import pandas as pd
import requests
from urllib.parse import urlparse, quote
import re
from bs4 import BeautifulSoup
from joblib import Parallel, delayed
import gradio as gr
from io import StringIO
from nltk import ngrams


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):
    
    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 process_csv_text(temp_file):
    if isinstance(temp_file, str):
        df = pd.read_csv(StringIO(temp_file))
    else:
        df = pd.read_csv(temp_file.name)
      
    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]]
    return df

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

def process_multiple_address(addresses):
    results=Parallel(n_jobs=32, prefer="threads")(delayed(catch_errors)(i) for i in addresses)
    return results

def feed_process_multiple(temp_file):
    df=process_csv_text(temp_file)
    addresses=df['Address All'].to_list()
    results=process_multiple_address(addresses)
    results=pd.concat(results)
    return results


with gr.Blocks() as demo:
    upload_button = gr.UploadButton(label="Upload Addresses", file_types = ['.csv'], live=True, file_count = "single")    
    table = gr.Dataframe(headers=['Address Input', 'Address Output','Match Percent','Website','Square Footage', 'Beds', 'Baths', 'Year Built',
 'Link','Google Search Result', 'Description' ], type="pandas", col_count=11)
    upload_button.upload(fn=feed_process_multiple, inputs=upload_button, 
                         outputs=table, api_name="Address Scrap")
  
   
demo.launch()