)):
message += encode_mm_file(files[i])
text = matches[i + 1].strip()
if text:
message.append(make_text(text))
return message
def check_has_videos(_question):
images_cnt = 0
videos_cnt = 0
for file in _question.files:
if check_mm_type(file) == "image":
images_cnt += 1
else:
videos_cnt += 1
return images_cnt, videos_cnt
def count_video_frames(_context):
num_frames = 0
for message in _context:
for item in message["content"]:
#if item["type"] == "image": # For remote call
if isinstance(item, Image.Image):
num_frames += 1
return num_frames
def request(_question, _chat_bot, _app_cfg):
images_cnt = _app_cfg['images_cnt']
videos_cnt = _app_cfg['videos_cnt']
files_cnts = check_has_videos(_question)
if files_cnts[1] + videos_cnt > 1 or (files_cnts[1] + videos_cnt == 1 and files_cnts[0] + images_cnt > 0):
gr.Warning("Only supports single video file input right now!")
return _question, _chat_bot, _app_cfg
if files_cnts[1] + videos_cnt + files_cnts[0] + images_cnt <= 0:
gr.Warning("Please chat with at least one image or video.")
return _question, _chat_bot, _app_cfg
_chat_bot.append((_question, None))
images_cnt += files_cnts[0]
videos_cnt += files_cnts[1]
_app_cfg['images_cnt'] = images_cnt
_app_cfg['videos_cnt'] = videos_cnt
upload_image_disabled = videos_cnt > 0
upload_video_disabled = videos_cnt > 0 or images_cnt > 0
return create_multimodal_input(upload_image_disabled, upload_video_disabled), _chat_bot, _app_cfg
def respond(_chat_bot, _app_cfg, params_form):
if len(_app_cfg) == 0:
yield (_chat_bot, _app_cfg)
elif _app_cfg['images_cnt'] == 0 and _app_cfg['videos_cnt'] == 0:
yield(_chat_bot, _app_cfg)
else:
_question = _chat_bot[-1][0]
_context = _app_cfg['ctx'].copy()
_context.append({'role': 'user', 'content': encode_message(_question)})
videos_cnt = _app_cfg['videos_cnt']
if params_form == 'Beam Search':
params = {
'sampling': False,
'stream': False,
'num_beams': 3,
'repetition_penalty': 1.2,
"max_new_tokens": 2048
}
else:
params = {
'sampling': True,
'stream': True,
'top_p': 0.8,
'top_k': 100,
'temperature': 0.7,
'repetition_penalty': 1.05,
"max_new_tokens": 2048
}
params["max_inp_length"] = 4352 # 4096+256
if videos_cnt > 0:
#params["max_inp_length"] = 4352 # 4096+256
params["use_image_id"] = False
params["max_slice_nums"] = 1 if count_video_frames(_context) > 16 else 2
gen = chat("", _context, None, params)
_context.append({"role": "assistant", "content": [""]})
_chat_bot[-1][1] = ""
for _char in gen:
_chat_bot[-1][1] += _char
_context[-1]["content"][0] += _char
yield (_chat_bot, _app_cfg)
_app_cfg['ctx']=_context
yield (_chat_bot, _app_cfg)
def fewshot_add_demonstration(_image, _user_message, _assistant_message, _chat_bot, _app_cfg):
ctx = _app_cfg["ctx"]
message_item = []
if _image is not None:
image = Image.open(_image).convert("RGB")
ctx.append({"role": "user", "content": [encode_image(image), make_text(_user_message)]})
message_item.append({"text": "[mm_media]1[/mm_media]" + _user_message, "files": [_image]})
_app_cfg["images_cnt"] += 1
else:
if _user_message:
ctx.append({"role": "user", "content": [make_text(_user_message)]})
message_item.append({"text": _user_message, "files": []})
else:
message_item.append(None)
if _assistant_message:
ctx.append({"role": "assistant", "content": [make_text(_assistant_message)]})
message_item.append({"text": _assistant_message, "files": []})
else:
message_item.append(None)
_chat_bot.append(message_item)
return None, "", "", _chat_bot, _app_cfg
def fewshot_request(_image, _user_message, _chat_bot, _app_cfg):
if _app_cfg["images_cnt"] == 0 and not _image:
gr.Warning("Please chat with at least one image.")
return None, '', '', _chat_bot, _app_cfg
if _image:
_chat_bot.append([
{"text": "[mm_media]1[/mm_media]" + _user_message, "files": [_image]},
""
])
_app_cfg["images_cnt"] += 1
else:
_chat_bot.append([
{"text": _user_message, "files": [_image]},
""
])
return None, '', '', _chat_bot, _app_cfg
def regenerate_button_clicked(_chat_bot, _app_cfg):
if len(_chat_bot) <= 1 or not _chat_bot[-1][1]:
gr.Warning('No question for regeneration.')
return None, None, '', '', _chat_bot, _app_cfg
if _app_cfg["chat_type"] == "Chat":
images_cnt = _app_cfg['images_cnt']
videos_cnt = _app_cfg['videos_cnt']
_question = _chat_bot[-1][0]
_chat_bot = _chat_bot[:-1]
_app_cfg['ctx'] = _app_cfg['ctx'][:-2]
files_cnts = check_has_videos(_question)
images_cnt -= files_cnts[0]
videos_cnt -= files_cnts[1]
_app_cfg['images_cnt'] = images_cnt
_app_cfg['videos_cnt'] = videos_cnt
_question, _chat_bot, _app_cfg = request(_question, _chat_bot, _app_cfg)
return _question, None, '', '', _chat_bot, _app_cfg
else:
last_message = _chat_bot[-1][0]
last_image = None
last_user_message = ''
if last_message.text:
last_user_message = last_message.text
if last_message.files:
last_image = last_message.files[0].file.path
_chat_bot[-1][1] = ""
_app_cfg['ctx'] = _app_cfg['ctx'][:-2]
return _question, None, '', '', _chat_bot, _app_cfg
def flushed():
return gr.update(interactive=True)
def clear(txt_message, chat_bot, app_session):
txt_message.files.clear()
txt_message.text = ''
chat_bot = copy.deepcopy(init_conversation)
app_session['sts'] = None
app_session['ctx'] = []
app_session['images_cnt'] = 0
app_session['videos_cnt'] = 0
return create_multimodal_input(), chat_bot, app_session, None, '', ''
def select_chat_type(_tab, _app_cfg):
_app_cfg["chat_type"] = _tab
return _app_cfg
init_conversation = [
[
None,
{
# The first message of bot closes the typewriter.
"text": "You can talk to me now",
"flushing": False
}
],
]
css = """
.example label { font-size: 16px;}
"""
introduction = """
## Features:
1. Chat with single image
2. Chat with multiple images
3. Chat with video
4. In-context few-shot learning
Click `How to use` tab to see examples.
"""
with gr.Blocks(css=css) as demo:
with gr.Tab(model_name):
with gr.Row():
with gr.Column(scale=1, min_width=300):
gr.Markdown(value=introduction)
params_form = create_component(form_radio, comp='Radio')
regenerate = create_component({'value': 'Regenerate'}, comp='Button')
clear_button = create_component({'value': 'Clear History'}, comp='Button')
with gr.Column(scale=3, min_width=500):
app_session = gr.State({'sts':None,'ctx':[], 'images_cnt': 0, 'videos_cnt': 0, 'chat_type': 'Chat'})
chat_bot = mgr.Chatbot(label=f"Chat with {model_name}", value=copy.deepcopy(init_conversation), height=560, flushing=False, bubble_full_width=False)
with gr.Tab("Chat") as chat_tab:
txt_message = create_multimodal_input()
chat_tab_label = gr.Textbox(value="Chat", interactive=False, visible=False)
txt_message.submit(
request,
[txt_message, chat_bot, app_session],
[txt_message, chat_bot, app_session]
).then(
respond,
[chat_bot, app_session, params_form],
[chat_bot, app_session]
)
with gr.Tab("Few Shot") as fewshot_tab:
fewshot_tab_label = gr.Textbox(value="Few Shot", interactive=False, visible=False)
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="filepath", sources=["upload"])
with gr.Column(scale=3):
user_message = gr.Textbox(label="User")
assistant_message = gr.Textbox(label="Assistant")
with gr.Row():
add_demonstration_button = gr.Button("Add Example")
generate_button = gr.Button(value="Generate", variant="primary")
add_demonstration_button.click(
fewshot_add_demonstration,
[image_input, user_message, assistant_message, chat_bot, app_session],
[image_input, user_message, assistant_message, chat_bot, app_session]
)
generate_button.click(
fewshot_request,
[image_input, user_message, chat_bot, app_session],
[image_input, user_message, assistant_message, chat_bot, app_session]
).then(
respond,
[chat_bot, app_session, params_form],
[chat_bot, app_session]
)
chat_tab.select(
select_chat_type,
[chat_tab_label, app_session],
[app_session]
)
chat_tab.select( # do clear
clear,
[txt_message, chat_bot, app_session],
[txt_message, chat_bot, app_session, image_input, user_message, assistant_message]
)
fewshot_tab.select(
select_chat_type,
[fewshot_tab_label, app_session],
[app_session]
)
fewshot_tab.select( # do clear
clear,
[txt_message, chat_bot, app_session],
[txt_message, chat_bot, app_session, image_input, user_message, assistant_message]
)
chat_bot.flushed(
flushed,
outputs=[txt_message]
)
regenerate.click(
regenerate_button_clicked,
[chat_bot, app_session],
[txt_message, image_input, user_message, assistant_message, chat_bot, app_session]
).then(
respond,
[chat_bot, app_session, params_form],
[chat_bot, app_session]
)
clear_button.click(
clear,
[txt_message, chat_bot, app_session],
[txt_message, chat_bot, app_session, image_input, user_message, assistant_message]
)
with gr.Tab("How to use"):
with gr.Column():
with gr.Row():
image_example = gr.Image(value="http://thunlp.oss-cn-qingdao.aliyuncs.com/multi_modal/never_delete/m_bear2.gif", label='1. Chat with single or multiple images', interactive=False, width=400, elem_classes="example")
example2 = gr.Image(value="http://thunlp.oss-cn-qingdao.aliyuncs.com/multi_modal/never_delete/video2.gif", label='2. Chat with video', interactive=False, width=400, elem_classes="example")
example3 = gr.Image(value="http://thunlp.oss-cn-qingdao.aliyuncs.com/multi_modal/never_delete/fshot.gif", label='3. Few shot', interactive=False, width=400, elem_classes="example")
# launch
#demo.launch(share=False, debug=True, show_api=False, server_port=8885, server_name="0.0.0.0")
demo.queue()
demo.launch(show_api=False)
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