from langchain_community.llms import OpenAI
from langchain_google_genai import ChatGoogleGenerativeAI
import streamlit as st



def get_answers(questions,model):
    st.write("running get answers function answering following questions",questions)


    answer_prompt = ( "I want you to become a teacher answer this specific Question: {questions}. You should gave me a straightforward and consise explanation and answer to each one of them")

    
    if model == "Open AI":
        llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"])
        answers = llm(answer_prompt)
        # return questions
        
    elif model == "Gemini":
        llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"])
        answers = llm.invoke(answer_prompt)
        answers = answers.content
        # return questions.content

    return(answers)    




def GetLLMResponse(selected_topic_level, selected_topic,num_quizzes, model):
    question_prompt = ('I want you to just generate question with this specification: Generate a {selected_topic_level} math quiz on the topic of {selected_topic}. Generate only {num_quizzes} questions not more and without providing answers.')
    
    st.write("running get llm response and print question prompt",question_prompt)
    if model == "Open AI":
        llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"])
        questions = llm(question_prompt)
        
        
    elif model == "Gemini":
        llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"])
        questions = llm.invoke(question_prompt)
        questions = questions.content
        # return questions.content


    st.write("print questions",questions)
    answers = get_answers(questions,model)
    
    st.write(questions,answers)
    return(questions,answers)