Upload app.py

app.py

@@ -10,14 +10,9 @@ import tensorflow as tf
 from nltk.corpus import stopwords
 from nltk.tokenize import word_tokenize
 from nltk.stem import WordNetLemmatizer
-from gensim.models import Word2Vec
 nltk.download('punkt')
 nltk.download('stopwords')
 
-# # Load tokenizer
-# with open("tokenizer.pkl", "rb") as tokenizer_file:
-#     tokenizer = pickle.load(tokenizer_file)
-# Define the model
 model_path= 'model'
 
 # Load model
@@ -114,11 +109,8 @@ def run():
         if submitted:
                 df_inf = {'preprocessing_review': text}
                 df_inf = pd.DataFrame([df_inf])
-                # Preprocess the text (apply the same preprocessing steps as used during training)
                 df_inf['preprocessing_review'] = df_inf['preprocessing_review'].apply(lambda x: review_preprocessing(x))
-                # df_inf = model.texts_to_sequences(df_inf)
-                # df_inf = pad_sequences(df_inf, maxlen=700)
-                # Make the prediction using the loaded model
+              
 
                 y_pred_inf = model.predict(df_inf['preprocessing_review'])
                 y_pred_inf = np.argmax(df_inf['preprocessing_review'], axis = -1)