app.py

# import spaces
import os
import re
import time
import gradio as gr
import torch
from transformers import AutoModelForCausalLM
from transformers import TextIteratorStreamer
from threading import Thread

import importlib.metadata
from importlib import import_module
from transformers.utils import is_flash_attn_2_available
from packaging import version

def check_flash_attention_2_requirements():
    # 检查 Flash Attention 2 是否可用
    flash_attn_2_available = is_flash_attn_2_available()
    if not flash_attn_2_available:
        raise ImportError("Flash Attention 2 is not available.")

    # 获取已安装的 flash_attn 版本
    try:
        installed_version = importlib.metadata.version("flash_attn")
    except importlib.metadata.PackageNotFoundError:
        raise ImportError("flash_attn package is not installed.")

    # 解析已安装的版本和所需的最低版本
    parsed_installed_version = version.parse(installed_version)
    required_version = version.parse("2.6.3")

    # 检查版本是否满足要求
    if parsed_installed_version < required_version:
        raise ImportError(f"flash_attn version {installed_version} is installed, but version >= 2.6.3 is required.")

    print("All requirements for Flash Attention 2 are met.")

# 使用 try-except 块来捕获和显示具体的错误
try:
    check_flash_attention_2_requirements()
except ImportError as e:
    print(f"Error: {e}")
    print("Using `flash_attention_2` requires having `flash_attn>=2.6.3` installed.")
else:
    print("Flash Attention 2 can be used.")

model_name = 'AIDC-AI/Ovis2-16B'

# load model
model = AutoModelForCausalLM.from_pretrained(model_name,
                                             torch_dtype=torch.bfloat16,
                                             multimodal_max_length=8192,
                                             trust_remote_code=True).to(device='cuda')
text_tokenizer = model.get_text_tokenizer()
visual_tokenizer = model.get_visual_tokenizer()
streamer = TextIteratorStreamer(text_tokenizer, skip_prompt=True, skip_special_tokens=True)
image_placeholder = '<image>'
cur_dir = os.path.dirname(os.path.abspath(__file__))

def submit_chat(chatbot, text_input):
    response = ''
    chatbot.append((text_input, response))
    return chatbot ,''

@spaces.GPU
def ovis_chat(chatbot, image_input):
    # preprocess inputs
    conversations = [{
        "from": "system",
        "value": "You are Ovis, a multimodal large language model developed by Alibaba International, and your task is to provide reliable and structured responses to users. 你是Ovis，由阿里国际研发的多模态大模型，你的任务是为用户提供可靠、结构化的回复。"
    }]
    response = ""
    text_input = chatbot[-1][0]
    for query, response in chatbot[:-1]:
        conversations.append({
            "from": "human",
            "value": query
        })
        conversations.append({
            "from": "gpt",
            "value": response
        })
    text_input = text_input.replace(image_placeholder, '')
    conversations.append({
        "from": "human",
        "value": text_input
    })
    if image_input is not None:
        conversations[0]["value"] = image_placeholder + '\n' + conversations[0]["value"]
    prompt, input_ids, pixel_values = model.preprocess_inputs(conversations, [image_input])
    attention_mask = torch.ne(input_ids, text_tokenizer.pad_token_id)
    input_ids = input_ids.unsqueeze(0).to(device=model.device)
    attention_mask = attention_mask.unsqueeze(0).to(device=model.device)
    if image_input is None:
        pixel_values = [None]
    else:
        pixel_values = [pixel_values.to(dtype=visual_tokenizer.dtype, device=visual_tokenizer.device)]

    with torch.inference_mode():
        gen_kwargs = dict(
            max_new_tokens=1536,
            do_sample=False,
            top_p=None,
            top_k=None,
            temperature=None,
            repetition_penalty=None,
            eos_token_id=model.generation_config.eos_token_id,
            pad_token_id=text_tokenizer.pad_token_id,
            use_cache=True
        )
        response = ""
        thread = Thread(target=model.generate, 
                    kwargs={"inputs": input_ids,
                            "pixel_values": pixel_values,
                            "attention_mask": attention_mask,
                            "streamer": streamer,
                            **gen_kwargs})
        thread.start()
        for new_text in streamer:
            response += new_text
            chatbot[-1][1] = response
            yield chatbot
        thread.join()
        # debug
        print('*'*60)
        print('*'*60)
        print('OVIS_CONV_START')
        for i, (request, answer) in enumerate(chatbot[:-1], 1):
            print(f'Q{i}:\n {request}')
            print(f'A{i}:\n {answer}')
        print('New_Q:\n', text_input)
        print('New_A:\n', response)
        print('OVIS_CONV_END')

def clear_chat():
    return [], None, ""

with open(f"{cur_dir}/resource/logo.svg", "r", encoding="utf-8") as svg_file:
    svg_content = svg_file.read()
font_size = "2.5em"
svg_content = re.sub(r'(<svg[^>]*)(>)', rf'\1 height="{font_size}" style="vertical-align: middle; display: inline-block;"\2', svg_content)
html = f"""
<p align="center" style="font-size: {font_size}; line-height: 1;">
    <span style="display: inline-block; vertical-align: middle;">{svg_content}</span>
    <span style="display: inline-block; vertical-align: middle;">{model_name.split('/')[-1]}</span>
</p>
<center><font size=3><b>Ovis</b> has been open-sourced on <a href='https://huggingface.co/{model_name}'>😊 Huggingface</a> and <a href='https://github.com/AIDC-AI/Ovis'>🌟 GitHub</a>. If you find Ovis useful, a like❤️ or a star🌟 would be appreciated.</font></center>
"""

latex_delimiters_set = [{
        "left": "\\(",
        "right": "\\)",
        "display": True 
    }, {
        "left": "\\begin{equation}",
        "right": "\\end{equation}",
        "display": True 
    }, {
        "left": "\\begin{align}",
        "right": "\\end{align}",
        "display": True
    }, {
        "left": "\\begin{alignat}",
        "right": "\\end{alignat}",
        "display": True
    }, {
        "left": "\\begin{gather}",
        "right": "\\end{gather}",
        "display": True
    }, {
        "left": "\\begin{CD}",
        "right": "\\end{CD}",
        "display": True
    }, {
        "left": "\\[",
        "right": "\\]",
        "display": True
    }]

text_input = gr.Textbox(label="prompt", placeholder="Enter your text here...", lines=1, container=False)
with gr.Blocks(title=model_name.split('/')[-1], theme=gr.themes.Ocean()) as demo:
    gr.HTML(html)
    with gr.Row():
        with gr.Column(scale=3):
            image_input = gr.Image(label="image", height=350, type="pil")
            gr.Examples(
                examples=[
                    [f"{cur_dir}/examples/case0.png", "Find the area of the shaded region."],
                    [f"{cur_dir}/examples/case1.png", "explain this model to me."],
                    [f"{cur_dir}/examples/case2.png", "What is net profit margin as a percentage of total revenue?"],
                ],
                inputs=[image_input, text_input]
            )
        with gr.Column(scale=7):
            chatbot = gr.Chatbot(label="Ovis", layout="panel", height=600, show_copy_button=True, latex_delimiters=latex_delimiters_set)
            text_input.render()
            with gr.Row():
                send_btn = gr.Button("Send", variant="primary")
                clear_btn = gr.Button("Clear", variant="secondary")

    send_click_event = send_btn.click(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot)
    submit_event = text_input.submit(submit_chat, [chatbot, text_input], [chatbot, text_input]).then(ovis_chat,[chatbot, image_input],chatbot)
    clear_btn.click(clear_chat, outputs=[chatbot, image_input, text_input])

demo.launch()