News_app.py

import streamlit as st
import numpy as np
import re
import emoji

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
# Download necessary resources
nltk.download('punkt_tab')
nltk.download('stopwords')
nltk.download('wordnet')

import tensorflow
import keras
from keras.utils import pad_sequences

import pickle

import base64

# Streamlit UI
st.set_page_config(page_title="News Category Classifier", page_icon="📰", layout="centered")

def set_background(local_image_path):
    with open(local_image_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read()).decode()
    
    bg_image_style = f"""
    <style>
        .stApp {{
            background-image: url(data:image/{"png"};base64,{encoded_string});
            background-size: cover;
            background-repeat: no-repeat;
            background-attachment: fixed;
        }}
    </style>
    """
    st.markdown(bg_image_style, unsafe_allow_html=True)

# Call this function with the path to your image
set_background(r"Images/bkg4.jpg")  # Replace with the actual image path

# Initialize stopwords and lemmatizer
stop_words = set(stopwords.words('english')).union({"pm"})
lemmatizer = WordNetLemmatizer()

def pre_process(x):
    x = x.lower()
    x = re.sub("<.*?>", "", x)
    x = re.sub("http[s]?://.+?\\S+", "", x)
    x = re.sub("[@#].+?\\S", "", x)
    x = re.sub(r"\\_+", " ", x)
    x = re.sub("^[A-Za-z.].*\\s-\\s", "", x)
    x = emoji.demojize(x)
    x = re.sub(":.*?:", "", x)
    x = re.sub("[^a-zA-Z0-9\\s_]", "", x)
    words = word_tokenize(x)
    words = [word for word in words if word not in stop_words]
    x = " ".join([lemmatizer.lemmatize(word) for word in words])
    return x

@st.cache_resource
def load_model():
    model_path = r"new_classifier_m6_acc84.keras"
    vectorizer_path = r"vec_text_m6.keras"
    label_encoder_path = r"label_encoder_m6.pkl"

    model = keras.models.load_model(model_path)
    vectorizer = keras.models.load_model(vectorizer_path)
    with open(label_encoder_path, 'rb') as file:
        label_encoder = pickle.load(file)
    return model, vectorizer, label_encoder

model, vectorizer, label_encoder = load_model()

def predict_category(text):
    processed_text = [pre_process(text)]
    text_vectorized = pad_sequences(vectorizer(processed_text).numpy().tolist(), padding='pre', maxlen=82)
    prediction = model.predict(text_vectorized)
    category_idx = np.argmax(prediction, axis=1)[0]
    return label_encoder.inverse_transform([category_idx])[0]

# UI
st.markdown(
    """
    <style>
    body {
        background-color: #f8f9fa;
        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
    }
    .title {
        color: #ffffff;
        font-size: 2.4em;
        text-align: center;
        font-weight: 700;
        margin-bottom: 15px;
        text-transform: uppercase;
        text-shadow: 2px 2px 8px rgba(0, 0, 0, 1.0);
        padding: 10px;
    }
    .subtitle {
        color: #ffff;
        font-size: 1.3em;
        text-align: center;
        font-weight: 600;
        margin-bottom: 15px;
        text-shadow: 1px 1px 6px rgba(0, 0, 0, 1.0);
        padding: 5px;
    }
    .classify-button {
        background-color: #3498db;
        color: white;
        font-size: 1.2em;
        padding: 12px 24px;
        border: none;
        border-radius: 8px;
        cursor: pointer;
        display: block;
        margin: 20px auto;
        transition: 0.3s;
    }
    .classify-button:hover {
        background-color: #2980b9;
    }
    .result-box {
        background: linear-gradient(135deg, #6284FF 30%, #FF0000 70%); /* Blue to Red */
        padding: 20px;
        border-radius: 10px;
        text-align: center;
        margin-top: 30px;
        position: relative;
        overflow: hidden;
        border: 2px solid transparent;
        background-clip: padding-box, border-box;
        border-image: linear-gradient(135deg, #6284FF 30%, #FF0000 70%);
        border-image-slice: 0;
        transition: transform 0.3s ease-in-out, box-shadow 0.3s ease-in-out;
    }

    /* Hover Effect */
    .result-box:hover {
        transform: scale(1.05);
        box-shadow: 0px 10px 30px rgba(98, 132, 255, 0.8), 
                    0px 10px 30px rgba(255, 0, 0, 0.8);
    }


    /* Light Reflection for Glossy Look */
    .result-box::before {
        content: "";
        position: absolute;
        top: -100%;
        left: -100%;
        width: 250%;
        height: 250%;
        background: radial-gradient(circle, rgba(255, 255, 255, 0.4) 10%, rgba(255, 255, 255, 0) 70%);
        opacity: 0.6;
        transform: rotate(-25deg);
        animation: shine 3s infinite linear;
    }

    /* Shimmer Animation */
    @keyframes shine {
        0% { top: -150%; left: -150%; }
        100% { top: 150%; left: 150%; }
    }

    .result-text {
        font-size: 1.8em;
        color: #ffffff; /* Darker color for better visibility */
        font-weight: 900;
        text-shadow: 3px 3px 10px rgba(0, 0, 0, 0.5);
        animation: fadeIn 0.8s ease-in-out;
    }
    .warning-box {
        background: linear-gradient(135deg, rgba(255, 77, 77, 0.8), rgba(255, 165, 0, 0.7)); /* Smooth red-orange gradient */
        padding: 18px;
        border-radius: 10px;
        text-align: center;
        color: white;
        font-weight: bold;
        font-size: 1.2em;
        text-shadow: 1px 1px 4px rgba(0, 0, 0, 0.6);
        margin-top: 25px;
        border: 2px solid rgba(255, 77, 77, 0.9);
        box-shadow: 0px 8px 15px rgba(255, 0, 0, 0.4);
        transition: transform 0.3s ease-in-out, box-shadow 0.3s ease-in-out;
    }
    </style>
    """,
    unsafe_allow_html=True
)

st.markdown("<div class='title'>📰 News Classifier</div>", unsafe_allow_html=True)
st.markdown("<div class='subtitle'>"
            "Enter a news headline or article snippet to analyze its category."
            "</div>", unsafe_allow_html=True)

user_input = st.text_area("Enter text here:", height=150, key="text_input", 
                          label_visibility="collapsed", placeholder="Type your news text here...",
                          help="Enter news article or headline")

if st.button("Analyze 🏷️", key="classify_button", on_click=None, help="Click to analyze the entered text"):
    if user_input.strip():
        category = predict_category(user_input)
        st.markdown(f"<div class='result-box'><span class='result-text'>🗂️ Predicted Category: <strong>{category}</strong></span></div>", unsafe_allow_html=True)
    else:
        st.markdown("<div class='warning-box'>⚠️ Please enter some text to analyze.</div>", unsafe_allow_html=True)