from openai import OpenAI
from pathlib import Path
from models.class_model import Classes
from models.employment import Employment
from models.student import Student
from models.class_teacher import ClassTeachers
from models.consultant import Consultant
from models.score import Score
from models.teacher import Teacher
from sqlalchemy import func

def llm_func(input_user):

    client = OpenAI(
    api_key="sk-2e7da09d2ef1432f96af95114824e6b5",
    base_url="https://api.deepseek.com"
    )

    prom_path = Path(__file__).parent / "prom.txt"
    context = prom_path.read_text(encoding="utf-8")



    # 2. 调用大模型

    response = client.chat.completions.create(
        model="deepseek-flash",
        messages=[
            {
                "role": "system",
                "content": context
            },
            {
                "role": "user",
                "content": input_user
            }
        ]
    )

    # 3. 获取AI回复
    code = response.choices[0].message.content
    print("====== LLM生成代码 ======")
    print(code)
    print("========================")

    env = {
        "Student": Student,
        "Classes": Classes,
        "Employment": Employment,
        "ClassTeachers": ClassTeachers,
        "Consultant": Consultant,
        "Score": Score,
        "Teacher": Teacher,
        "func": func
    }

    namespace={}
    exec(code, env,namespace)

    execute_func = namespace["execute"]
    return execute_func,code