Update app.py

app.py

@@ -3,18 +3,22 @@ import emoji
 import nltk
 import numpy as np
 import streamlit as st
+import pickle  # To load the tokenizer
+
 from nltk.tokenize import word_tokenize
 from nltk.stem import WordNetLemmatizer
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import Tokenizer
 
 # Ensure necessary downloads
 nltk.download("punkt")
 nltk.download("wordnet")
+nltk.download("omw-1.4")
+nltk.download("averaged_perceptron_tagger")
 
 lemmatizer = WordNetLemmatizer()
 
+# Function to preprocess text
 def pre_process(x):
     x = x.lower()
     x = re.sub("<.*?>", "", x)
@@ -32,13 +36,17 @@ def pre_process(x):
 # Load trained model
 model = load_model("best_rnn_model.h5")
 
-# Tokenizer (Ensure this matches the one used during training)
-MAX_LENGTH = 100  # Set this to the same max length used in training
-tokenizer = Tokenizer()  # Load your trained tokenizer here
+# Load the same tokenizer used during training
+with open("tokenizer.pickle", "rb") as handle:
+    tokenizer = pickle.load(handle)
+
+# Maximum length (must match training settings)
+MAX_LENGTH = 100  
 
 # Class labels
 class_labels = ['Sports', 'Business', 'SciTech', 'World']
 
+# Function to predict category
 def predict_category(text):
     processed_text = pre_process(text)
     seq = tokenizer.texts_to_sequences([processed_text])
@@ -48,14 +56,14 @@ def predict_category(text):
     return predicted_label
 
 # Streamlit UI
-st.title("News Category Classifier")
+st.title("📰 News Category Classifier")
 st.write("Enter a news headline or article snippet, and the model will predict its category.")
 
-user_input = st.text_area("Enter text here:")
+user_input = st.text_area("✍ Enter text here:")
 
-if st.button("Predict"):
+if st.button("🔍 Predict"):
     if user_input.strip():
         prediction = predict_category(user_input)
-        st.success(f"Predicted Category: {prediction}")
+        st.success(f"📌 Predicted Category: **{prediction}**")
     else:
-        st.warning("Please enter some text to classify.")
+        st.warning("⚠️ Please enter some text to classify.")