Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import Token
+from tensorflow.keras.preprocessing.text import Tokenizer
 import pickle 
 
 # Load the trained model
@@ -34,7 +34,8 @@ ui = gr.Interface(
  
     title='🚫 Spam Message Detection 🕵️‍♂️',
     description="""
-    Welcome to the Spam Message Detection app—a powerful demo designed for learning purposes. 🎓 This application employs advanced machine learning techniques to identify and flag spam messages with remarkable accuracy. 🤖 With a training set accuracy of 99.89% and a validation/test set accuracy of 98.39%, the model has been fine-tuned using a comprehensive dataset.
+    
+    Welcome to the Spam Message Detection app—a powerful demo designed for learning purposes. 🎓 This application employs advanced machine learning techniques to identify and flag spam messages with remarkable accuracy. 🤖 With a training set accuracy of 99.89% and a validation/test set accuracy of 98.39%, the model has been Trained using a comprehensive dataset.
 
     **🔍 Key Features:**
     - State-of-the-art machine learning model
@@ -47,7 +48,7 @@ ui = gr.Interface(
     2. Click the "Detect" button to initiate the spam detection process.
     3. Receive instant feedback on whether the input message is classified as spam or not.
 
-    **📌 Note: **
+    **📌 Note:**
     
     This app is a demonstration and educational tool. It showcases the effectiveness of machine learning in identifying spam messages. Enjoy exploring the world of spam detection with our highly accurate model! 🚀"""
 )