Upload app.py

app.py

@@ -1,624 +1,637 @@
-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()
-
-
+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;">Resume Analyzer AI</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)
+
+            # Locate the sign-in modal dialog 
+            try:
+                driver.find_element(by=By.CSS_SELECTOR, 
+                                value="button[data-tracking-control-name='public_jobs_contextual-sign-in-modal_modal_dismiss']>icon>svg").click()
+            except:
+                pass
+
+            # 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]
+                
+                # Check Description length and Duplicate
+                if len(data.strip()) > 0 and data not in job_description:
+                    job_description.append(data)
+                    description_count += 1
+                else:
+                    job_description.append('Description Not Available')
+            
+            # If any unexpected issue 
+            except:
+                job_description.append('Description Not Available')
+            
+            # Check Description Count reach 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;">{e}</h5>', unsafe_allow_html=True)
+        
+        finally:
+            if driver:
+                driver.quit()
+
+
+
+# Streamlit Configuration Setup
+streamlit_config()
+add_vertical_space(2)
+
+
+
+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()
+
+