Spaces:
Sleeping
Sleeping
File size: 1,491 Bytes
0d09775 cfd5951 0d09775 ff6158d 0d09775 ff6158d 0d09775 ff6158d 0d09775 ff6158d 0d09775 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
from langchain_community.llms import OpenAI
from langchain_google_genai import ChatGoogleGenerativeAI
import streamlit as st
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.')
if model == "Open AI":
llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"])
questions = [llm(question_prompt)]
# return questions
elif model == "Gemini":
llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"])
questions = [llm.invoke(question_prompt)]
# return questions.content
answer_prompt = ( "I want you to become a teacher answer this specific Question:\n {questions}\n\n. 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)]
# return questions.content
return(questions,answers)
|