summary.py

import nltk 
from nltk.corpus import stopwords 
from nltk.tokenize import word_tokenize, sent_tokenize
import traceback
import sys
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM



nltk.download('stopwords')
nltk.download('punkt')

def summary_nlp(text):
    stopWords = set(stopwords.words("english")) 
    words = word_tokenize(text)
    freqTable = dict() 
    for word in words: 
        word = word.lower() 
        if word in stopWords: 
            continue
        if word in freqTable: 
            freqTable[word] += 1
        else: 
            freqTable[word] = 1
    sentences = sent_tokenize(text) 
    sentenceValue = dict()
    for sentence in sentences: 
        for word, freq in freqTable.items(): 
            if word in sentence.lower(): 
                if sentence in sentenceValue: 
                    sentenceValue[sentence] += freq 
                else: 
                    sentenceValue[sentence] = freq
    sumValues = 0
    for sentence in sentenceValue: 
        sumValues += sentenceValue[sentence]
    average = int(sumValues / len(sentenceValue))
    summary = '' 
    for sentence in sentences: 
        if (sentence in sentenceValue) and (sentenceValue[sentence] > (1.2 * average)): 
            summary += " " + sentence
    return summary



def Summary_BART(text):
    checkpoint = "sshleifer/distilbart-cnn-12-6"
    tokenizer = AutoTokenizer.from_pretrained(checkpoint)
    model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
    inputs = tokenizer(text, 
                    max_length=1024, 
                    truncation=True,
                    return_tensors="pt")
    summary_ids = model.generate(inputs["input_ids"])
    summary = tokenizer.batch_decode(summary_ids, 
                                  skip_special_tokens=True, 
                                  clean_up_tokenization_spaces=False)
    return summary[0]