app.py

import streamlit as st
import pandas as pd
import numpy as np
import requests
from urllib.parse import urlparse, quote
import re
from bs4 import BeautifulSoup
import time
from joblib import Parallel, delayed
from nltk import ngrams

@st.cache_data
def convert_df(df):
    return df.to_csv()

def normalize_string(string):
    normalized_string = string.lower()
    normalized_string = re.sub(r'[^\w\s]', '', normalized_string)
    
    return normalized_string


def jaccard_similarity(string1, string2,n = 2, normalize=True):
    try:
        if normalize:
           string1,string2= normalize_string(string1),normalize_string(string2)
           
        grams1 = set(ngrams(string1, n))
        grams2 = set(ngrams(string2, n))
        similarity = len(grams1.intersection(grams2)) / len(grams1.union(grams2))
    except:
        similarity=0
        
    if string2=='did not extract address':
        similarity=0
        
    return similarity

def jaccard_sim_split_word_number(string1,string2):
    numbers1 = ' '.join(re.findall(r'\d+', string1))
    words1 = ' '.join(re.findall(r'\b[A-Za-z]+\b', string1))
    
    numbers2 = ' '.join(re.findall(r'\d+', string2))
    words2 = ' '.join(re.findall(r'\b[A-Za-z]+\b', string2))   
    
    number_similarity=jaccard_similarity(numbers1,numbers2)
    words_similarity=jaccard_similarity(words1,words2)
    return (number_similarity+words_similarity)/2

def extract_website_domain(url):
    parsed_url = urlparse(url)
    return parsed_url.netloc


def google_address(address):     
    # address_number = re.findall(r'\b\d+\b', address)[0]
    # address_zip =re.search(r'(\d{5})$', address).group()[:2]
    
    search_query = quote(address)
    url=f'https://www.google.com/search?q={search_query}'
    response = requests.get(url)
    soup = BeautifulSoup(response.content, "html.parser")
    
    texts_links = []
    for link in soup.find_all("a"):
        t,l=link.get_text(), link.get("href")
        if (l[:11]=='/url?q=http') and (len(t)>20 ):
            texts_links.append((t,l))
    
    text = soup.get_text()
    
    texts_links_des=[]
    for i,t_l in enumerate(texts_links):
        start=text.find(texts_links[i][0][:50])
        try:
            end=text.find(texts_links[i+1][0][:50])
        except:
            end=text.find('Related searches')
            
        description=text[start:end]
        texts_links_des.append((t_l[0],t_l[1],description))
    
    df=pd.DataFrame(texts_links_des,columns=['Title','Link','Description'])
    df['Description']=df['Description'].bfill()
    df['Address Output']=df['Title'].str.extract(r'(.+? \d{5})').fillna("**DID NOT EXTRACT ADDRESS**")
    
    df['Link']=[i[7:i.find('&sa=')] for i in df['Link']]
    df['Website'] = df['Link'].apply(extract_website_domain)
    
    df['Square Footage']=df['Description'].str.extract(r"((\d+) Square Feet|(\d+) sq. ft.|(\d+) sqft|(\d+) Sq. Ft.|(\d+) sq|(\d+(?:,\d+)?) Sq\. Ft\.|(\d+(?:,\d+)?) sq)")[0]
    try:
        df['Square Footage']=df['Square Footage'].replace({',':''},regex=True).str.replace(r'\D', '')
    except:
        pass
    df['Beds']=df['Description'].replace({'-':' ','total':''},regex=True).str.extract(r"(\d+) bed")
    
    
    df['Baths']=df['Description'].replace({'-':' ','total':''},regex=True).str.extract(r"((\d+) bath|(\d+(?:\.\d+)?) bath)")[0]
    df['Baths']=df['Baths'].str.extract(r'([\d.]+)').astype(float)
    
    df['Year Built']=df['Description'].str.extract(r"built in (\d{4})")
    
    df['Match Percent']=[jaccard_sim_split_word_number(address,i)*100 for i in df['Address Output']]
    df['Google Search Result']=[*range(1,df.shape[0]+1)]
   
    # df_final=df[df['Address Output'].notnull()]
    # df_final=df_final[(df_final['Address Output'].str.contains(str(address_number))) & (df_final['Address Output'].str.contains(str(address_zip)))]

    df.insert(0,'Address Input',address)
    
    return df

    
def catch_errors(addresses):
    try: 
        return google_address(addresses)
    except:
        return pd.DataFrame({'Address Input':[addresses]})

@st.cache_data
def process_multiple_address(addresses):
    results=Parallel(n_jobs=32, prefer="threads")(delayed(catch_errors)(i) for i in addresses)
    return results
    
  
st.set_page_config(layout="wide")
st.header("Google Data Scrap")  

address = st.sidebar.text_input("Single Address:", "190 Pebble Creek Dr Etna, OH 43062")
uploaded_file = st.sidebar.file_uploader("Upload Multiple Addresses:")
return_top_1 = st.sidebar.radio('Return Only Top Results',('No', 'Yes'))
match_percent = st.sidebar.selectbox('Address Match Percentage At Least:',(70, 80, 90, 100, 0))
return_sq = st.sidebar.radio('Return Only Results with Square Footage',('No', 'Yes'))

if uploaded_file is not None:
    try:
        df = pd.read_csv(uploaded_file)
    except:
        df = pd.read_excel(uploaded_file)
     
    address_cols=list(df.columns[:4])
    df[address_cols[-1]]=df[address_cols[-1]].astype(str).str[:5].astype(int).astype(str)
    df[address_cols[-1]]=df[address_cols[-1]].apply(lambda x: x.zfill(5))
    
    df['Address All']=df[address_cols[0]]+', '+df[address_cols[1]]+', '+df[address_cols[2]]+' '+df[address_cols[3]]
    
    results= process_multiple_address(df['Address All'].values)
    results=pd.concat(results).reset_index(drop=1)
    # results.index=results.index+1

else:    
    results=google_address(address).reset_index(drop=1)
    # results.index=results.index+1

    
results=results[['Address Input', 'Address Output','Match Percent','Website','Square Footage', 'Beds', 'Baths', 'Year Built',
 'Link','Google Search Result', 'Description' ]]
results=results.query(f"`Match Percent`>={match_percent}")

if return_sq=='Yes':
    results=results.query("`Square Footage`==`Square Footage`").reset_index(drop=1)
    # results.index=results.index+1
    
if return_top_1=='Yes':
    results=results.query("`Google Search Result`==1").reset_index(drop=1)


with st.container():

    st.dataframe(
        results,
        column_config={
       
            "Link": st.column_config.LinkColumn("Link"),
            'Match Percent': st.column_config.NumberColumn(format='%.2f %%'),
        },
        hide_index=True,
        # height=500,
        # width=500,
    )

csv2 = convert_df(results)
st.download_button(
    label="Download Results as CSV",
    data=csv2,
    file_name=f'{address}.csv',
    mime='text/csv')


st.markdown(""" <style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style> """, unsafe_allow_html=True)