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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 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 = 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("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 = ChatGoogleGenerativeAI(
model="gemini-pro")
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 = 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("Professional 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.")
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