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app.py

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+import gradio as gr 
+from transformers import AutoModelForCausalLM,AutoProcessor,Qwen2VLForConditionalGeneration
+from PIL import Image
+import os 
+import tempfile
+import torch
+from pathlib import Path
+import secrets
+
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
+)
+
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
+math_messages = []
+def process_image(image, shouldConvert=False):
+    global math_messages
+    math_messages = [] # reset when upload image
+    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.'}
+        ]
+    }]
+    
+    text_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+
+    inputs = processor(
+        text = [text_prompt],
+        images = [image],
+        padding = True,
+        return_tensors = "pt"
+    )
+
+
+    output_ids = model.generate(**inputs, max_new_tokens=1024)
+
+    generated_ids = [
+        output_ids[len(input_ids) :]
+        for input_ids, output_ids in zip(inputs.input_ids, output_ids)
+    ]
+
+    output_text = processor.batch_decode(
+        generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
+    )
+    
+    os.remove(filename)
+    
+    return output_text
+
+
+
+def get_math_response(image_description, user_question):
+    global math_messages
+    if not math_messages:
+        math_messages.append({'role': 'system', 'content': 'You are a helpful math assistant.'})
+    math_messages = math_messages[:1]
+    if image_description is not None:
+        content = f'Image description: {image_description}\n\n'
+    else:
+        content = ''
+    query = f"{content}User question: {user_question}"
+    math_messages.append({'role': 'user', 'content': query})
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    model_name = "Qwen/Qwen2-Math-72B-Instruct"
+    device = "cuda" # the device to load the model onto
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype="auto",
+        device_map="auto"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    text = tokenizer.apply_chat_template(
+        math_messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+    answer = None
+    for resp in response:
+        if resp.output is None:
+            continue
+        answer = resp.output.choices[0].message.content
+        yield answer.replace("\\", "\\\\")
+    print(f'query: {query}\nanswer: {answer}')
+    if answer is None:
+        math_messages.pop()
+    else:
+        math_messages.append({'role': 'assistant', 'content': answer})
+def math_chat_bot(image, sketchpad, question, state):
+    current_tab_index = state["tab_index"]
+    image_description = None
+    # Upload
+    if current_tab_index == 0:
+        if image is not None:
+            image_description = process_image(image)
+    # Sketch
+    elif current_tab_index == 1:
+        print(sketchpad)
+        if sketchpad and sketchpad["composite"]:
+            image_description = process_image(sketchpad["composite"], True)
+    yield from get_math_response(image_description, question)
+
+css = """
+#qwen-md .katex-display { display: inline; }
+#qwen-md .katex-display>.katex { display: inline; }
+#qwen-md .katex-display>.katex>.katex-html { display: inline; }
+"""
+
+def tabs_select(e: gr.SelectData, _state):
+    _state["tab_index"] = e.index
+
+
+# 创建Gradio接口
+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-Math Demo</center>"""
+            """\
+<center><font size=3>This WebUI is based on Qwen2-VL for OCR and Qwen2-Math for mathematical reasoning. You can input either images or texts of mathematical or arithmetic problems.</center>"""
+            )
+    state = gr.State({"tab_index": 0})
+    with gr.Row():
+        with gr.Column():
+            with gr.Tabs() as input_tabs:
+                with gr.Tab("Upload"):
+                    input_image = gr.Image(type="pil", label="Upload"),
+                with gr.Tab("Sketch"):
+                    input_sketchpad = gr.Sketchpad(type="pil", label="Sketch", layers=False)
+            input_tabs.select(fn=tabs_select, inputs=[state])
+            input_text = gr.Textbox(label="input your question")
+            with gr.Row():
+                with gr.Column():
+                    clear_btn = gr.ClearButton(
+                        [*input_image, input_sketchpad, input_text])
+                with gr.Column():
+                    submit_btn = gr.Button("Submit", variant="primary")
+        with gr.Column():
+            output_md = gr.Markdown(label="answer",
+                                    latex_delimiters=[{
+                                        "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
+                                    }],
+                                    elem_id="qwen-md")
+        submit_btn.click(
+            fn=math_chat_bot,
+            inputs=[*input_image, input_sketchpad, input_text, state],
+            outputs=output_md)
+demo.launch()

requirements.txt

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+gradio
+numpy==1.24.4
+Pillow==10.3.0
+Requests==2.31.0
+torch
+torchvision
+transformers==4.43.0
+accelerate==0.30.0
+qwen-vl-utils