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import streamlit as st
from langchain.embeddings import OpenAIEmbeddings
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 ChatOpenAI
from PyPDF2 import PdfReader
from prompts.prompt_selector import prompt_sector
def embedding(text):
"""embeddings"""
text_splitter = NLTKTextSplitter()
texts = text_splitter.split_text(text)
# Create emebeddings
embeddings = OpenAIEmbeddings()
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))
#if 'retriever' not in st.session_state:
st.session_state.retriever = st.session_state.docsearch.as_retriever(search_type="similarity")
#if 'chain_type_kwargs' not in st.session_state:
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)
#if 'memory' not in st.session_state:
st.session_state.memory = ConversationBufferMemory()
# interview history
#if "history" not in st.session_state:
st.session_state.history = []
# token count
#if "token_count" not in st.session_state:
st.session_state.token_count = 0
#if "guideline" not in st.session_state:
llm = ChatOpenAI(
model_name="gpt-3.5-turbo",
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 questions for each topic. Make sure the questions tests the technical knowledge")
# llm chain and memory
#if "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.
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)
#if "feedback" not in st.session_state:
llm = ChatOpenAI(
model_name = "gpt-3.5-turbo",
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,
) |