MiniCPM-V/web_demo_2.6.py

#!/usr/bin/env python
# encoding: utf-8
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 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)
        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)
    else:
        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(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'})


def chat(img, msgs, ctx, params=None, vision_hidden_states=None):
    try:
        print('msgs:', msgs)
        answer = model.chat(
            image=None,
            msgs=msgs,
            tokenizer=tokenizer,
            **params
        )
        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:', answer)
        return 0, answer, None, None
    except Exception as e:
        print(e)
        traceback.print_exc()
        return -1, ERROR_MSG, None, None


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 respond(_question, _chat_bot, _app_cfg, params_form):
    _context = _app_cfg['ctx'].copy()
    _context.append({'role': 'user', 'content': encode_message(_question)})

    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 params_form == 'Beam Search':
        params = {
            'sampling': False,
            'num_beams': 3,
            'repetition_penalty': 1.2,
            "max_new_tokens": 2048
        }
    else:
        params = {
            'sampling': True,
            'top_p': 0.8,
            'top_k': 100,
            'temperature': 0.7,
            'repetition_penalty': 1.05,
            "max_new_tokens": 2048
        }
    
    if files_cnts[1] + 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

    code, _answer, _, sts = chat("", _context, None, params)

    images_cnt += files_cnts[0]
    videos_cnt += files_cnts[1]
    _context.append({"role": "assistant", "content": [make_text(_answer)]}) 
    _chat_bot.append((_question, _answer))
    if code == 0:
        _app_cfg['ctx']=_context
        _app_cfg['sts']=sts
    _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 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]})
    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_respond(_image, _user_message, _chat_bot, _app_cfg, params_form):
    user_message_contents = []
    _context = _app_cfg["ctx"].copy()
    if _image:
        image = Image.open(_image).convert("RGB")
        user_message_contents += [encode_image(image)]
    if _user_message:
        user_message_contents += [make_text(_user_message)]
    if user_message_contents:
        _context.append({"role": "user", "content": user_message_contents})

    if params_form == 'Beam Search':
        params = {
            'sampling': False,
            'num_beams': 3,
            'repetition_penalty': 1.2,
            "max_new_tokens": 2048
        }
    else:
        params = {
            'sampling': True,
            'top_p': 0.8,
            'top_k': 100,
            'temperature': 0.7,
            'repetition_penalty': 1.05,
            "max_new_tokens": 2048
        }
    
    code, _answer, _, sts = chat("", _context, None, params)

    _context.append({"role": "assistant", "content": [make_text(_answer)]})

    if _image:
        _chat_bot.append([
            {"text": "[mm_media]1[/mm_media]" + _user_message, "files": [_image]},
            {"text": _answer, "files": []}        
        ])
    else:
        _chat_bot.append([
            {"text": _user_message, "files": [_image]},
            {"text": _answer, "files": []}        
        ])
    if code == 0:
        _app_cfg['ctx']=_context
        _app_cfg['sts']=sts
    return None, '', '', _chat_bot, _app_cfg


def regenerate_button_clicked(_question, _image, _user_message, _assistant_message, _chat_bot, _app_cfg, params_form):
    if len(_chat_bot) <= 1 or not _chat_bot[-1][1]:
        gr.Warning('No question for regeneration.')
        return '', _image, _user_message, _assistant_message, _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
        upload_image_disabled = videos_cnt > 0
        upload_video_disabled = videos_cnt > 0 or images_cnt > 0
        _question, _chat_bot, _app_cfg = respond(_question, _chat_bot, _app_cfg, params_form)
        return _question, _image, _user_message, _assistant_message, _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 = _chat_bot[:-1]
        _app_cfg['ctx'] = _app_cfg['ctx'][:-2]
        _image, _user_message, _assistant_message, _chat_bot, _app_cfg = fewshot_respond(last_image, last_user_message, _chat_bot, _app_cfg, params_form)
        return _question, _image, _user_message, _assistant_message, _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 = """
video { height: auto !important; }
.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=600, 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(
                        respond,
                        [txt_message, chat_bot, app_session, params_form], 
                        [txt_message, 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_respond,
                        [image_input, user_message, chat_bot, app_session, params_form],
                        [image_input, user_message, assistant_message, 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,
                    [txt_message, image_input, user_message, assistant_message, chat_bot, app_session, params_form],
                    [txt_message, image_input, user_message, assistant_message, 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")