app.py

# Copyright (c) Alibaba Cloud.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
os.system('pip install tiktoken')
os.system('pip install "modelscope" --upgrade -f https://pypi.org/project/modelscope/')
os.system('pip install transformers_stream_generator')

# os.environ['CUDA_VISIBLE_DEVICES'] = '0,1'
from argparse import ArgumentParser
from pathlib import Path

import copy
import gradio as gr
import os
import re
import secrets
import tempfile
from modelscope import (
    AutoModelForCausalLM, AutoTokenizer, GenerationConfig, snapshot_download
)

DEFAULT_CKPT_PATH = 'qwen/Qwen-VL-Chat'
REVISION = 'v1.0.4'
BOX_TAG_PATTERN = r"<box>([\s\S]*?)</box>"
PUNCTUATION = "！？。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏."


def _get_args():
    parser = ArgumentParser()
    parser.add_argument("-c", "--checkpoint-path", type=str, default=DEFAULT_CKPT_PATH,
                        help="Checkpoint name or path, default to %(default)r")
    parser.add_argument("--revision", type=str, default=REVISION)
    parser.add_argument("--cpu-only", action="store_true", help="Run demo with CPU only")

    parser.add_argument("--share", action="store_true", default=False,
                        help="Create a publicly shareable link for the interface.")
    parser.add_argument("--inbrowser", action="store_true", default=False,
                        help="Automatically launch the interface in a new tab on the default browser.")
    parser.add_argument("--server-port", type=int, default=8000,
                        help="Demo server port.")
    parser.add_argument("--server-name", type=str, default="127.0.0.1",
                        help="Demo server name.")

    args = parser.parse_args()
    return args


def _load_model_tokenizer(args):
    model_id = args.checkpoint_path
    model_dir = snapshot_download(model_id, revision=args.revision)
    tokenizer = AutoTokenizer.from_pretrained(
        model_dir, trust_remote_code=True, resume_download=True,
    )

    if args.cpu_only:
        device_map = "cpu"
    else:
        device_map = "auto"

    model = AutoModelForCausalLM.from_pretrained(
        model_dir,
        device_map=device_map,
        trust_remote_code=True,
        resume_download=True,
    ).eval()
    model.generation_config = GenerationConfig.from_pretrained(
        model_dir, trust_remote_code=True, resume_download=True,
    )

    return model, tokenizer


def _parse_text(text):
    lines = text.split("\n")
    lines = [line for line in lines if line != ""]
    count = 0
    for i, line in enumerate(lines):
        if "```" in line:
            count += 1
            items = line.split("`")
            if count % 2 == 1:
                lines[i] = f'<pre><code class="language-{items[-1]}">'
            else:
                lines[i] = f"<br></code></pre>"
        else:
            if i > 0:
                if count % 2 == 1:
                    line = line.replace("`", r"\`")
                    line = line.replace("<", "&lt;")
                    line = line.replace(">", "&gt;")
                    line = line.replace(" ", "&nbsp;")
                    line = line.replace("*", "&ast;")
                    line = line.replace("_", "&lowbar;")
                    line = line.replace("-", "&#45;")
                    line = line.replace(".", "&#46;")
                    line = line.replace("!", "&#33;")
                    line = line.replace("(", "&#40;")
                    line = line.replace(")", "&#41;")
                    line = line.replace("$", "&#36;")
                lines[i] = "<br>" + line
    text = "".join(lines)
    return text


def _launch_demo(args, model, tokenizer):
    uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(
        Path(tempfile.gettempdir()) / "gradio"
    )

    def predict(_chatbot, task_history):
        chat_query = _chatbot[-1][0]
        query = task_history[-1][0]
        print("User: " + _parse_text(query))
        history_cp = copy.deepcopy(task_history)
        full_response = ""

        history_filter = []
        pic_idx = 1
        pre = ""
        for i, (q, a) in enumerate(history_cp):
            if isinstance(q, (tuple, list)):
                q = f'Picture {pic_idx}: <img>{q[0]}</img>'
                pre += q + '\n'
                pic_idx += 1
            else:
                pre += q
                history_filter.append((pre, a))
                pre = ""
        history, message = history_filter[:-1], history_filter[-1][0]
        response, history = model.chat(tokenizer, message, history=history)
        image = tokenizer.draw_bbox_on_latest_picture(response, history)
        if image is not None:
            temp_dir = secrets.token_hex(20)
            temp_dir = Path(uploaded_file_dir) / temp_dir
            temp_dir.mkdir(exist_ok=True, parents=True)
            name = f"tmp{secrets.token_hex(5)}.jpg"
            filename = temp_dir / name
            image.save(str(filename))
            _chatbot[-1] = (_parse_text(chat_query), (str(filename),))
            chat_response = response.replace("<ref>", "")
            chat_response = chat_response.replace(r"</ref>", "")
            chat_response = re.sub(BOX_TAG_PATTERN, "", chat_response)
            if chat_response != "":
                _chatbot.append((None, chat_response))
        else:
            _chatbot[-1] = (_parse_text(chat_query), response)
        full_response = _parse_text(response)

        task_history[-1] = (query, full_response)
        print("Qwen-VL-Chat: " + _parse_text(full_response))
        task_history = task_history[-10:]
        return _chatbot

    def regenerate(_chatbot, task_history):
        if not task_history:
            return _chatbot
        item = task_history[-1]
        if item[1] is None:
            return _chatbot
        task_history[-1] = (item[0], None)
        chatbot_item = _chatbot.pop(-1)
        if chatbot_item[0] is None:
            _chatbot[-1] = (_chatbot[-1][0], None)
        else:
            _chatbot.append((chatbot_item[0], None))
        return predict(_chatbot, task_history)

    def add_text(history, task_history, text):
        task_text = text
        if len(text) >= 2 and text[-1] in PUNCTUATION and text[-2] not in PUNCTUATION:
            task_text = text[:-1]
        history = history + [(_parse_text(text), None)]
        task_history = task_history + [(task_text, None)]
        return history, task_history, ""

    def add_file(history, task_history, file):
        history = history + [((file.name,), None)]
        task_history = task_history + [((file.name,), None)]
        return history, task_history

    def reset_user_input():
        return gr.update(value="")

    def reset_state(task_history):
        task_history.clear()
        return []

    with gr.Blocks() as demo:
        gr.Markdown("""\
<p align="center"><img src="https://modelscope.cn/api/v1/models/qwen/Qwen-VL-Chat/repo?Revision=master&FilePath=assets/logo.jpg&View=true" style="height: 80px"/><p>""")
        gr.Markdown("""<center><font size=8>Qwen-VL-Chat Bot</center>""")
        gr.Markdown(
            """\
<center><font size=3>This WebUI is based on Qwen-VL-Chat, developed by Alibaba Cloud. \
(本WebUI基于Qwen-VL-Chat打造，实现聊天机器人功能。)</center>""")
        gr.Markdown("""\
<center><font size=4>Qwen-VL <a href="https://modelscope.cn/models/qwen/Qwen-VL/summary">🤖 </a> 
| <a href="https://huggingface.co/Qwen/Qwen-VL">🤗</a>&nbsp ｜ 
Qwen-VL-Chat <a href="https://modelscope.cn/models/qwen/Qwen-VL-Chat/summary">🤖 </a> | 
<a href="https://huggingface.co/Qwen/Qwen-VL-Chat">🤗</a>&nbsp ｜ 
&nbsp<a href="https://github.com/QwenLM/Qwen-VL">Github</a></center>""")

        chatbot = gr.Chatbot(label='Qwen-VL-Chat', elem_classes="control-height").style(height=500)
        query = gr.Textbox(lines=2, label='Input')
        task_history = gr.State([])

        with gr.Row():
            addfile_btn = gr.UploadButton("📁 Upload (上传文件)", file_types=["image"])
            submit_btn = gr.Button("🚀 Submit (发送)")
            regen_btn = gr.Button("🤔️ Regenerate (重试)")
            empty_bin = gr.Button("🧹 Clear History (清除历史)")

        submit_btn.click(add_text, [chatbot, task_history, query], [chatbot, task_history]).then(
            predict, [chatbot, task_history], [chatbot], show_progress=True
        )
        submit_btn.click(reset_user_input, [], [query])
        empty_bin.click(reset_state, [task_history], [chatbot], show_progress=True)
        regen_btn.click(regenerate, [chatbot, task_history], [chatbot], show_progress=True)
        addfile_btn.upload(add_file, [chatbot, task_history, addfile_btn], [chatbot, task_history], show_progress=True)

        gr.Markdown("""\
<font size=2>Note: This demo is governed by the original license of Qwen-VL. \
We strongly advise users not to knowingly generate or allow others to knowingly generate harmful content, \
including hate speech, violence, pornography, deception, etc. \
(注：本演示受Qwen-VL的许可协议限制。我们强烈建议，用户不应传播及不应允许他人传播以下内容，\
包括但不限于仇恨言论、暴力、色情、欺诈相关的有害信息。)""")

    demo.queue().launch(
        share=args.share,
        inbrowser=args.inbrowser,
        server_port=args.server_port,
        server_name=args.server_name,
    )


def main():
    args = _get_args()

    model, tokenizer = _load_model_tokenizer(args)

    _launch_demo(args, model, tokenizer)


if __name__ == '__main__':
    main()