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import streamlit as st
from groq import Groq
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
import os
from langchain_groq import ChatGroq
from secret_key import groq_api_key
import pandas as pd
from langchain.schema import (AIMessage, HumanMessage, SystemMessage)
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
from langchain.memory import ConversationBufferMemory
from langchain.memory import ConversationBufferWindowMemory
import time, random
from templates import choose_template, extract_template, interview_feedback_template, interview_question_template
from utils import select_questions
st.set_page_config(page_icon='rex.png', layout='wide')
st.title("Interview Round : Skill Up Your Interviewing Prowess")
category = st.selectbox("Which type of questions do you want to practice?",
['Technical', 'Behavioural', 'Culture Fit', 'STAR'], index=None)
while category is None:
st.info('Please select question category')
st.stop()
if category:
data = select_questions(category=category)
while category is None:
st.markdown('Please select question category')
if not st.session_state.groq_key:
st.info("Please add your API key to continue")
st.stop()
if not st.session_state["Resume Info"]:
st.info("Please upload your Resume")
st.stop()
if not st.session_state["Job Description"]:
st.info("Please add your job description")
st.stop()
os.environ['GROQ_API_KEY'] = st.session_state.groq_key
# Initialize Groq client with Mixtral model
client = ChatGroq(
groq_api_key=groq_api_key,
model_name="mixtral-8x7b-32768"
)
### Extract previously asked Questions from the history
memory = ConversationBufferMemory(
memory_key="history",
return_messages=True
)
system_template_e = extract_template
system_message_prompt_e = SystemMessagePromptTemplate.from_template(system_template_e)
human_template_e = "{text}"
human_message_prompt_e = HumanMessagePromptTemplate.from_template(human_template_e)
chat_prompt_e = ChatPromptTemplate.from_messages([system_message_prompt_e, human_message_prompt_e])
extract_chain = LLMChain(llm=client, prompt=chat_prompt_e)
### Choose question based on action
system_template_c = choose_template
system_message_prompt_c = SystemMessagePromptTemplate.from_template(system_template_c)
human_template_c = "{text}"
human_message_prompt_c = HumanMessagePromptTemplate.from_template(human_template_c)
chat_prompt_c = ChatPromptTemplate.from_messages([system_message_prompt_c, human_message_prompt_c])
choose_chain = LLMChain(llm=client, prompt=chat_prompt_c)
### Asking the questions
system_template_q = interview_question_template
system_message_prompt_q = SystemMessagePromptTemplate.from_template(system_template_q)
human_template_q = "{text}"
human_message_prompt_q = HumanMessagePromptTemplate.from_template(human_template_q)
chat_prompt_q = ChatPromptTemplate.from_messages([system_message_prompt_q, human_message_prompt_q])
question_chain = LLMChain(llm=client, prompt=chat_prompt_q)
### Provide Feedback
system_template_f = interview_feedback_template
system_message_prompt_f = SystemMessagePromptTemplate.from_template(system_template_f)
human_template_f = "{text}"
human_message_prompt_f = HumanMessagePromptTemplate.from_template(human_template_f)
chat_prompt_f = ChatPromptTemplate.from_messages([system_message_prompt_f, human_message_prompt_f])
feedback_chain = LLMChain(llm=client, prompt=chat_prompt_f)
if "messages" not in st.session_state:
st.session_state.messages = []
if "action" not in st.session_state:
st.session_state.action = "Next"
if "history" not in st.session_state:
st.session_state.history = []
if "questions" not in st.session_state:
st.session_state.questions = []
for message in st.session_state.messages:
if message['role'] == "user":
name = "user"
avatar = "user.png"
else:
name = "assistant"
avatar = "rex.png"
with st.chat_message(name, avatar=avatar):
st.markdown(f"{message['content']}")
if inp := st.chat_input("Type here"):
with st.chat_message("user", avatar='user.png'):
st.markdown(inp)
st.session_state['messages'].append({'role': 'user', 'content': inp})
question = None
if st.session_state.messages != [] and st.session_state.messages[-1]['role'] == "feedback":
option = st.radio(label="Which question would you like to do?", options=["Next", "Repeat"], index=None)
while option is None:
pass
st.session_state.action = option
if st.session_state.action == "Next" or "Repeat" and (
st.session_state.messages == [] or st.session_state.messages[-1]['role'] == "feedback"):
if st.session_state.questions != []:
extracts = extract_chain.run(history=st.session_state.questions, text="")
else:
extracts = "No previous Questions"
chosen_q = choose_chain.run(action=st.session_state.action, questions=extracts, data=data, text="",
details=st.session_state["Resume Info"], description=st.session_state['Job Description'])
response = question_chain.run(question=chosen_q, history=st.session_state.history[-1:], text=inp,
details=st.session_state["Resume Info"])
with st.chat_message("assistant", avatar='rex.png'):
message_placeholder = st.empty()
full_response = ""
for chunk in response.split():
full_response += chunk + " "
time.sleep(0.05)
# Add a blinking cursor to simulate typing
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
# st.markdown(response)
st.session_state.action = "Feedback"
st.session_state['messages'].append({'role': 'interviewer', 'content': response})
memory.save_context({"input": ""}, {"output": response})
st.session_state['history'].append(memory.buffer_as_messages[-2:])
st.session_state['questions'].append({'Question': response})
question = chosen_q
if st.session_state.messages[-1]['role'] == "user" and st.session_state.action == "Feedback":
feedback = feedback_chain.run(question=question, response=inp, history=st.session_state.history[-1:], text=inp,
asked=st.session_state.messages[-2]['content'])
with st.chat_message("assistant", avatar='rex.png'):
message_placeholder = st.empty()
full_response = ""
for chunk in feedback.split():
full_response += chunk + " "
time.sleep(0.05)
# Add a blinking cursor to simulate typing
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
# st.markdown(feedback)
st.session_state['messages'].append({'role': 'feedback', 'content': feedback})
memory.save_context({"input": inp}, {"output": feedback})
st.session_state['history'].append(memory.buffer_as_messages[-2:])
st.button("Continue")
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