from flask import Flask, redirect, render_template, request, jsonify
import requests
from datetime import datetime
import pandas as pd
import numpy as np
from gensim.models import Doc2Vec
import snowballstemmer, re
from bs4 import BeautifulSoup
import re, sys
from tensorflow.keras.models import load_model
import joblib
import gradio as gr
import json

headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36, Opera/9.80 (Windows NT 6.1; WOW64) Presto/2.12.388 Version/12.18'
}

app = Flask(__name__)

def getsoup(url):
    response = requests.get(url, headers=headers)
    Status_Code = response.status_code
    print(url)
    print(Status_Code)
    
    if Status_Code == 200:
      soup = BeautifulSoup(response.content, features="lxml")
    else:
      soup = getsoup(url)
    return soup 

def getLastPageNumber(soup, site):
    pageNumber = []
    if site == 'flipkart':
        review_number = int(soup.find("span", "_2_R_DZ").text.strip().replace(',', '').split()[-2])
        if review_number <=10:
            lastPage = 1
        else:
            link = soup.find(attrs={"class": "_2MImiq _1Qnn1K"})
            pageNumber = link.find('span').text.strip().replace(',', '').split()
            lastPage1 = pageNumber[len(pageNumber)-1]
            lastPage = int(lastPage1)
    elif site == 'amazon':
        review_number = int(soup.find("div", {"data-hook": "cr-filter-info-review-rating-count"}).text.strip().replace(',', '').split()[-3])
        if review_number <=10:
            lastPage = 1
        else:
            lastPage = review_number // 10
    if lastPage > 500:
        lastPage = 2
    return lastPage


def geturllist(url, lastPage):
    urllistPages = []
    url = url[:-1]
    for i in range(1,lastPage+1):
      urllistPages.append (url + str(i))
    return urllistPages


def getReviews(soup, site, url):
    if site == 'flipkart':
        #Extracting the Titles
        title_sec = soup.find_all("p",'_2-N8zT')
        title = []
        for s in title_sec:
            title.append(s.text)

        author_sec = soup.find_all("p","_2sc7ZR _2V5EHH")
        author = []
        for r in author_sec:
            author.append(r.text)

        Review_text_sec = soup.find_all("div",'t-ZTKy')
        text = []
        for t in Review_text_sec:
            text.append(t.text)
        
        print(Review_text_sec)
            
        Rating = soup.find_all("div", {"class": ["_3LWZlK _1BLPMq", "_3LWZlK _32lA32 _1BLPMq", "_3LWZlK _1rdVr6 _1BLPMq"]})    
        rate = []
        for d in Rating:
            rate.append(d.text)

        Date_sec = soup.find_all(lambda tag: tag.name == 'p' and tag.get('class') == ['_2sc7ZR'])    
        date = []
        for d in Date_sec:
          date.append(d.text)

        help_sec = soup.find_all(lambda tag: tag.name == 'div' and tag.get('class') == ['_1LmwT9'])    
        help1 = []
        for d in help_sec:
          help1.append(d.text)
    
    elif site == 'amazon':
        n_ = 0
        title_sec = soup.find_all(attrs={"data-hook": "review-title", "class": "a-size-base a-link-normal review-title a-color-base review-title-content a-text-bold"})
        title = []
        for s in title_sec:
            title.append(s.text.replace('\n', ''))
        n_ = len(title)

        author_sec = soup.find_all(attrs = {"class": "a-profile-name"})
        author = []
        for r in author_sec:
            author.append(r.text)
        while(1):
            if len(author) > n_:
                author.pop(0)
            else:
                break

        Review_text_sec = soup.find_all(attrs={"data-hook": "review-body", "class": "a-size-base review-text review-text-content"})
        text = []
        for t in Review_text_sec:
            text.append(t.text.replace('\n', ''))
        print(Review_text_sec)

        Rating = soup.find_all(attrs={"data-hook": "review-star-rating"})    
        rate = []
        for d in Rating:
            rate.append(d.text)

        Date_sec = soup.find_all(attrs={"data-hook": "review-date"})    
        date = []
        for d in Date_sec:
          date.append(d.text) 

        help_sec = soup.find_all(attrs={"data-hook": "helpful-vote-statement"})    
        help1 = []
        for d in help_sec:
             help1.append(d.text.replace('\n          ', '')) 
        while(1):
            if len(help1) < n_:
                help1.append(0)
            else:
                break
        
    url1 = []
    url1 = [url] * len(date)

    collate = {'Date': date, 'URL': url1, 'Review_Title': title, 'Author': author, 'Rating': rate, 'Review_text': text, 'Review_helpful': help1}          
    collate_df = pd.DataFrame.from_dict(collate)
    return collate_df

def preprocess_text(text):
    stemmer = snowballstemmer.EnglishStemmer()
    text = " ".join(stemmer.stemWords(re.sub('[!"#%\'()*+,-./:;<=>?@[\\]^_`{|}~1234567890’”“′‘\\\\]', ' ', text).split(' ')))
    stop_words = set(["may", "also", "zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "ten", "across","among", "beside", "however", "yet", "within"] + list('abcdefghijklmnopqrstuvwxyz'))
    stop_list = stemmer.stemWords(stop_words)
    stop_words.update(stop_list)
    text = " ".join(filter(None, filter(lambda word: word not in stop_words, text.lower().split(' '))))
    return text.split(' ')

def vectorize_comments_(df, d2v_model):
    y = []
    comments = []
    for i in range(0, len(df)): 
        
        print(i)
        label = 'SENT_%s' %i
        comments.append(d2v_model.docvecs[label])
    return comments

def scraper(url):
    df2 = []      
    soup = getsoup(url)
    site = url.split('.')[1]
    # if site == 'flipkart':
    #     url = url + '&page=1'
    # elif site == 'amazon':
    #     url = url + '&pageNumber=1'
    product = url.split('/')[3]
    lastPage = 1
    urllistPages = geturllist(url, lastPage)
    x = 1
    for url in urllistPages:
        soup = getsoup(url)
        df1 = getReviews(soup, site, url)         
        if x == 1:
            df3 = []
            df3 = df1
        else:                        
            df2 = df3
            result = df2.append(df1, ignore_index=True)
            df3 = result
        x += 1
    
    loaded_model = load_model('weights.best.from_scratch1 (1).hdf5')

    preprocessed_arr = [preprocess_text(x) for x in list(df3['Review_text'])]

    doc2vec_model = Doc2Vec.load("doc2vec_model_opinion_corpus (1).d2v")

    textData = vectorize_comments_(preprocessed_arr, doc2vec_model)

    textData_array = np.array(textData)

    num_vectors = textData_array.shape[0]
    textData_3d = textData_array.reshape((num_vectors, 1, -1))

    new_shape = (textData_array.shape[0], 380, 512)

    X_test3_reshaped = np.zeros(new_shape, dtype=textData_3d.dtype)
    X_test3_reshaped[:, :textData_3d.shape[1], :textData_3d.shape[2]] = textData_3d

    predictions = np.rint(loaded_model.predict(X_test3_reshaped))

    argMax = []

    for i in predictions:
        argMax.append(np.argmax(i))

    arr = []
    for i, j in enumerate(argMax):
        if j == 2 or j == 1:
            arr.append(i)
    return {'class': 'review-text-content', 'indices': arr}

def index(img_url):
    results = scraper(img_url)
    print(results)
    return json.dumps(results)

inputs_image_url = [
    gr.Textbox(type="text", label="Image URL"),
]

outputs_result_dict = [
    gr.Textbox(type="text", label="Result Dictionary"),
]

interface_image_url = gr.Interface(
    fn=index,
    inputs=inputs_image_url,
    outputs=outputs_result_dict,
    title="Dark review detection",
    cache_examples=False,
)

gr.TabbedInterface(
    [interface_image_url],
    tab_names=['Reviews inference']
).queue().launch()