JobGPT / app.py
Aditya Patkar
Saving generation conversations to log file
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raw
history blame
14.9 kB
'''
This is the main file of the app. This file contains the code for the streamlit app.
'''
import time
import datetime
import base64
import streamlit as st
from streamlit_chat import message
from job_description_generator import predict_job_description, get_job_description_conversation
from job_description_fixer import fix_job_description, get_job_description_fixer_conversation
from interview_questions_generator import (predict_interview_question,
get_interview_questions_conversation)
from cover_letter_generator import get_cover_letter
from top_accomplishment_generator import get_accomplishments
from constants import PROMPT_VERSION
conversation = get_job_description_conversation()
if 'generator_conversation' not in st.session_state:
with open("./conversation.txt", "a", encoding='utf-8') as f:
#add a horizontal line
f.write("--------------------------------------------------\n")
#add the date
f.write(f"Conversation on {datetime.datetime.now()}, prompt_{PROMPT_VERSION}: \n\n")
st.session_state['generator_conversation'] = conversation
fixer_conversation = get_job_description_fixer_conversation()
if 'fixer_conversation' not in st.session_state:
st.session_state['fixer_conversation'] = fixer_conversation
st.session_state['response'] = {'history': [], 'prediction': ''}
interview_questions_conversation = get_interview_questions_conversation()
if 'interview_questions_conversation' not in st.session_state:
st.session_state['interview_questions_conversation'] = interview_questions_conversation
def get_downloadable_conversation(input_text, response):
'''
Downloads the conversation to a text file.
'''
conversation_to_save = f"Conversation with JobGPT on {datetime.datetime.now()}, prompt_{PROMPT_VERSION}: \n\n"
for historical_message in response['history']:
conversation_to_save = conversation_to_save + historical_message + "\n"
conversation_to_save = conversation_to_save + f"Human: {input_text} \n"
conversation_to_save = conversation_to_save + f"JobGPT: {response['prediction']} \n"
conversation_to_save = conversation_to_save + "----------------------------------------\n"
return conversation_to_save
def message_writer(input_text, response):
'''
Writes the messages to the chat window.
'''
messages = []
current_message = ""
current_is_user = True
for historical_message in response['history']:
if "human" in historical_message.lower():
messages.append([current_message, current_is_user])
current_message = historical_message.replace("Human:", "")
current_is_user = True
elif "JobGPT" in historical_message:
messages.append([current_message, current_is_user])
current_message = historical_message.replace("JobGPT:", "")
current_is_user = False
else:
current_message = current_message + "\n" + historical_message
messages.append([current_message, current_is_user])
for message_to_send, is_user in messages:
if message_to_send.strip() != "":
message(message_to_send, is_user=is_user)
message(input_text, is_user=True)
message(response['prediction'], is_user=False)
return 0
def setup():
"""
Streamlit related setup. This has to be run for each page.
"""
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
def main():
'''
Main function of the app.
'''
setup()
#create a sidebar where you can select your page
st.sidebar.title("JobGPT")
st.sidebar.markdown("---")
#selector
page = st.sidebar.selectbox(
"Select a page", ["Home",
"Job Description Generator",
"Job Description Fixer",
"Cover Letter Generator",
"Interview questions generator",
"Accomplishments Generator"])
if page == "Home":
st.title("JobGPT")
st.write("Select a page in the sidebar to get started.")
st.write("### Available options:")
st.write("1. Job Description Generator")
st.write("2. Job Description Fixer")
st.write("3. Cover Letter Generator")
st.write("4. Interview Questions Generator")
st.write("5. Accomplishments Generator")
st.markdown("---")
elif page == "Job Description Generator":
container_one = st.container()
container_one.title("A Job Description Generating Chatbot")
container_one.markdown(
"JobGPT is a chatbot that generates job descriptions. \
This is built just for demo purpose."
)
input_text = container_one.text_area(
"Prompt",
"Hi, can you please help me generate an unbiased job description?")
button = container_one.button("Send")
st.sidebar.markdown("---")
st.sidebar.markdown("Click on `new chat` to start a new chat. \
History will be cleared and you'll lose access to current chat."
)
clear_session = st.sidebar.button("New Chat")
if clear_session:
with open("./conversation.txt", "a", encoding='utf-8') as f:
#add a horizontal line
f.write("--------------------------------------------------\n")
#add the date
f.write(f"Conversation on {datetime.datetime.now()}, prompt_{PROMPT_VERSION}: \n\n")
st.session_state['generator_conversation'] = conversation
container_one.markdown("---")
initial_message = "Hello, how can I help you?"
message(initial_message)
#download_button = st.sidebar.button("Download Conversation")
if button:
response = predict_job_description(input_text,
st.session_state['generator_conversation'])
message_writer(input_text, response)
st.session_state['response'] = response
conversation_to_save = get_downloadable_conversation(
input_text, st.session_state['response'])
#write to conversation.txt
with open("./conversation.txt", "a", encoding='utf-8') as f:
f.write(f"HUMAN: {input_text}\n")
f.write(f"BOT: {response['prediction']}\n")
#download the conversation
b64 = base64.b64encode(conversation_to_save.encode()).decode()
href = f'<a href="data:file/txt;base64,{b64}" download="{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}-conversation.txt">Download conversation</a>'
st.sidebar.markdown(href, unsafe_allow_html=True)
elif page == "Job Description Fixer":
container_two = st.container()
container_two.title("A Job Description Fixing Chatbot")
container_two.markdown(
"JobGPT is a chatbot that fixes job descriptions. This is built just for demo purpose."
)
input_text = container_two.text_area(
"Prompt",
"Hi, can you please help me fix my job description? It's biased.")
button = container_two.button("Send")
st.sidebar.markdown("---")
st.sidebar.markdown("Click on `new chat` to start a new chat. \
History will be cleared and you'll lose access to current chat."
)
clear_session = st.sidebar.button("New Chat")
if clear_session:
st.session_state['fixer_conversation'] = fixer_conversation
container_two.markdown("---")
initial_message = "Hello, how can I help you?"
message(initial_message)
if button:
response = fix_job_description(
input_text, st.session_state['fixer_conversation'])
message_writer(input_text, response)
st.session_state['response'] = response
conversation_to_save = get_downloadable_conversation(
input_text, st.session_state['response'])
#download the conversation
b64 = base64.b64encode(conversation_to_save.encode()).decode()
href = f'<a href="data:file/txt;base64,{b64}" download="{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}-conversation.txt">Download conversation</a>'
st.sidebar.markdown(href, unsafe_allow_html=True)
elif page == "Cover Letter Generator":
container_three = st.container()
container_three.title("A Cover Letter Generating Chatbot")
container_three.markdown( "JobGPT is a chatbot that generates cover letters. \
This is built just for demo purpose.")
container_three.markdown("---")
uploaded_files = container_three.file_uploader("Upload your resume", type=["pdf", "txt", "docx"], accept_multiple_files=True)
if uploaded_files is not None and uploaded_files != []:
counter = 0
for uploaded_file in uploaded_files:
file_extension = uploaded_file.name.split(".")[-1]
with open(f"./documents/resume_{counter}.{file_extension}", "wb") as file_io:
file_io.write(uploaded_file.getbuffer())
counter += 1
with st.spinner('Uploading...'):
time.sleep(1)
container_three.success('Uploaded!')
container_three.markdown("---")
form = container_three.form(key='my_form')
title = form.text_input("Job Title (required)", placeholder="VP of Engineering")
company = form.text_input("Company Name (required)", placeholder="Google")
more_info = form.text_area("More Info",
help="Add more info about you or the job in natural language",
placeholder="I am a software engineer with 5 years of experience. The job focuses on building a new product in healthcare sector.")
submit_button = form.form_submit_button(label='Submit')
if submit_button:
if title == "":
st.error("Please enter a job title")
elif company == "":
st.error("Please enter a company name")
else:
with st.spinner('Generating...'):
cover_letter = get_cover_letter(title, company, more_info, "./documents")
container_three.markdown("---")
container_three.markdown("### Cover Letter:")
container_three.write(cover_letter)
elif page == "Accomplishments Generator":
container_three = st.container()
container_three.title("An Accomplishments Generating Chatbot")
container_three.markdown( "JobGPT is a chatbot that generates Accomplishments from resume. \
This is built just for demo purpose.")
container_three.markdown("---")
uploaded_file = container_three.file_uploader("Upload your resume", type=["pdf"])
if uploaded_file is not None:
with open("resume.pdf", "wb") as file_io:
file_io.write(uploaded_file.getbuffer())
with st.spinner('Uploading...'):
time.sleep(1)
container_three.success('Uploaded!')
container_three.markdown("---")
form = container_three.form(key='my_form')
title = form.text_input("Job Title (required)", placeholder="VP of Engineering")
company = form.text_input("Company Name (required)", placeholder="Google")
job_description = form.text_area("Job Description",
help="Paste the job description you are applying for",
placeholder="We are looking for a software engineer with 5 years of experience. The job focuses on building a new product in healthcare sector")
submit_button = form.form_submit_button(label='Submit')
if submit_button:
if title == "":
st.error("Please enter a job title")
elif company == "":
st.error("Please enter a company name")
elif job_description == "":
st.error("Please enter a job description")
else:
with st.spinner('Generating...'):
accomplishments = get_accomplishments(title, company, job_description, "resume.pdf")
container_three.markdown("---")
container_three.markdown("### Top Accomplishments for the given job:")
container_three.write(accomplishments)
elif page == "Interview questions generator":
container_two = st.container()
container_two.title("An Interview Questions Generating Chatbot")
container_two.markdown(
"JobGPT is a chatbot that generates interview questions.\
This is built just for demo purpose."
)
input_text = container_two.text_area(
"Prompt",
"Hi, can you please help me generate interview questions?")
button = container_two.button("Send")
st.sidebar.markdown("---")
st.sidebar.markdown("Click on `new chat` to start a new chat. \
History will be cleared and you'll lose access to current chat."
)
clear_session = st.sidebar.button("New Chat")
if clear_session:
st.session_state['interview_questions_conversation'] = interview_questions_conversation
container_two.markdown("---")
initial_message = "Hello, how can I help you?"
message(initial_message)
if button:
response = predict_interview_question(
input_text, st.session_state['interview_questions_conversation'])
message_writer(input_text, response)
st.session_state['response'] = response
conversation_to_save = get_downloadable_conversation(
input_text, st.session_state['response'])
#download the conversation
b64 = base64.b64encode(conversation_to_save.encode()).decode()
href = f'<a href="data:file/txt;base64,{b64}" download="{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}-conversation.txt">Download conversation</a>'
st.sidebar.markdown(href, unsafe_allow_html=True)
main()