Update app.py

app.py

@@ -1,18 +1,17 @@
 import streamlit as st
 import tensorflow as tf
-import dill  # Use dill instead of pickle for preprocessing function
 import numpy as np
 
-# Load Trained Model
+# Load the trained model
 model = tf.keras.models.load_model("news_classification_rnn.h5")
 
 # Load Preprocessing Function
+import dill
 with open("preprocessing1.pkl", "rb") as f:
     clean_text = dill.load(f)
 
-# Load Text Vectorization Layer
-with open("text_vectorizer (1).pkl", "rb") as f:
-    vectorizer = dill.load(f)
+# Load Text Vectorization Layer from SavedModel
+vectorizer = tf.saved_model.load("vectorizer")
 
 # Define News Categories
 news_categories = ["Business", "Sci/Tech", "Sports", "World"]
@@ -25,16 +24,16 @@ user_input = st.text_area("Enter News Text:", "")
 
 if st.button("Classify"):
     if user_input.strip():
-        # Preprocess Input
+        # Preprocess input
         processed_text = clean_text(user_input)
 
-        # Vectorize Input (Convert text to integer sequence)
-        text_sequence = vectorizer([processed_text])  # Directly vectorizes text
+        # Convert text to integer sequence
+        text_sequence = vectorizer([processed_text])
 
-        # Ensure correct shape (model expects batch input)
+        # Convert to numpy array (model expects batch input)
         text_sequence = np.array(text_sequence)
 
-        # Make Prediction
+        # Predict Category
         prediction = model.predict(text_sequence)
         category = np.argmax(prediction)