technicalScreen.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.chains import ConversationChain, RetrievalQA
from langchain.prompts.prompt import PromptTemplate
from langchain.text_splitter import NLTKTextSplitter
from langchain.vectorstores import FAISS
import nltk
from prompts.prompts import templates
from langchain_google_genai import ChatGoogleGenerativeAI
import getpass
import os
from langchain_google_genai import GoogleGenerativeAIEmbeddings


if "GOOGLE_API_KEY" not in os.environ:
    os.environ["GOOGLE_API_KEY"] = "AIzaSyD-61G3GhSY97O-X2AlpXGv1MYBBMRFmwg"

@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 = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
        docsearch = FAISS.from_texts(texts, embeddings)
        return docsearch

def initialize_session_state_jd(jd):
        """ initialize session states """
        if "user_responses" not in st.session_state:
            st.session_state.user_responses = [] 
        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 Technical 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 = ChatGoogleGenerativeAI(
            model="gemini-pro")
            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(f"Create a list of DSA interview questions that comprehensively test the technical knowledge of candidates.")
        if "jd_screen" not in st.session_state:
            llm = ChatGoogleGenerativeAI(
            model="gemini-pro")
            PROMPT = PromptTemplate(
    input_variables=["history", "input"],
    template="""I want you to act as a technical interviewer, strictly following the guideline in the current conversation.
    Candidate has no idea what the guideline is.
    Ask me technical questions related to {job_role}, including Data Structures and Algorithms (DSA), conceptual questions related to {job_role}, and role-specific questions. Wait for my answers after each question. Do not write explanations.
    Ask questions like a real technical interviewer, focusing on one concept at a time.
    Do not ask the same question repeatedly.
    Do not repeat the question verbatim.
    Ask follow-up questions if necessary to clarify or probe deeper into the candidate's understanding.
    You are the Technical Interviewer.
    Respond only as a technical interviewer.
    Do not write the entire conversation at once.
    If there is an error in my response, point it out.

    Current Conversation:
    {history}

    Candidate: {input}
    Technical Interviewer: """)


            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 = ChatGoogleGenerativeAI(
            model="gemini-pro")
            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():
        formatted_history = []
        for message in st.session_state.jd_history:
            if message.origin == "human":
                formatted_message = {"speaker": "user", "text": message.message}
            else:
                formatted_message = {"speaker": "assistant", "text": message.message}
            formatted_history.append(formatted_message)

        user_answer = st.session_state.get('answer', '')

        answer = st.session_state.jd_screen.run(input=user_answer, history=formatted_history)

        if user_answer:  
            st.session_state.jd_history.append(Message("human", user_answer))
            if st.session_state.jd_history and len(st.session_state.jd_history) > 1:
                last_question = st.session_state.jd_history[-2].message  # Assuming the last message before the user's answer is the question
                st.session_state.user_responses.append({"question": last_question, "answer": user_answer})

        if answer:
            st.session_state.jd_history.append(Message("ai", answer))

        return answer

def app():
    st.title("Technical Screen")


    with open('job_description.json', 'r') as f:
        jd = json.load(f)

    

    if jd:
        # initialize session states
        initialize_session_state_jd(jd)
        #st.write(st.session_state.jd_guideline)
        credit_card_placeholder = st.empty()
        col1, col2, col3 = st.columns(3)
        with col1:
            feedback = st.button("Get Interview Feedback")
        with col2:
            guideline = st.button("Show me interview guideline!")
        with col3:
            myresponse = st.button("Show my responses")
        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()
        if myresponse:
            with st.container():
                st.write("### My Interview Responses")
                for idx, message in enumerate(st.session_state.jd_history):  # Corrected from history to jd_history
                    if message.origin == "ai":
                        st.write(f"**Question {idx//2 + 1}:** {message.message}")
                    else:
                        st.write(f"**My Answer:** {message.message}\n")

        else:
            with answer_placeholder:
                voice = 0
                if voice:
                    print(voice)               
                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 audio:
                            with st.chat_message("assistant"):
                                st.write(answer.message)
                        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) / 50 * 100)}% completed.""")
    else:
        st.info("Please submit a job description to start the interview.")