initialization.py

import streamlit as st
from langchain.embeddings import OllamaEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import NLTKTextSplitter
from langchain.memory import ConversationBufferMemory
from langchain.chains import RetrievalQA, ConversationChain
from prompts.prompts import templates
from langchain.prompts.prompt import PromptTemplate
from langchain.chat_models import ChatOllama
from PyPDF2 import PdfReader
from prompts.prompt_selector import prompt_sector

def embedding(text):
    """Generate embeddings"""
    text_splitter = NLTKTextSplitter()
    texts = text_splitter.split_text(text)
    embeddings = OllamaEmbeddings()
    docsearch = FAISS.from_texts(texts, embeddings)
    return docsearch

def resume_reader(resume):
    pdf_reader = PdfReader(resume)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()
    return text

def initialize_session_state(template=None, position=None):
    """Initialize session states"""
    if 'jd' in st.session_state:
        st.session_state.docsearch = embedding(st.session_state.jd)
    else:
        st.session_state.docsearch = embedding(resume_reader(st.session_state.resume))

    st.session_state.retriever = st.session_state.docsearch.as_retriever(search_type="similarity")

    if 'jd' in st.session_state:
        Interview_Prompt = PromptTemplate(input_variables=["context", "question"], template=template)
        st.session_state.chain_type_kwargs = {"prompt": Interview_Prompt}
    else:
        st.session_state.chain_type_kwargs = prompt_sector(position, templates)

    st.session_state.memory = ConversationBufferMemory()
    st.session_state.history = []
    st.session_state.token_count = 0

    llm = ChatOllama(model_name="ollama-3b", temperature=0.6)
    st.session_state.guideline = RetrievalQA.from_chain_type(llm=llm,
        chain_type_kwargs=st.session_state.chain_type_kwargs, chain_type='stuff',
        retriever=st.session_state.retriever, memory=st.session_state.memory).run(
        "Create an interview guideline and prepare only one question for each topic.")

    llm = ChatOllama(model_name="ollama-13b", 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.
                            Ask me questions and wait for my answers like a real person.
                            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.screen = ConversationChain(prompt=PROMPT, llm=llm, memory=st.session_state.memory)

    llm = ChatOllama(model_name="ollama-3b", temperature=0.5)
    st.session_state.feedback = ConversationChain(
        prompt=PromptTemplate(input_variables=["history", "input"], template=templates.feedback_template),
        llm=llm,
        memory=st.session_state.memory,
    )