ksv-muralidhar · August 23, 2021 08:55
diff --git a/bing_scrape_streamlit.py b/bing_scrape_streamlit.py
 import pandas as pd
 from bs4 import BeautifulSoup
 import requests as r
 import streamlit as st

 st.markdown('<h1 style="background-color: gainsboro; padding-left: 10px; padding-bottom: 20px;">Search Engine Scraper</h1>', unsafe_allow_html=True)
 query = st.text_input('', help='Enter the search string and hit Enter/Return')
 query = query.replace(" ", "+") #replacing the spaces in query result with +

 if query: #Activates the code below on hitting Enter/Return in the search textbox
    try:#Exception handling 
        req = r.get(f"https://www.bing.com/search?q={query}",
                    headers = {"user-agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.131 Safari/537.36"})
        result_str = '<html><table style="border: none;">' #Initializing the HTML code for displaying search results
        
        if req.status_code == 200: #Status code 200 indicates a successful request
            bs = BeautifulSoup(req.content, features="html.parser") #converting the content/text returned by request to a BeautifulSoup object
            search_result = bs.find_all("li", class_="b_algo") #'b_algo' is the class of the list object which represents a single result
            search_result = [str(i).replace("<strong>","") for i in search_result] #removing the <strong> tag
            search_result = [str(i).replace("</strong>","") for i in search_result] #removing the </strong> tag
            result_df = pd.DataFrame() #Initializing the data frame that stores the results
            
            for n,i in enumerate(search_result): #iterating through the search results
                individual_search_result = BeautifulSoup(i, features="html.parser") #converting individual search result into a BeautifulSoup object
                h2 = individual_search_result.find('h2') #Finding the title of the individual search result
                href = h2.find('a').get('href') #title's URL of the individual search result
                cite = f'{href[:50]}...' if len(href) >= 50 else href # cite with first 20 chars of the URL
                url_txt = h2.find('a').text #title's text of the individual search result
                #In a few cases few individual search results doesn't have a description. In such cases the description would be blank
                description = "" if individual_search_result.find('p') is None else individual_search_result.find('p').text
                #Appending the result data frame after processing each individual search result
                result_df = result_df.append(pd.DataFrame({"Title": url_txt, "URL": href, "Description": description}, index=[n]))
                count_str = f'<b style="font-size:20px;">Bing Search returned {len(result_df)} results</b>'
                ########################################################
                ######### HTML code to display search results ##########
                ########################################################
                result_str += f'<tr style="border: none;"><h3><a href="{href}" target="_blank">{url_txt}</a></h3></tr>'+\
                f'<tr style="border: none;"><strong style="color:green;">{cite}</strong></tr>'+\
                f'<tr style="border: none;">{description}</tr>'+\
                f'<tr style="border: none;"><td style="border: none;"></td></tr>'
            result_str += '</table></html>'
            
        #if the status code of the request isn't 200, then an error message is displayed along with an empty data frame        
        else:
            result_df = pd.DataFrame({"Title": "", "URL": "", "Description": ""}, index=[0])
            result_str = '<html></html>'
            count_str = '<b style="font-size:20px;">Looks like an error!!</b>'
            
    #if an exception is raised, then an error message is displayed along with an empty data frame
    except:
        result_df = pd.DataFrame({"Title": "", "URL": "", "Description": ""}, index=[0])
        result_str = '<html></html>'
        count_str = '<b style="font-size:20px;">Looks like an error!!</b>'
    
    st.markdown(f'{count_str}', unsafe_allow_html=True)
    st.markdown(f'{result_str}', unsafe_allow_html=True)
    st.markdown('<h3>Data Frame of the above search result</h3>', unsafe_allow_html=True)
    st.dataframe(result_df)
	import pandas as pd
	from bs4 import BeautifulSoup
	import requests as r
	import streamlit as st

	st.markdown('<h1 style="background-color: gainsboro; padding-left: 10px; padding-bottom: 20px;">Search Engine Scraper</h1>', unsafe_allow_html=True)
	query = st.text_input('', help='Enter the search string and hit Enter/Return')
	query = query.replace(" ", "+") #replacing the spaces in query result with +

	if query: #Activates the code below on hitting Enter/Return in the search textbox
	try:#Exception handling
	req = r.get(f"https://www.bing.com/search?q={query}",
	headers = {"user-agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.131 Safari/537.36"})
	result_str = '<html><table style="border: none;">' #Initializing the HTML code for displaying search results

	if req.status_code == 200: #Status code 200 indicates a successful request
	bs = BeautifulSoup(req.content, features="html.parser") #converting the content/text returned by request to a BeautifulSoup object
	search_result = bs.find_all("li", class_="b_algo") #'b_algo' is the class of the list object which represents a single result
	search_result = [str(i).replace("<strong>","") for i in search_result] #removing the <strong> tag
	search_result = [str(i).replace("</strong>","") for i in search_result] #removing the </strong> tag
	result_df = pd.DataFrame() #Initializing the data frame that stores the results

	for n,i in enumerate(search_result): #iterating through the search results
	individual_search_result = BeautifulSoup(i, features="html.parser") #converting individual search result into a BeautifulSoup object
	h2 = individual_search_result.find('h2') #Finding the title of the individual search result
	href = h2.find('a').get('href') #title's URL of the individual search result
	cite = f'{href[:50]}...' if len(href) >= 50 else href # cite with first 20 chars of the URL
	url_txt = h2.find('a').text #title's text of the individual search result
	#In a few cases few individual search results doesn't have a description. In such cases the description would be blank
	description = "" if individual_search_result.find('p') is None else individual_search_result.find('p').text
	#Appending the result data frame after processing each individual search result
	result_df = result_df.append(pd.DataFrame({"Title": url_txt, "URL": href, "Description": description}, index=[n]))
	count_str = f'<b style="font-size:20px;">Bing Search returned {len(result_df)} results</b>'
	########################################################
	######### HTML code to display search results ##########
	########################################################
	result_str += f'<tr style="border: none;"><h3><a href="{href}" target="_blank">{url_txt}</a></h3></tr>'+\
	f'<tr style="border: none;"><strong style="color:green;">{cite}</strong></tr>'+\
	f'<tr style="border: none;">{description}</tr>'+\
	f'<tr style="border: none;"><td style="border: none;"></td></tr>'
	result_str += '</table></html>'

	#if the status code of the request isn't 200, then an error message is displayed along with an empty data frame
	else:
	result_df = pd.DataFrame({"Title": "", "URL": "", "Description": ""}, index=[0])
	result_str = '<html></html>'
	count_str = '<b style="font-size:20px;">Looks like an error!!</b>'

	#if an exception is raised, then an error message is displayed along with an empty data frame
	except:
	result_df = pd.DataFrame({"Title": "", "URL": "", "Description": ""}, index=[0])
	result_str = '<html></html>'
	count_str = '<b style="font-size:20px;">Looks like an error!!</b>'

	st.markdown(f'{count_str}', unsafe_allow_html=True)
	st.markdown(f'{result_str}', unsafe_allow_html=True)
	st.markdown('<h3>Data Frame of the above search result</h3>', unsafe_allow_html=True)
	st.dataframe(result_df)