Create app.py

app.py

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+import gradio as gr
+import re
+import contractions
+import unicodedata
+import translators as ts
+
+import numpy as np
+import nltk
+nltk.download('punkt')
+
+import spacy
+spacy.load('en_core_web_sm')
+
+nlp = spacy.load('en_core_web_sm')
+
+def spacy_lemmatize_text(text):
+  text = nlp(text)
+  text = ' '.join([word.lemma_ if word.lemma_ != '-PRON-' else word.text for word in text])
+  return text
+
+def remove_accented_chars(text):
+  text = unicodedata.normalize('NFC', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')
+  return text
+ 
+ def remove_special_characters(text, remove_digits=False):
+  pattern = r'[^a-zA-Z0-9\s]' if not remove_digits else r'[^a-zA-Z\s]'
+  text = re.sub(pattern, '', text)
+  return text
+  
+def remove_stopwords(text, is_lower_case=False, stopwords=None):
+  if not stopwords:
+      stopwords = nltk.corpus.stopwords.words('english')
+  tokens = nltk.word_tokenize(text)
+  tokens = [token.strip() for token in tokens]
+  
+  if is_lower_case:
+      filtered_tokens = [token for token in tokens if token not in stopwords]
+  else:
+      filtered_tokens = [token for token in tokens if token.lower() not in stopwords]
+  
+  filtered_text = ' '.join(filtered_tokens)    
+  return filtered_text
+
+def greet(sentence):
+  opo_texto_sem_caracteres_especiais = (remove_accented_chars(sentence))
+  # sentenceMCTIList_base = nltk.word_tokenize(opo_texto_sem_caracteres_especiais)
+  sentenceExpanded = contractions.fix(opo_texto_sem_caracteres_especiais)
+  sentenceWithoutPunctuation = remove_special_characters(sentenceExpanded , remove_digits=True)
+  sentenceLowered = sentenceWithoutPunctuation.lower()
+  sentenceLemmatized = spacy_lemmatize_text(sentenceLowered)
+  sentenceLemStopped = remove_stopwords(sentenceLemmatized, is_lower_case=False)
+
+  return nltk.word_tokenize(sentence)
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()