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| import time | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| from streamlit_option_menu import option_menu | |
| from streamlit_extras.add_vertical_space import add_vertical_space | |
| from PyPDF2 import PdfReader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.chains.question_answering import load_qa_chain | |
| from selenium import webdriver | |
| from selenium.webdriver.common.by import By | |
| from selenium.webdriver.common.keys import Keys | |
| from selenium.common.exceptions import NoSuchElementException | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| def streamlit_config(): | |
| # page configuration | |
| st.set_page_config(page_title='Resume Analyzer AI', layout="wide") | |
| # page header transparent color | |
| page_background_color = """ | |
| <style> | |
| [data-testid="stHeader"] | |
| { | |
| background: rgba(0,0,0,0); | |
| } | |
| </style> | |
| """ | |
| st.markdown(page_background_color, unsafe_allow_html=True) | |
| # title and position | |
| st.markdown(f'<h1 style="text-align: center;">AI-Powered Resume Analyzer and <br> LinkedIn Scraper with Selenium</h1>', | |
| unsafe_allow_html=True) | |
| class resume_analyzer: | |
| def pdf_to_chunks(pdf): | |
| # read pdf and it returns memory address | |
| pdf_reader = PdfReader(pdf) | |
| # extrat text from each page separately | |
| text = "" | |
| for page in pdf_reader.pages: | |
| text += page.extract_text() | |
| # Split the long text into small chunks. | |
| text_splitter = RecursiveCharacterTextSplitter( | |
| chunk_size=700, | |
| chunk_overlap=200, | |
| length_function=len) | |
| chunks = text_splitter.split_text(text=text) | |
| return chunks | |
| def openai(openai_api_key, chunks, analyze): | |
| # Using OpenAI service for embedding | |
| embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) | |
| # Facebook AI Similarity Serach library help us to convert text data to numerical vector | |
| vectorstores = FAISS.from_texts(chunks, embedding=embeddings) | |
| # compares the query and chunks, enabling the selection of the top 'K' most similar chunks based on their similarity scores. | |
| docs = vectorstores.similarity_search(query=analyze, k=3) | |
| # creates an OpenAI object, using the ChatGPT 3.5 Turbo model | |
| llm = ChatOpenAI(model='gpt-3.5-turbo', api_key=openai_api_key) | |
| # question-answering (QA) pipeline, making use of the load_qa_chain function | |
| chain = load_qa_chain(llm=llm, chain_type='stuff') | |
| response = chain.run(input_documents=docs, question=analyze) | |
| return response | |
| def summary_prompt(query_with_chunks): | |
| query = f''' need to detailed summarization of below resume and finally conclude them | |
| """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | |
| {query_with_chunks} | |
| """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | |
| ''' | |
| return query | |
| def resume_summary(): | |
| with st.form(key='Summary'): | |
| # User Upload the Resume | |
| add_vertical_space(1) | |
| pdf = st.file_uploader(label='Upload Your Resume', type='pdf') | |
| add_vertical_space(1) | |
| # Enter OpenAI API Key | |
| col1,col2 = st.columns([0.6,0.4]) | |
| with col1: | |
| openai_api_key = st.text_input(label='Enter OpenAI API Key', type='password') | |
| add_vertical_space(2) | |
| # Click on Submit Button | |
| submit = st.form_submit_button(label='Submit') | |
| add_vertical_space(1) | |
| add_vertical_space(3) | |
| if submit: | |
| if pdf is not None and openai_api_key != '': | |
| try: | |
| with st.spinner('Processing...'): | |
| pdf_chunks = resume_analyzer.pdf_to_chunks(pdf) | |
| summary_prompt = resume_analyzer.summary_prompt(query_with_chunks=pdf_chunks) | |
| summary = resume_analyzer.openai(openai_api_key=openai_api_key, chunks=pdf_chunks, analyze=summary_prompt) | |
| st.markdown(f'<h4 style="color: orange;">Summary:</h4>', unsafe_allow_html=True) | |
| st.write(summary) | |
| except Exception as e: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">{e}</h5>', unsafe_allow_html=True) | |
| elif pdf is None: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Please Upload Your Resume</h5>', unsafe_allow_html=True) | |
| elif openai_api_key == '': | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Please Enter OpenAI API Key</h5>', unsafe_allow_html=True) | |
| def strength_prompt(query_with_chunks): | |
| query = f'''need to detailed analysis and explain of the strength of below resume and finally conclude them | |
| """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | |
| {query_with_chunks} | |
| """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | |
| ''' | |
| return query | |
| def resume_strength(): | |
| with st.form(key='Strength'): | |
| # User Upload the Resume | |
| add_vertical_space(1) | |
| pdf = st.file_uploader(label='Upload Your Resume', type='pdf') | |
| add_vertical_space(1) | |
| # Enter OpenAI API Key | |
| col1,col2 = st.columns([0.6,0.4]) | |
| with col1: | |
| openai_api_key = st.text_input(label='Enter OpenAI API Key', type='password') | |
| add_vertical_space(2) | |
| # Click on Submit Button | |
| submit = st.form_submit_button(label='Submit') | |
| add_vertical_space(1) | |
| add_vertical_space(3) | |
| if submit: | |
| if pdf is not None and openai_api_key != '': | |
| try: | |
| with st.spinner('Processing...'): | |
| pdf_chunks = resume_analyzer.pdf_to_chunks(pdf) | |
| summary_prompt = resume_analyzer.summary_prompt(query_with_chunks=pdf_chunks) | |
| summary = resume_analyzer.openai(openai_api_key=openai_api_key, chunks=pdf_chunks, analyze=summary_prompt) | |
| strength_prompt = resume_analyzer.strength_prompt(query_with_chunks=summary) | |
| strength = resume_analyzer.openai(openai_api_key=openai_api_key, chunks=pdf_chunks, analyze=strength_prompt) | |
| st.markdown(f'<h4 style="color: orange;">Strength:</h4>', unsafe_allow_html=True) | |
| st.write(strength) | |
| except Exception as e: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">{e}</h5>', unsafe_allow_html=True) | |
| elif pdf is None: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Please Upload Your Resume</h5>', unsafe_allow_html=True) | |
| elif openai_api_key == '': | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Please Enter OpenAI API Key</h5>', unsafe_allow_html=True) | |
| def weakness_prompt(query_with_chunks): | |
| query = f'''need to detailed analysis and explain of the weakness of below resume and how to improve make a better resume. | |
| """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | |
| {query_with_chunks} | |
| """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | |
| ''' | |
| return query | |
| def resume_weakness(): | |
| with st.form(key='Weakness'): | |
| # User Upload the Resume | |
| add_vertical_space(1) | |
| pdf = st.file_uploader(label='Upload Your Resume', type='pdf') | |
| add_vertical_space(1) | |
| # Enter OpenAI API Key | |
| col1,col2 = st.columns([0.6,0.4]) | |
| with col1: | |
| openai_api_key = st.text_input(label='Enter OpenAI API Key', type='password') | |
| add_vertical_space(2) | |
| # Click on Submit Button | |
| submit = st.form_submit_button(label='Submit') | |
| add_vertical_space(1) | |
| add_vertical_space(3) | |
| if submit: | |
| if pdf is not None and openai_api_key != '': | |
| try: | |
| with st.spinner('Processing...'): | |
| pdf_chunks = resume_analyzer.pdf_to_chunks(pdf) | |
| summary_prompt = resume_analyzer.summary_prompt(query_with_chunks=pdf_chunks) | |
| summary = resume_analyzer.openai(openai_api_key=openai_api_key, chunks=pdf_chunks, analyze=summary_prompt) | |
| weakness_prompt = resume_analyzer.weakness_prompt(query_with_chunks=summary) | |
| weakness = resume_analyzer.openai(openai_api_key=openai_api_key, chunks=pdf_chunks, analyze=weakness_prompt) | |
| st.markdown(f'<h4 style="color: orange;">Weakness and Suggestions:</h4>', unsafe_allow_html=True) | |
| st.write(weakness) | |
| except Exception as e: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">{e}</h5>', unsafe_allow_html=True) | |
| elif pdf is None: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Please Upload Your Resume</h5>', unsafe_allow_html=True) | |
| elif openai_api_key == '': | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Please Enter OpenAI API Key</h5>', unsafe_allow_html=True) | |
| def job_title_prompt(query_with_chunks): | |
| query = f''' what are the job roles i apply to likedin based on below? | |
| """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | |
| {query_with_chunks} | |
| """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | |
| ''' | |
| return query | |
| def job_title_suggestion(): | |
| with st.form(key='Job Titles'): | |
| # User Upload the Resume | |
| add_vertical_space(1) | |
| pdf = st.file_uploader(label='Upload Your Resume', type='pdf') | |
| add_vertical_space(1) | |
| # Enter OpenAI API Key | |
| col1,col2 = st.columns([0.6,0.4]) | |
| with col1: | |
| openai_api_key = st.text_input(label='Enter OpenAI API Key', type='password') | |
| add_vertical_space(2) | |
| # Click on Submit Button | |
| submit = st.form_submit_button(label='Submit') | |
| add_vertical_space(1) | |
| add_vertical_space(3) | |
| if submit: | |
| if pdf is not None and openai_api_key != '': | |
| try: | |
| with st.spinner('Processing...'): | |
| pdf_chunks = resume_analyzer.pdf_to_chunks(pdf) | |
| summary_prompt = resume_analyzer.summary_prompt(query_with_chunks=pdf_chunks) | |
| summary = resume_analyzer.openai(openai_api_key=openai_api_key, chunks=pdf_chunks, analyze=summary_prompt) | |
| job_title_prompt = resume_analyzer.job_title_prompt(query_with_chunks=summary) | |
| job_title = resume_analyzer.openai(openai_api_key=openai_api_key, chunks=pdf_chunks, analyze=job_title_prompt) | |
| st.markdown(f'<h4 style="color: orange;">Job Titles:</h4>', unsafe_allow_html=True) | |
| st.write(job_title) | |
| except Exception as e: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">{e}</h5>', unsafe_allow_html=True) | |
| elif pdf is None: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Please Upload Your Resume</h5>', unsafe_allow_html=True) | |
| elif openai_api_key == '': | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Please Enter OpenAI API Key</h5>', unsafe_allow_html=True) | |
| class linkedin_scraper: | |
| def webdriver_setup(): | |
| options = webdriver.ChromeOptions() | |
| options.add_argument('--headless') | |
| options.add_argument('--no-sandbox') | |
| options.add_argument('--disable-dev-shm-usage') | |
| driver = webdriver.Chrome(options=options) | |
| driver.maximize_window() | |
| return driver | |
| def get_userinput(): | |
| add_vertical_space(2) | |
| with st.form(key='linkedin_scarp'): | |
| add_vertical_space(1) | |
| col1,col2,col3 = st.columns([0.5,0.3,0.2], gap='medium') | |
| with col1: | |
| job_title_input = st.text_input(label='Job Title') | |
| job_title_input = job_title_input.split(',') | |
| with col2: | |
| job_location = st.text_input(label='Job Location', value='India') | |
| with col3: | |
| job_count = st.number_input(label='Job Count', min_value=1, value=1, step=1) | |
| # Submit Button | |
| add_vertical_space(1) | |
| submit = st.form_submit_button(label='Submit') | |
| add_vertical_space(1) | |
| return job_title_input, job_location, job_count, submit | |
| def build_url(job_title, job_location): | |
| b = [] | |
| for i in job_title: | |
| x = i.split() | |
| y = '%20'.join(x) | |
| b.append(y) | |
| job_title = '%2C%20'.join(b) | |
| link = f"https://in.linkedin.com/jobs/search?keywords={job_title}&location={job_location}&locationId=&geoId=102713980&f_TPR=r604800&position=1&pageNum=0" | |
| return link | |
| def open_link(driver, link): | |
| while True: | |
| # Break the Loop if the Element is Found, Indicating the Page Loaded Correctly | |
| try: | |
| driver.get(link) | |
| driver.implicitly_wait(5) | |
| time.sleep(3) | |
| driver.find_element(by=By.CSS_SELECTOR, value='span.switcher-tabs__placeholder-text.m-auto') | |
| return | |
| # Retry Loading the Page | |
| except NoSuchElementException: | |
| continue | |
| def link_open_scrolldown(driver, link, job_count): | |
| # Open the Link in LinkedIn | |
| linkedin_scraper.open_link(driver, link) | |
| # Scroll Down the Page | |
| for i in range(0,job_count): | |
| # Simulate clicking the Page Up button | |
| body = driver.find_element(by=By.TAG_NAME, value='body') | |
| body.send_keys(Keys.PAGE_UP) | |
| # Scoll down the Page to End | |
| driver.execute_script("window.scrollTo(0, document.body.scrollHeight);") | |
| driver.implicitly_wait(2) | |
| # Click on See More Jobs Button if Present | |
| try: | |
| x = driver.find_element(by=By.CSS_SELECTOR, value="button[aria-label='See more jobs']").click() | |
| driver.implicitly_wait(5) | |
| except: | |
| pass | |
| def job_title_filter(scrap_job_title, user_job_title_input): | |
| # User Job Title Convert into Lower Case | |
| user_input = [i.lower().strip() for i in user_job_title_input] | |
| # scraped Job Title Convert into Lower Case | |
| scrap_title = [i.lower().strip() for i in [scrap_job_title]] | |
| # Verify Any User Job Title in the scraped Job Title | |
| confirmation_count = 0 | |
| for i in user_input: | |
| if all(j in scrap_title[0] for j in i.split()): | |
| confirmation_count += 1 | |
| # Return Job Title if confirmation_count greater than 0 else return NaN | |
| if confirmation_count > 0: | |
| return scrap_job_title | |
| else: | |
| return np.nan | |
| def scrap_company_data(driver, job_title_input, job_location): | |
| # scraping the Company Data | |
| company = driver.find_elements(by=By.CSS_SELECTOR, value='h4[class="base-search-card__subtitle"]') | |
| company_name = [i.text for i in company] | |
| location = driver.find_elements(by=By.CSS_SELECTOR, value='span[class="job-search-card__location"]') | |
| company_location = [i.text for i in location] | |
| title = driver.find_elements(by=By.CSS_SELECTOR, value='h3[class="base-search-card__title"]') | |
| job_title = [i.text for i in title] | |
| url = driver.find_elements(by=By.XPATH, value='//a[contains(@href, "/jobs/")]') | |
| website_url = [i.get_attribute('href') for i in url] | |
| # combine the all data to single dataframe | |
| df = pd.DataFrame(company_name, columns=['Company Name']) | |
| df['Job Title'] = pd.DataFrame(job_title) | |
| df['Location'] = pd.DataFrame(company_location) | |
| df['Website URL'] = pd.DataFrame(website_url) | |
| # Return Job Title if there are more than 1 matched word else return NaN | |
| df['Job Title'] = df['Job Title'].apply(lambda x: linkedin_scraper.job_title_filter(x, job_title_input)) | |
| # Return Location if User Job Location in Scraped Location else return NaN | |
| df['Location'] = df['Location'].apply(lambda x: x if job_location.lower() in x.lower() else np.nan) | |
| # Drop Null Values and Reset Index | |
| df = df.dropna() | |
| df.reset_index(drop=True, inplace=True) | |
| return df | |
| def scrap_job_description(driver, df, job_count): | |
| # Get URL into List | |
| website_url = df['Website URL'].tolist() | |
| # Scrap the Job Description | |
| job_description, description_count = [], 0 | |
| for i in range(0, len(website_url)): | |
| try: | |
| # Open the Link in LinkedIn | |
| linkedin_scraper.open_link(driver, website_url[i]) | |
| # Click on Show More Button | |
| driver.find_element(by=By.CSS_SELECTOR, value='button[data-tracking-control-name="public_jobs_show-more-html-btn"]').click() | |
| driver.implicitly_wait(5) | |
| time.sleep(1) | |
| # Get Job Description | |
| description = driver.find_elements(by=By.CSS_SELECTOR, value='div[class="show-more-less-html__markup relative overflow-hidden"]') | |
| data = [i.text for i in description][0] | |
| if len(data.strip()) > 0: | |
| job_description.append(data) | |
| description_count += 1 | |
| else: | |
| job_description.append('Description Not Available') | |
| # If URL cannot Loading Properly | |
| except: | |
| job_description.append('Description Not Available') | |
| # Check Description Count Meets User Job Count | |
| if description_count == job_count: | |
| break | |
| # Filter the Job Description | |
| df = df.iloc[:len(job_description), :] | |
| # Add Job Description in Dataframe | |
| df['Job Description'] = pd.DataFrame(job_description, columns=['Description']) | |
| df['Job Description'] = df['Job Description'].apply(lambda x: np.nan if x=='Description Not Available' else x) | |
| df = df.dropna() | |
| df.reset_index(drop=True, inplace=True) | |
| return df | |
| def display_data_userinterface(df_final): | |
| # Display the Data in User Interface | |
| add_vertical_space(1) | |
| if len(df_final) > 0: | |
| for i in range(0, len(df_final)): | |
| st.markdown(f'<h3 style="color: orange;">Job Posting Details : {i+1}</h3>', unsafe_allow_html=True) | |
| st.write(f"Company Name : {df_final.iloc[i,0]}") | |
| st.write(f"Job Title : {df_final.iloc[i,1]}") | |
| st.write(f"Location : {df_final.iloc[i,2]}") | |
| st.write(f"Website URL : {df_final.iloc[i,3]}") | |
| with st.expander(label='Job Desription'): | |
| st.write(df_final.iloc[i, 4]) | |
| add_vertical_space(3) | |
| else: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">No Matching Jobs Found</h5>', | |
| unsafe_allow_html=True) | |
| def main(): | |
| # Initially set driver to None | |
| driver = None | |
| try: | |
| job_title_input, job_location, job_count, submit = linkedin_scraper.get_userinput() | |
| add_vertical_space(2) | |
| if submit: | |
| if job_title_input != [] and job_location != '': | |
| with st.spinner('Chrome Webdriver Setup Initializing...'): | |
| driver = linkedin_scraper.webdriver_setup() | |
| with st.spinner('Loading More Job Listings...'): | |
| # build URL based on User Job Title Input | |
| link = linkedin_scraper.build_url(job_title_input, job_location) | |
| # Open the Link in LinkedIn and Scroll Down the Page | |
| linkedin_scraper.link_open_scrolldown(driver, link, job_count) | |
| with st.spinner('scraping Job Details...'): | |
| # Scraping the Company Name, Location, Job Title and URL Data | |
| df = linkedin_scraper.scrap_company_data(driver, job_title_input, job_location) | |
| # Scraping the Job Descriptin Data | |
| df_final = linkedin_scraper. scrap_job_description(driver, df, job_count) | |
| # Display the Data in User Interface | |
| linkedin_scraper.display_data_userinterface(df_final) | |
| # If User Click Submit Button and Job Title is Empty | |
| elif job_title_input == []: | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Job Title is Empty</h5>', | |
| unsafe_allow_html=True) | |
| elif job_location == '': | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Job Location is Empty</h5>', | |
| unsafe_allow_html=True) | |
| except Exception as e: | |
| add_vertical_space(2) | |
| st.markdown(f'<h5 style="text-align: center;color: orange;">Check Connection! Refresh the Page and Try Again</h5>', unsafe_allow_html=True) | |
| finally: | |
| if driver: | |
| driver.quit() | |
| # Streamlit Configuration Setup | |
| streamlit_config() | |
| add_vertical_space(5) | |
| with st.sidebar: | |
| add_vertical_space(4) | |
| option = option_menu(menu_title='', options=['Summary', 'Strength', 'Weakness', 'Job Titles', 'Linkedin Jobs'], | |
| icons=['house-fill', 'database-fill', 'pass-fill', 'list-ul', 'linkedin']) | |
| if option == 'Summary': | |
| resume_analyzer.resume_summary() | |
| elif option == 'Strength': | |
| resume_analyzer.resume_strength() | |
| elif option == 'Weakness': | |
| resume_analyzer.resume_weakness() | |
| elif option == 'Job Titles': | |
| resume_analyzer.job_title_suggestion() | |
| elif option == 'Linkedin Jobs': | |
| linkedin_scraper.main() | |