63 lines
1.4 KiB
Python
63 lines
1.4 KiB
Python
from openai import OpenAI
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from pathlib import Path
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from models.class_model import Classes
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from models.employment import Employment
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from models.student import Student
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from models.class_teacher import ClassTeachers
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from models.consultant import Consultant
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from models.score import Score
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from models.teacher import Teacher
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from sqlalchemy import func
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def llm_func(input_user):
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client = OpenAI(
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api_key="sk-2e7da09d2ef1432f96af95114824e6b5",
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base_url="https://api.deepseek.com"
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)
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prom_path = Path(__file__).parent / "prom.txt"
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context = prom_path.read_text(encoding="utf-8")
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# 2. 调用大模型
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response = client.chat.completions.create(
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model="deepseek-flash",
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messages=[
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{
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"role": "system",
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"content": context
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},
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{
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"role": "user",
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"content": input_user
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}
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]
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)
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# 3. 获取AI回复
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code = response.choices[0].message.content
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print("====== LLM生成代码 ======")
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print(code)
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print("========================")
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env = {
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"Student": Student,
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"Classes": Classes,
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"Employment": Employment,
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"ClassTeachers": ClassTeachers,
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"Consultant": Consultant,
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"Score": Score,
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"Teacher": Teacher,
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"func": func
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}
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namespace={}
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exec(code, env,namespace)
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execute_func = namespace["execute"]
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return execute_func,code
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