Model_class.py

import re
import spacy
from heapq import nlargest
import pickle
import subprocess



def predict(text):
    subprocess.run(["python3", "-m", "spacy", "download", "en_core_web_sm"])
    stop_words = [ 'stop', 'the', 'to', 'and', 'a', 'in', 'it', 'is', 'I', 'that', 'had', 'on', 'for', 'were', 'was']
    nlp = spacy.load("en_core_web_sm")
    doc = nlp(text)

    lemmatized_text = " ".join([token.lemma_ for token in doc])

    re_text = re.sub("[^\s\w,.]"," ",lemmatized_text)
    re_text = re.sub("[ ]{2,}"," ",re_text).lower()

    word_frequencies = {}
    for word in doc:
        if word.text not in "\n":
            if word.text not in stop_words:
                if word.text not in word_frequencies.keys():
                    word_frequencies[word.text] = 1
                else:
                    word_frequencies[word.text] +=1

    max_word_frequency = max(word_frequencies.values(),default=0)

    for word in word_frequencies.keys():
        word_frequencies[word] = word_frequencies[word] / max_word_frequency

    sent_tokens = [sent for sent in doc.sents]
    sent_scores = {}

    for sent in sent_tokens:
        for word in sent:
            if word.text in word_frequencies.keys():
                if sent not in sent_scores.keys():
                    sent_scores[sent] = word_frequencies[word.text]
                else:
                    sent_scores[sent] += word_frequencies[word.text]
   
    sentence_length = int(len(sent_tokens)*0.3)
    summary = nlargest(sentence_length,sent_scores,sent_scores.get)
    final_summary = [word.text for word in summary]
    final_summary = " ".join(final_summary)
    return final_summary