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from openai import OpenAI
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import os
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# 初始化OpenAI客户端
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client = OpenAI(
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# 如果没有配置环境变量，请用百炼API Key替换：api_key="sk-xxx"
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api_key="sk-01d13a39e09844038322108ecdbd1bbc",
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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reasoning_content = "" # 定义完整思考过程
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answer_content = "" # 定义完整回复
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is_answering = False # 判断是否结束思考过程并开始回复
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prompt = "1、现在需要解答第1张图片的第4问。第2张图片的内容是此题的答案。\n"
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prompt=prompt+"2、限定在初中数学知识范围内解决问题，即不用使用建系，向量等高级数学方法，只限使用三角形相似，勾股定理，全等三角形，平面几何知识等。\n"
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prompt = prompt + "3、要求：整理提示词，即：如果不提供答案，该给你提供什么样的提示词，总结题目特点，相关解题思路。可以让你向正确的思路上快速推进，而不是乱试各种方法，最终还得不出正确答案。"
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# 创建聊天完成请求
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completion = client.chat.completions.create(
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model="qvq-max", # 此处以 qvq-max 为例，可按需更换模型名称
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {
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"url": "https://dsideal.obs.cn-north-1.myhuaweicloud.com/wb/math.jpg"
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},
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},
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{
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"type": "image_url",
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"image_url": {
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"url": "https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/Backup/answer.jpg"
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},
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},
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{"type": "text",
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"text": prompt},
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],
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},
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],
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stream=True,
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)
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print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
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for chunk in completion:
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# 如果chunk.choices为空，则打印usage
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if not chunk.choices:
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print("\nUsage:")
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print(chunk.usage)
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else:
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delta = chunk.choices[0].delta
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# 打印思考过程
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if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
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print(delta.reasoning_content, end='', flush=True)
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reasoning_content += delta.reasoning_content
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else:
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# 开始回复
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if delta.content != "" and is_answering is False:
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print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
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is_answering = True
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# 打印回复过程
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print(delta.content, end='', flush=True)
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answer_content += delta.content
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# print("=" * 20 + "完整思考过程" + "=" * 20 + "\n")
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# print(reasoning_content)
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# print("=" * 20 + "完整回复" + "=" * 20 + "\n")
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# print(answer_content)
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