app.py

import gradio as gr
import os
import tempfile
from pathlib import Path
import secrets
import dashscope
from dashscope import MultiModalConversation, Generation
from PIL import Image

# API key setup
YOUR_API_TOKEN = os.getenv('YOUR_API_TOKEN')
dashscope.api_key = YOUR_API_TOKEN

# Global variables
math_messages = []
image_descriptions = []

def process_image(image, shouldConvert=False):
    uploaded_file_dir = os.environ.get("GRADIO_TEMP_DIR") or str(
        Path(tempfile.gettempdir()) / "gradio"
    )
    os.makedirs(uploaded_file_dir, exist_ok=True)
    
    name = f"tmp{secrets.token_hex(20)}.jpg"
    filename = os.path.join(uploaded_file_dir, name)
    
    if shouldConvert:
        new_img = Image.new('RGB', size=(image.width, image.height), color=(255, 255, 255))
        new_img.paste(image, (0, 0), mask=image)
        image = new_img
    image.save(filename)
    
    messages = [{
        'role': 'system',
        'content': [{'text': 'You are a helpful assistant.'}]
    }, {
        'role': 'user',
        'content': [
            {'image': f'file://{filename}'},
            {'text': 'Please describe the math-related content in this image, ensuring that any LaTeX formulas are correctly transcribed. Non-mathematical details do not need to be described.'}
        ]
    }]
    
    response = MultiModalConversation.call(model='qwen-vl-max-0809', messages=messages)
    
    os.remove(filename)
    
    return response.output.choices[0]["message"]["content"]

def get_math_response(image_descriptions, user_question):
    global math_messages
    if not math_messages:
        math_messages.append({'role': 'system', 'content': 'You are a helpful math assistant.'})
    
    content = "Image descriptions:\n" + "\n".join(image_descriptions) if image_descriptions else ""
    content += f"\n\nUser question: {user_question}"
    
    math_messages.append({'role': 'user', 'content': content})
    response = Generation.call(	
        model="qwen2.5-math-72b-instruct",
        messages=math_messages,	
        result_format='message',
        stream=True
    )
    answer = ""
    for resp in response:
        if resp.output is None:
            continue
        answer = resp.output.choices[0].message.content
        yield answer.replace("\\", "\\\\")
    
    math_messages.append({'role': 'assistant', 'content': answer})

def math_chat_bot(images, sketchpad, question, chat_history):
    global image_descriptions
    
    # Process new images
    for image in images:
        if image:
            description = process_image(image)
            image_descriptions.append(description)
    
    # Process sketchpad if present
    if sketchpad and sketchpad["composite"]:
        sketch_description = process_image(sketchpad["composite"], True)
        image_descriptions.append(sketch_description)
    
    # Generate response
    response = ""
    for chunk in get_math_response(image_descriptions, question):
        response += chunk
        yield chat_history + [(question, response)]

css = """
#qwen-md .katex-display { display: inline; }
#qwen-md .katex-display>.katex { display: inline; }
#qwen-md .katex-display>.katex>.katex-html { display: inline; }
"""

# Create Gradio interface
with gr.Blocks(css=css) as demo:
    gr.HTML("""\
<p align="center"><img src="https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png" style="height: 60px"/><p>"""
            """<center><font size=8>📖 Qwen2.5-Math Demo</center>"""
            """\
<center><font size=3>This WebUI is based on Qwen2-VL for OCR and Qwen2.5-Math for mathematical reasoning. You can input either images or texts of mathematical or arithmetic problems.</center>"""
            )
    
    with gr.Row():
        with gr.Column():
            input_images = gr.File(file_count="multiple", label="Upload Images")
            input_sketchpad = gr.Sketchpad(type="pil", label="Sketch", layers=False)
            input_text = gr.Textbox(label="Input your question")
            with gr.Row():
                clear_btn = gr.ClearButton([input_images, input_sketchpad, input_text])
                submit_btn = gr.Button("Submit", variant="primary")
        
        with gr.Column():
            chat_output = gr.Chatbot(label="Chat History", elem_id="qwen-md")
    
    submit_btn.click(
        fn=math_chat_bot,
        inputs=[input_images, input_sketchpad, input_text, chat_output],
        outputs=chat_output
    )

demo.launch()