Update app.py

app.py

@@ -1,3 +1,62 @@
+import nltk
+import re
+
+nltk.download('wordnet')
+nltk.download('punkt')
+nltk.download('stopwords')
+nltk.download('averaged_perceptron_tagger')
+nltk.download('maxent_ne_chunker')
+nltk.download('words')
+nltk.download('brown')
+
+from newspaper import Article
+from newspaper import fulltext
+import requests
+import itertools
+
+from nltk.tokenize import word_tokenize
+from sentence_transformers import SentenceTransformer
+import pandas as pd
+import numpy as np
+from pandas import ExcelWriter
+from torch.utils.data import DataLoader
+import math
+from sentence_transformers import models, losses
+from sentence_transformers import SentencesDataset, LoggingHandler, SentenceTransformer
+from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
+from sentence_transformers.readers import *
+from nltk.corpus import stopwords
+stop_words = stopwords.words('english')
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+from sklearn.metrics.pairwise import cosine_similarity
+import scipy.spatial
+import networkx as nx
+from nltk.tokenize import sent_tokenize
+import scispacy
+import spacy
+import en_core_sci_lg
+import string
+from nltk.stem.wordnet import WordNetLemmatizer
+import gradio as gr
+import inflect
+from Bio import Entrez
+from sklearn.cluster import KMeans
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics import silhouette_samples, silhouette_score, davies_bouldin_score
+import json
+p = inflect.engine()
+
+nlp = en_core_sci_lg.load()
+sp = en_core_sci_lg.load()
+all_stopwords = sp.Defaults.stop_words
+
+
+
+
+
+
 word_embedding_model = models.Transformer('cambridgeltl/SapBERT-from-PubMedBERT-fulltext')
 pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
                                pooling_mode_mean_tokens=True,