Create app.py

app.py

@@ -0,0 +1,134 @@
+
+
+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
+
+
+@st.cache_data
+def convert_df(df):
+    return df.to_csv()
+
+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]
+    
+    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']=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\.)")[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")
+    df['Year Built']=df['Description'].str.extract(r"built in (\d{4})")
+    
+    df_final=df[df['Address'].notnull()]
+    df_final=df_final[df_final['Address'].str.contains(str(address_number))]
+    return df_final
+   
+def process_multiple_address(addresses):
+        results=Parallel(n_jobs=-1, prefer="threads")(delayed(google_address)(i) for i in addresses)
+    return results
+
+
+st.set_page_config(layout="wide")
+# col1, col2 = st.columns((2))
+address_file = st.sidebar.radio('Choose',('Single Address', 'File'))
+
+address = st.sidebar.text_input("Address", "190 Pebble Creek Dr Etna, OH 43062")
+uploaded_file = st.sidebar.file_uploader("Choose a file")
+# uploaded_file='C:/Users/mritchey/Documents/addresses 100 generated.xlsx'
+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,trial=True)
+    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.insert(0,'Address Input',address)
+    
+    
+results=results[['Address Input', 'Address', 'Website','Square Footage', 'Beds', 'Baths', 'Year Built',
+ 'Link', 'Description', 
+      ]]
+
+if return_sq=='Yes':
+    results=results.query("`Square Footage`==`Square Footage`").reset_index(drop=1)
+    results.index=results.index+1
+    
+st.dataframe(
+    results,
+    column_config={
+   
+        "Link": st.column_config.LinkColumn("Link"),
+    },
+    hide_index=True,
+)
+
+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)