Professional Screen.py

import streamlit as st
from streamlit_lottie import st_lottie
from typing import Literal
from dataclasses import dataclass
import json
import base64
from langchain.memory import ConversationBufferMemory
from langchain.callbacks import get_openai_callback
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain, RetrievalQA
from langchain.prompts.prompt import PromptTemplate
from langchain.text_splitter import NLTKTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
import nltk
from prompts.prompts import templates
# Audio
from speech_recognition.openai_whisper import save_wav_file, transcribe
from audio_recorder_streamlit import audio_recorder
from aws.synthesize_speech import synthesize_speech
from IPython.display import Audio


def load_lottiefile(filepath: str):
    with open(filepath, "r") as f:
        return json.load(f)
st_lottie(load_lottiefile("images/welcome.json"), speed=1, reverse=False, loop=True, quality="high", height=300)

#st.markdown("""solutions to potential errors:""")
with st.expander("""Why did I encounter errors when I tried to talk to the AI Interviewer?"""):
    st.write("""
    This is because the app failed to record. Make sure that your microphone is connected and that you have given permission to the browser to access your microphone.""")

jd = st.text_area("Please enter the job description here (If you don't have one, enter keywords, such as PostgreSQL or Python instead): ")
auto_play = st.checkbox("Let AI interviewer speak! (Please don't switch during the interview)")

#st.toast("4097 tokens is roughly equivalent to around 800 to 1000 words or 3 minutes of speech. Please keep your answer within this limit.")

@dataclass
class Message:
    """class for keeping track of interview history."""
    origin: Literal["human", "ai"]
    message: str

def save_vector(text):
    """embeddings"""

    nltk.download('punkt')
    text_splitter = NLTKTextSplitter()
    texts = text_splitter.split_text(text)
     # Create emebeddings
    embeddings = OpenAIEmbeddings()
    docsearch = FAISS.from_texts(texts, embeddings)
    return docsearch

def initialize_session_state_jd():
    """ initialize session states """
    if 'jd_docsearch' not in st.session_state:
        st.session_state.jd_docserch = save_vector(jd)
    if 'jd_retriever' not in st.session_state:
        st.session_state.jd_retriever = st.session_state.jd_docserch.as_retriever(search_type="similarity")
    if 'jd_chain_type_kwargs' not in st.session_state:
        Interview_Prompt = PromptTemplate(input_variables=["context", "question"],
                                          template=templates.jd_template)
        st.session_state.jd_chain_type_kwargs = {"prompt": Interview_Prompt}
    if 'jd_memory' not in st.session_state:
        st.session_state.jd_memory = ConversationBufferMemory()
    # interview history
    if "jd_history" not in st.session_state:
        st.session_state.jd_history = []
        st.session_state.jd_history.append(Message("ai",
                                                   "Hello, Welcome to the interview. I am your interviewer today. I will ask you professional questions regarding the job description you submitted."
                                                   "Please start by introducting a little bit about yourself. Note: The maximum length of your answer is 4097 tokens!"))
    # token count
    if "token_count" not in st.session_state:
        st.session_state.token_count = 0
    if "jd_guideline" not in st.session_state:
        llm = ChatOpenAI(
        model_name = "gpt-3.5-turbo",
        temperature = 0.8,)
        st.session_state.jd_guideline = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type_kwargs=st.session_state.jd_chain_type_kwargs, chain_type='stuff',
            retriever=st.session_state.jd_retriever, memory = st.session_state.jd_memory).run("Create an interview guideline and prepare only one questions for each topic. Make sure the questions tests the technical knowledge")
    # llm chain and memory
    if "jd_screen" not in st.session_state:
        llm = ChatOpenAI(
            model_name="gpt-3.5-turbo",
            temperature=0.8, )
        PROMPT = PromptTemplate(
            input_variables=["history", "input"],
            template="""I want you to act as an interviewer strictly following the guideline in the current conversation.
                            Candidate has no idea what the guideline is.
                            Ask me questions and wait for my answers. Do not write explanations.
                            Ask question like a real person, only one question at a time.
                            Do not ask the same question.
                            Do not repeat the question.
                            Do ask follow-up questions if necessary. 
                            You name is GPTInterviewer.
                            I want you to only reply as an interviewer.
                            Do not write all the conversation at once.
                            If there is an error, point it out.

                            Current Conversation:
                            {history}

                            Candidate: {input}
                            AI: """)

        st.session_state.jd_screen = ConversationChain(prompt=PROMPT, llm=llm,
                                                           memory=st.session_state.jd_memory)
    if 'jd_feedback' not in st.session_state:
        llm = ChatOpenAI(
            model_name="gpt-3.5-turbo",
            temperature=0.8, )
        st.session_state.jd_feedback = ConversationChain(
            prompt=PromptTemplate(input_variables=["history", "input"], template=templates.feedback_template),
            llm=llm,
            memory=st.session_state.jd_memory,
        )

def answer_call_back():
    with get_openai_callback() as cb:
        # user input
        human_answer = st.session_state.answer
        # transcribe audio
        if voice:
            save_wav_file("temp/audio.wav", human_answer)
            try:
                input = transcribe("temp/audio.wav")
                # save human_answer to history
            except:
                st.session_state.jd_history.append(Message("ai", "Sorry, I didn't get that."))
                return "Please try again."
        else:
            input = human_answer

        st.session_state.jd_history.append(
            Message("human", input)
        )
        # OpenAI answer and save to history
        llm_answer = st.session_state.jd_screen.run(input)
        # speech synthesis and speak out
        audio_file_path = synthesize_speech(llm_answer)
        # create audio widget with autoplay
        audio_widget = Audio(audio_file_path, autoplay=True)
        # save audio data to history
        st.session_state.jd_history.append(
            Message("ai", llm_answer)
        )
        st.session_state.token_count += cb.total_tokens
        return audio_widget

if jd:
    # initialize session states
    initialize_session_state_jd()
    #st.write(st.session_state.jd_guideline)
    credit_card_placeholder = st.empty()
    col1, col2 = st.columns(2)
    with col1:
        feedback = st.button("Get Interview Feedback")
    with col2:
        guideline = st.button("Show me interview guideline!")
    chat_placeholder = st.container()
    answer_placeholder = st.container()
    audio = None
    # if submit email adress, get interview feedback imediately
    if guideline:
        st.write(st.session_state.jd_guideline)
    if feedback:
        evaluation = st.session_state.jd_feedback.run("please give evalution regarding the interview")
        st.markdown(evaluation)
        st.download_button(label="Download Interview Feedback", data=evaluation, file_name="interview_feedback.txt")
        st.stop()
    else:
        with answer_placeholder:
            voice: bool = st.checkbox("I would like to speak with AI Interviewer")
            if voice:
                answer = audio_recorder(pause_threshold = 2.5, sample_rate = 44100)
                #st.warning("An UnboundLocalError will occur if the microphone fails to record.")
            else:
                answer = st.chat_input("Your answer")
            if answer:
                st.session_state['answer'] = answer
                audio = answer_call_back()
        with chat_placeholder:
            for answer in st.session_state.jd_history:
                if answer.origin == 'ai':
                    if auto_play and audio:
                        with st.chat_message("assistant"):
                            st.write(answer.message)
                            st.write(audio)
                    else:
                        with st.chat_message("assistant"):
                            st.write(answer.message)
                else:
                    with st.chat_message("user"):
                        st.write(answer.message)

        credit_card_placeholder.caption(f"""
        Progress: {int(len(st.session_state.jd_history) / 30 * 100)}% completed.""")
else:
    st.info("Please submit a job description to start the interview.")