Summary.py

#!/usr/bin/env python
# coding: utf-8
import nltk
from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
import networkx as nx
import math
import joblib
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression

def read_article(a):
    if a[-1]!='.':
        a+='.'
    article = a.split(".")
    sentences = []
    for sentence in article:
        sentences.append(sentence.replace("[^a-zA-Z]", " ").split(" "))
    sentences.pop()
    n=len(sentences)
    return sentences,n


def sentence_similarity(sent1, sent2, stopwords=None):
    if stopwords is None:
        stopwords = []

    sent1 = [w.lower() for w in sent1]
    sent2 = [w.lower() for w in sent2]

    all_words = list(set(sent1 + sent2))

    vector1 = [0] * len(all_words)
    vector2 = [0] * len(all_words)

    # build the vector for the first sentence
    for w in sent1:
        if w in stopwords:
            continue
        vector1[all_words.index(w)] += 1

    # build the vector for the second sentence
    for w in sent2:
        if w in stopwords:
            continue
        vector2[all_words.index(w)] += 1

    return 1 - cosine_distance(vector1, vector2)


def build_similarity_matrix(sentences, stop_words):
    # Create an empty similarity matrix
    similarity_matrix = np.zeros((len(sentences), len(sentences)))

    for idx1 in range(len(sentences)):
        for idx2 in range(len(sentences)):
            if idx1 == idx2:  # ignore if both are same sentences
                continue
            similarity_matrix[idx1][idx2] = sentence_similarity(sentences[idx1], sentences[idx2], stop_words)

    return similarity_matrix


def generate_summary(file_name):
    nltk.download("stopwords")
    stop_words = stopwords.words('english')
    summarize_text = []

    # Step 1 - Read text anc split it
    sentences,n = read_article(file_name)
    top_n=int(n/2)+1

    # Step 2 - Generate Similary Martix across sentences
    sentence_similarity_martix = build_similarity_matrix(sentences, stop_words)

    # Step 3 - Rank sentences in similarity martix
    sentence_similarity_graph = nx.from_numpy_array(sentence_similarity_martix)
    scores = nx.pagerank(sentence_similarity_graph)

    # Step 4 - Sort the rank and pick top sentences
    ranked_sentence = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)

    for i in range(top_n):
        summarize_text.append(" ".join(ranked_sentence[i][1]))
    return summarize_text

def generate_legal(Sentence_list):
    Sentence_list_legal=[]
    for i in Sentence_list:
        if legal(i):
            Sentence_list_legal+=[i]
    if Sentence_list_legal==[]:
    	Sentence_list_legal+=['The Document Is Safe']
    return Sentence_list_legal

   
def Format(Sum):
    string=''
    count=1
    for i in Sum:
        w=str(count)+'.'+i+'.<br/><br/>'
        string+=w
        count+=1
    return string
    
def legal(sentence):
    with open('./asset/vectorizer.joblib','rb') as f:
        encoder=joblib.load(f)
    with open('./asset/classifier.joblib','rb') as f:
        model=joblib.load(f)
    t=encoder.transform([sentence])
    if int(model.predict(t))==1:
        return True
    return False