app.py

import streamlit as st
import tensorflow as tf
import pickle
import numpy as np
import pandas as pd


# Load Model
model = tf.keras.models.load_model("news_classification_rnn.h5")

# Load Preprocessing Function
with open("preprocessing.pkl", "rb") as f:
    clean_text = pickle.load(f)

# Load TF-IDF Vectorizer
with open("vectorizer.pkl", "rb") as f:
    vectorizer = pickle.load(f)

# Define News Categories
news_categories = ["Business", "Sci/Tech","Sports","World"]

# Streamlit UI
st.title("📰 News Classification with Simple RNN")
st.write("Enter a news headline to predict its category.")

user_input = st.text_area("Enter News Text:", "")

if st.button("Classify"):
    if user_input:
        # Preprocess Input
        processed_text = clean_text(user_input)

        # Convert text to integer sequence
        text_sequence = tokenizer.texts_to_sequences([processed_text])

        # Pad the sequence to match model input size
        text_padded = tf.keras.preprocessing.sequence.pad_sequences(text_sequence, maxlen=100)

        # Prediction
        prediction = model.predict(text_padded)
        category = np.argmax(prediction)

        st.success(f"Predicted Category: {news_categories[category]}")
    else:
        st.warning("Please enter a news headline.")