Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 
 # Load the trained model
-model = tf.keras.models.load_model('./saved_model.pb')  
+model = tf.saved_model.load('./saved_model.pb')  
 
 def spam_detection(message):
     # Preprocess the input message
@@ -15,10 +15,10 @@ def spam_detection(message):
     prediction = model.predict(padded_sequence)[0, 0]
     
     # Return the result
-    return "Spam" if prediction >= 0.5 else "Ham"
+    return "Spam" if prediction >= 0.5 else "Not Spam"
 
 # Gradio Interface
-iface = gr.Interface(
+ui = gr.Interface(
     fn=spam_detection,
     inputs=gr.Textbox(prompt="Enter a message:"),
     outputs="text",
@@ -43,4 +43,4 @@ iface = gr.Interface(
     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! 🚀"""
 )
 # Launch the app
-iface.launch()
+ui.launch()