app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from PIL import Image
import re 
import requests
from io import BytesIO
import copy
import secrets
from pathlib import Path

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-VL-Chat-Int4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-VL-Chat-Int4", device_map="auto", trust_remote_code=True).eval()

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

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 predict(_chatbot, task_history):
    chat_query = _chatbot[-1][0]
    query = task_history[-1][0]
    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("/tmp") / 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)
    return _chatbot
    
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 []
    
def regenerate(_chatbot, task_history):
    print("Regenerate clicked")
    print("Before:", task_history, _chatbot)
    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))
    print("After:", task_history, _chatbot)
    return predict(_chatbot, task_history)

css = '''
.gradio-container{max-width:800px !important}
'''

with gr.Blocks(css=css) as demo:
    gr.Markdown("# Qwen-VL-Chat Bot")
    gr.Markdown("## Qwen-VL: A Multimodal Large Vision Language Model by Alibaba Cloud **Space by [@Artificialguybr](https://twitter.com/artificialguybr). Test the [QwenLLM-14B](https://huggingface.co/spaces/artificialguybr/qwen-14b-chat-demo) here for free!</center>")
    chatbot = gr.Chatbot(label='Qwen-VL-Chat', elem_classes="control-height", height=520)
    query = gr.Textbox(lines=2, label='Input')
    task_history = gr.State([])

    with gr.Row():
        addfile_btn = gr.UploadButton("📁 Upload", file_types=["image"])
        submit_btn = gr.Button("🚀 Submit")
        regen_btn = gr.Button("🤔️ Regenerate")
        empty_bin = gr.Button("🧹 Clear History")
    
    gr.Markdown("### Key Features:\n- **Strong Performance**: Surpasses existing LVLMs on multiple English benchmarks including Zero-shot Captioning and VQA.\n- **Multi-lingual Support**: Supports English, Chinese, and multi-lingual conversation.\n- **High Resolution**: Utilizes 448*448 resolution for fine-grained recognition and understanding.")
    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)

demo.launch()