app/services/gemini.py

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import PromptTemplate
from app.core.config import settings
from app.schemas import MCQResponse


class GenerateAnswer:
    """
    Class to generate answers using Google Gemini API.
    """

    def __init__(self):
        self.llm = ChatGoogleGenerativeAI(
            model="gemini-2.0-flash",
            temperature=0.6,
            api_key=settings.GOOGLE_API_KEY,
        )

    async def generate_mcq(self, topic: str, solo_level: str):
        """
        Generate an answer to the given question using Google Gemini API.
        """
        prompt = PromptTemplate(
            template="""You are an AI tutor. Based on the SOLO taxonomy level and the content snippet provided, generate a single multiple-choice question (MCQ) that matches the SOLO level.

            Content Snippet:
            \"\"\"
            Photosynthesis is the process by which plants use sunlight, water, and carbon dioxide to create glucose and oxygen. Chlorophyll absorbs sunlight.
            \"\"\"


            SOLO Level: {solo_level}
            You should be based on this Topic: {topic}

            SOLO Level Consideration:
            - Unistructural: Focus on recalling a single piece of information from the content_snippet.
            - Multistructural: Focus on recalling several pieces of information from the content_snippet.

            Generate one MCQ with:
            - "question_text": A single question aligned to the SOLO level
            - "options": 3–4 plausible answer choices
            - "correct_answer": The correct answer (must match one of the options)""",
            input_variables=["topic", "solo_level"],
            )
        model=self.llm.with_structured_output(MCQResponse)
        chain=prompt | model
        response= await chain.ainvoke({"topic": topic, "solo_level": solo_level})
        return response