Update app.py

app.py

@@ -29,19 +29,23 @@ nltk.download('wordnet')
 
 def prediction(text):
     
+    
     def preprocess_nltk(text):
+        
+        
         lemmatizer = WordNetLemmatizer()
         tokens = word_tokenize(text.lower())  # Tokenization
         stop_words = set(stopwords.words("english"))
         filtered_tokens = [lemmatizer.lemmatize(token) for token in tokens if token.isalnum() and token not in stop_words]
         return " ".join(filtered_tokens)
+    
     with open("sentiment_analysis_model.pkl", "rb") as file:
         pipe2 = pickle.load(file)
-      text_processed=(preprocess_nltk(text))
-      ans=pipe2.predict([text_processed])
-      classes = ['Irrelevant', 'Natural', 'Negative', 'Positive']
-      predicted_label = ans[0]
-      return(f"The above text is:{classes[predicted_label]}" )
+    text_processed=(preprocess_nltk(text))
+    ans=pipe2.predict([text_processed])
+    classes = ['Irrelevant', 'Natural', 'Negative', 'Positive']
+    predicted_label = ans[0]
+    return(f"The above text is:{classes[predicted_label]}" )
 
 pre = gr.Interface(
     fn=prediction,