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from langchain_community.llms import OpenAI |
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from langchain_google_genai import ChatGoogleGenerativeAI |
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import streamlit as st |
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def get_answers(questions,model): |
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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") |
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if model == "Open AI": |
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llm = OpenAI(temperature=0.8, openai_api_key=st.secrets["OPENAI_API_KEY"]) |
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answers = llm(answer_prompt) |
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elif model == "Gemini": |
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llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"]) |
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answers = llm.invoke(answer_prompt) |
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answers = answers.content |
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return(answers) |
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def GetLLMResponse(selected_topic_level, selected_topic, num_quizzes, selected_Question_Difficulty, selected_level, model): |
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for i in range(num_quizzes): |
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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.') |
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if model == "Open AI": |
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llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"]) |
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questions = llm(question_prompt) |
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elif model == "Gemini": |
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llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"]) |
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questions = llm.invoke(question_prompt) |
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questions = questions.content |
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answers = get_answers(questions,model) |
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return(questions,answers) |
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