Update app.py

app.py

@@ -17,6 +17,7 @@ nltk.download('stopwords')
 lemmatizer = WordNetLemmatizer()
 stop_words = set(stopwords.words('english'))  
 
+@st.cache_data
 def pre_process(text):
     text = text.lower()
     text = re.sub("<.*?>", "", text)
@@ -31,15 +32,23 @@ def pre_process(text):
     words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]
     return " ".join(words)
 
-# Load the label encoder
-with open("label_encoder.pkl", "rb") as f:
-    label_encoder = pickle.load(f)
+@st.cache_resource
+def load_label_encoder():
+    with open("label_encoder.pkl", "rb") as f:
+        return pickle.load(f)
 
-# Load the text vectorization model
-text_vectorizer = tf.keras.models.load_model("news_tv_model.keras")
+@st.cache_resource
+def load_text_vectorizer():
+    return tf.keras.models.load_model("news_tv_model.keras")
 
-# Load the news classification model
-news_model = tf.keras.models.load_model("news_model.keras")
+@st.cache_resource
+def load_news_model():
+    return tf.keras.models.load_model("news_model.keras")
+
+# Load resources
+label_encoder = load_label_encoder()
+text_vectorizer = load_text_vectorizer()
+news_model = load_news_model()
 
 def predict_category(text):
     processed_text = [pre_process(text)]
@@ -59,4 +68,4 @@ if st.button("Predict Category"):
         category = predict_category(user_text)
         st.success(f"Predicted Category: {category}")
     else:
-        st.warning("Please enter some text to classify.")
+        st.warning("Please enter some text to classify.")