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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):
answer_prompt = (f"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, selected_Question_Difficulty, selected_level, model):
for i in range(num_quizzes):
question_prompt = (f'You are an AI interview assistant that helps generate customized interview questions for various technical and non-technical roles. Your task is to create a set of interview questions based on the {selected_topic_level} and topic : {selected_topic}.Ensure the questions match the indicated level of understanding:{selected_level} and difficulty:{selected_Question_Difficulty}. Generate only 1 question.')
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
# answers = "testing"
answers = get_answers(questions,model)
return(questions,answers)
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