from torch.utils.data import Dataset, DataLoader import numpy as np import pandas as pd import re import string import emoji from bs4 import BeautifulSoup import warnings warnings.filterwarnings('ignore') import torch from transformers import AutoModel, AutoTokenizer import numpy as np import pandas as pd pd.set_option("display.max_columns", None) class TextPreprocessing: contraction_mapping = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not", "didn't": "did not", "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not", "he'd": "he would","he'll": "he will", "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is", "I'd": "I would", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have","I'm": "I am", "I've": "I have", "i'd": "i would", "i'd've": "i would have", "i'll": "i will", "i'll've": "i will have","i'm": "i am", "i've": "i have", "isn't": "is not", "it'd": "it would", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have", "it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have","mightn't": "might not", "mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have", "needn't": "need not", "needn't've": "need not have","o'clock": "of the clock", "oughtn't": "ought not", "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have", "she'd": "she would", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is", "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have","so's": "so as", "this's": "this is","that'd": "that would", "that'd've": "that would have", "that's": "that is", "there'd": "there would", "there'd've": "there would have", "there's": "there is", "here's": "here is","they'd": "they would", "they'd've": "they would have", "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have", "wasn't": "was not", "we'd": "we would", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have", "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have", "what're": "what are", "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have", "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have", "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not", "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have", "y'all": "you all", "y'all'd": "you all would", "y'all'd've": "you all would have","y'all're": "you all are","y'all've": "you all have","you'd": "you would", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have", "you're": "you are", "you've": "you have", 'u.s':'america', 'e.g':'for example'} punct = [',', '.', '"', ':', ')', '(', '-', '!', '?', '|', ';', "'", '$', '&', '/', '[', ']', '>', '%', '=', '#', '*', '+', '\\', '•', '~', '@', '£', '·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…', '“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─', '▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞', '∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ] punct_mapping = {"‘": "'", "₹": "e", "´": "'", "°": "", "€": "e", "™": "tm", "√": " sqrt ", "×": "x", "²": "2", "—": "-", "–": "-", "’": "'", "_": "-", "`": "'", '“': '"', '”': '"', '“': '"', "£": "e", '∞': 'infinity', 'θ': 'theta', '÷': '/', 'α': 'alpha', '•': '.', 'à': 'a', '−': '-', 'β': 'beta', '∅': '', '³': '3', 'π': 'pi', '!':' '} mispell_dict = {'colour': 'color', 'centre': 'center', 'favourite': 'favorite', 'travelling': 'traveling', 'counselling': 'counseling', 'theatre': 'theater', 'cancelled': 'canceled', 'labour': 'labor', 'organisation': 'organization', 'wwii': 'world war 2', 'citicise': 'criticize', 'youtu ': 'youtube ', 'Qoura': 'Quora', 'sallary': 'salary', 'Whta': 'What', 'narcisist': 'narcissist', 'howdo': 'how do', 'whatare': 'what are', 'howcan': 'how can', 'howmuch': 'how much', 'howmany': 'how many', 'whydo': 'why do', 'doI': 'do I', 'theBest': 'the best', 'howdoes': 'how does', 'mastrubation': 'masturbation', 'mastrubate': 'masturbate', "mastrubating": 'masturbating', 'pennis': 'penis', 'Etherium': 'Ethereum', 'narcissit': 'narcissist', 'bigdata': 'big data', '2k17': '2017', '2k18': '2018', 'qouta': 'quota', 'exboyfriend': 'ex boyfriend', 'airhostess': 'air hostess', "whst": 'what', 'watsapp': 'whatsapp', 'demonitisation': 'demonetization', 'demonitization': 'demonetization', 'demonetisation': 'demonetization'} @staticmethod def clean_text(text): '''Clean emoji, Make text lowercase, remove text in square brackets,remove links,remove punctuation and remove words containing numbers.''' text = emoji.demojize(text) text = re.sub(r'\:(.*?)\:','',text) text = str(text).lower() #Making Text Lowercase text = re.sub('\[.*?\]', '', text) #The next 2 lines remove html text text = BeautifulSoup(text, 'lxml').get_text() text = re.sub('https?://\S+|www\.\S+', '', text) text = re.sub('<.*?>+', '', text) text = re.sub('\n', '', text) text = re.sub('\w*\d\w*', '', text) # replacing everything with space except (a-z, A-Z, ".", "?", "!", ",", "'") text = re.sub(r"[^a-zA-Z?.!,¿']+", " ", text) return text @staticmethod def clean_contractions(text, mapping): '''Clean contraction using contraction mapping''' specials = ["’", "‘", "´", "`"] for s in specials: text = text.replace(s, "'") for word in mapping.keys(): if ""+word+"" in text: text = text.replace(""+word+"", ""+mapping[word]+"") #Remove Punctuations text = re.sub('[%s]' % re.escape(string.punctuation), '', text) # creating a space between a word and the punctuation following it # eg: "he is a boy." => "he is a boy ." text = re.sub(r"([?.!,¿])", r" \1 ", text) text = re.sub(r'[" "]+', " ", text) return text @staticmethod def clean_special_chars(text, punct, mapping): '''Cleans special characters present(if any)''' for p in mapping: text = text.replace(p, mapping[p]) for p in punct: text = text.replace(p, f' {p} ') specials = {'\u200b': ' ', '…': ' ... ', '\ufeff': '', 'करना': '', 'है': ''} for s in specials: text = text.replace(s, specials[s]) return text @staticmethod def correct_spelling(x, dic): '''Corrects common spelling errors''' for word in dic.keys(): x = x.replace(word, dic[word]) return x @staticmethod def remove_space(text): '''Removes awkward spaces''' #Removes awkward spaces text = text.strip() text = text.split() return " ".join(text) @staticmethod def pipeline(text): '''Cleaning and parsing the text.''' text = TextPreprocessing.clean_text(text) text = TextPreprocessing.clean_contractions(text, TextPreprocessing.contraction_mapping) text = TextPreprocessing.clean_special_chars(text, TextPreprocessing.punct, TextPreprocessing.punct_mapping) text = TextPreprocessing.correct_spelling(text, TextPreprocessing.mispell_dict) text = TextPreprocessing.remove_space(text) return text class BERTTestDataset(Dataset): def __init__(self, df, tokenizer, max_len): self.df = df self.max_len = max_len self.text = df.summary self.tokenizer = tokenizer def __len__(self): return len(self.df) def __getitem__(self, index): text = self.text[index] inputs = self.tokenizer.encode_plus( text, truncation=True, add_special_tokens=True, max_length=self.max_len, padding='max_length', return_token_type_ids=True ) ids = inputs['input_ids'] mask = inputs['attention_mask'] token_type_ids = inputs["token_type_ids"] return { 'ids': torch.tensor(ids, dtype=torch.long), 'mask': torch.tensor(mask, dtype=torch.long), 'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long), } class BERTClass(torch.nn.Module): def __init__(self): super(BERTClass, self).__init__() self.roberta = AutoModel.from_pretrained('roberta-base') self.fc = torch.nn.Linear(768,10) def forward(self, ids, mask, token_type_ids): _, features = self.roberta(ids, attention_mask = mask, token_type_ids = token_type_ids, return_dict=False) output = self.fc(features) return output class ModelUtils: @staticmethod def load_model(path): model = BERTClass() model.load_state_dict(torch.load(path, map_location=torch.device('cpu'))) device = 'cpu' return model, device @staticmethod def validation(pred_loader, model): fin_outputs = [] with torch.no_grad(): for _, data in enumerate(pred_loader, 0): ids = data['ids'] mask = data['mask'] token_type_ids = data['token_type_ids'] outputs = model(ids, mask, token_type_ids) fin_outputs.extend(torch.sigmoid(outputs).cpu().detach().numpy().tolist()) return fin_outputs @staticmethod def get_pred_genres(validation, pred_loader, model, device, threshold=0.5): outputs = validation(pred_loader, model) outputs = np.array(outputs) >= threshold genres = ["Drama", "Comedy", "Romance", "Thriller", "Action", "Crime", "Horror", "Family Film", "Adventure", "Animation"] values_array = np.array(outputs) pred_genres = [np.array(genres)[value_row] for value_row in values_array] return pred_genres def load_model_and_tokenizer(): path = 'model.bin' MAX_LEN = 200 tokenizer = AutoTokenizer.from_pretrained('roberta-base') model, device = ModelUtils.load_model(path) return model, device, tokenizer, MAX_LEN def predict_genre(text, model, device, tokenizer, MAX_LEN): TRAIN_BATCH_SIZE = 64 text = TextPreprocessing.pipeline(text) df = pd.DataFrame({'summary': [text]}) pred_data = BERTTestDataset(df, tokenizer, MAX_LEN) pred_loader = DataLoader(pred_data, batch_size=TRAIN_BATCH_SIZE, num_workers=4, shuffle=True, pin_memory=True) pred_genres = ModelUtils.get_pred_genres(ModelUtils.validation, pred_loader, model, device) return pred_genres[0]