Update app.py

app.py

@@ -3,7 +3,7 @@ import tensorflow as tf
 import numpy as np
 
 # Load the trained model
-model = tf.keras.models.load_model("news_classification_rnn.h5")
+model = tf.keras.models.load_model("news_classification_rnn1.h5")
 
 # Load Preprocessing Function
 import dill
@@ -11,7 +11,7 @@ with open("preprocessing1.pkl", "rb") as f:
     clean_text = dill.load(f)
 
 # Load Text Vectorization Layer from SavedModel
-vectorizer = tf.saved_model.load("vectorizer")
+vectorizer = tf.saved_model.load("vector")
 
 # Define News Categories
 news_categories = ["Business", "Sci/Tech", "Sports", "World"]
@@ -31,7 +31,7 @@ if st.button("Classify"):
         text_sequence = vectorizer([processed_text])
 
         # Convert to numpy array (model expects batch input)
-        text_sequence = np.array(text_sequence)
+        
 
         # Predict Category
         prediction = model.predict(text_sequence)