64 lines
2.5 KiB
Python
64 lines
2.5 KiB
Python
import re
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_openai import ChatOpenAI
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from loguru import logger
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import traceback
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from config.config import load_config
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class_type_config =[
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"本期开展了小袋鼠整合主题课程：(语言、社会、科学、健康、艺术）、生活数学；特色课程（英语、体能、美工、篮球）。",
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"本学期开展了柏克莱主题课程（语言、社会、科学、艺术、健康）；英语及特色课程（体能、舞蹈、美工、魔力猴、足球、国学）。",
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"本学期开展了双木桥主题课程（图说汉字、妙趣汉音、情智阅读、麦斯思维、专注力训练）；英语及特色课程（体能、舞蹈、美工、魔力猴、足球、国学）。"
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]
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def generate_comment(name, age_group, traits,sex):
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"""
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生成评语
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:param name: 学生姓名
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:param age_group: 所在班级
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:param traits: 表现特征
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:param sex: 性别
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:return: 评语
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"""
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# 1. 加载配置文件
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try:
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config = load_config("config.toml")
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except Exception as e:
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logger.error(f"配置文件获取失败: {str(e)}")
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# 打印详细报错位置，方便调试
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logger.error(traceback.format_exc())
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return "配置文件加载失败，请检查文件路径和内容。"
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ai_config = config["ai"]
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llm = ChatOpenAI(
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base_url=ai_config["api_url"],
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api_key=ai_config["api_key"],
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model=ai_config["model"],
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temperature=0.7,
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)
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# 2. 构建 Prompt Template
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prompt = ChatPromptTemplate.from_messages([
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("system", ai_config["prompt"]),
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("human", "学生姓名：{name}\n所在班级：{age_group}\n性别：{sex}\n表现特征：{traits}\n\n请开始撰写评语：")
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])
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# 3. 组装链 (Prompt -> Model -> OutputParser)
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chain = prompt | llm | StrOutputParser()
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# 4. 执行
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try:
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comment = chain.invoke({
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"name": name,
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"age_group": age_group,
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"traits": traits,
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"sex": sex,
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"class_type": class_type_config[(config.get("class_type", 0))],
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})
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cleaned_text = re.sub(r'\s+', '', comment)
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logger.success(f"学生：{name} =>生成评语成功: {cleaned_text}")
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return cleaned_text
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except Exception as e:
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logger.error(f"生成评语失败: {e}")
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return "生成失败，请检查网络或Key。"
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