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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)
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