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):
    
    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))
    return similarity


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})')
    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]
    df['Square Footage']=df['Square Footage'].replace({',':''},regex=True).str.replace(r'\D', '')
    
    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_similarity(address,i)*100 for i in df['Address Output']]
    
    
    # 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]})


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")

address = st.sidebar.text_input("Address", "190 Pebble Creek Dr Etna, OH 43062")
uploaded_file = st.sidebar.file_uploader("Choose a file")
address_file = st.sidebar.radio('Choose',('Single Address', 'File'))
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 address_file == 'File' and 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', '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



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 data 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)