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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"
def load_lottiefile(filepath: str):
'''Load lottie animation file'''
with open(filepath, "r") as f:
return json.load(f)
def autoplay_audio(file_path: str):
'''Play audio automatically'''
def update_audio():
global global_audio_md
with open(file_path, "rb") as f:
data = f.read()
b64 = base64.b64encode(data).decode()
global_audio_md = f"""
<audio controls autoplay="true">
<source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
</audio>
"""
def update_markdown(audio_md):
st.markdown(audio_md, unsafe_allow_html=True)
update_audio()
update_markdown(global_audio_md)
def embeddings(text: str):
'''Create embeddings for the job description'''
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)
retriever = docsearch.as_retriever(search_tupe='similarity search')
return retriever
def initialize_session_state(jd):
'''Initialize session state variables'''
if "retriever" not in st.session_state:
st.session_state.retriever = embeddings(jd)
if "chain_type_kwargs" not in st.session_state:
Behavioral_Prompt = PromptTemplate(input_variables=["context", "question"],
template=templates.behavioral_template)
st.session_state.chain_type_kwargs = {"prompt": Behavioral_Prompt}
# interview history
if "history" not in st.session_state:
st.session_state.history = []
st.session_state.history.append(Message("ai", "Hello there! I am your interviewer today. I will access your soft skills through a series of questions. Let's get started! Please start by saying hello or introducing 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 "memory" not in st.session_state:
st.session_state.memory = ConversationBufferMemory()
if "guideline" not in st.session_state:
llm = ChatGoogleGenerativeAI(
model="gemini-pro")
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 total of 8 questions. Make sure the questions tests the soft skills")
# llm chain and memory
if "conversation" 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.conversation = ConversationChain(prompt=PROMPT, llm=llm,
memory=st.session_state.memory)
if "feedback" not in st.session_state:
llm = ChatGoogleGenerativeAI(
model="gemini-pro")
st.session_state.feedback = ConversationChain(
prompt=PromptTemplate(input_variables = ["history", "input"], template = templates.feedback_template),
llm=llm,
memory = st.session_state.memory,
)
def answer_call_back():
'''callback function for answering user input'''
# user input
human_answer = st.session_state.answer
st.session_state.history.append(
Message("human", human_answer)
)
# OpenAI answer and save to history
llm_answer = st.session_state.conversation.run(human_answer)
st.session_state.history.append(
Message("ai", llm_answer)
)
st.session_state.token_count += len(llm_answer.split())
return llm_answer
@dataclass
class Message:
'''dataclass for keeping track of the messages'''
origin: Literal["human", "ai"]
message: str
def app():
st.title("Behavioral Screen")
st.markdown("""\n""")
with open('job_description.json', 'r') as f:
jd = json.load(f)
### ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
if jd:
initialize_session_state(jd)
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:
myresposes = st.button("Show my responses")
audio = None
chat_placeholder = st.container()
answer_placeholder = st.container()
if guideline:
st.write(st.session_state.guideline)
if feedback:
evaluation = st.session_state.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 myresposes:
with st.container():
st.write("### My Interview Responses")
for idx, message in enumerate(st.session_state.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")
#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.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.history) / 50 * 100)}% completed.
""")
else:
st.info("Please submit job description to start interview.")
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