Update app.py

app.py

@@ -13,28 +13,8 @@ from nltk.stem import WordNetLemmatizer
 from nltk.tokenize import word_tokenize, sent_tokenize
 from sklearn.feature_extraction.text import CountVectorizer
 
-nltk_data_path = '/home/user/nltk_data'
-
-
-if not os.path.exists(nltk_data_path):
-    os.makedirs(nltk_data_path)
-
-
-nltk.data.path.append(nltk_data_path)
-nltk.download('punkt', download_dir=nltk_data_path)
-nltk.download('stopwords', download_dir=nltk_data_path)
-
-
-from nltk.corpus import stopwords
-from nltk.tokenize import sent_tokenize
-
-
-stop_words = set(stopwords.words('english'))
-
-
-sample_text = "This is a test sentence. Let's see if it works!"
-sentences = sent_tokenize(sample_text)
-print(sentences)
+nltk.download('stopwords')
+nltk.download('punkt')
     
 model = load_model('model_improved.keras')
 vectorizer = joblib.load('vectorizer.joblib')
@@ -44,6 +24,7 @@ with open('product_mapping.json', 'r') as file1:
 reverse_mapping = {v: k for k, v in product_mapping.items()}
 
 lemmatizer = WordNetLemmatizer()
+stop_words = set(stopwords.words('english'))
 
 def clean_text(text):
     if text is None: