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
import tensorflow as tf
import keras
from keras.utils import pad_sequences
import pickle
import os

# Streamlit UI
st.set_page_config(page_title="PressGuard", page_icon="🛡️")
# Radium color effect for the title
st.markdown("""
    <style>
    .radium {
        font-size: 60px;
        font-weight: bold;
        color: #f4ff81;  /* Radium-like light greenish-yellow color */
        text-shadow: 0 0 5px #f4ff81, 0 0 10px #f4ff81, 0 0 20px #f4ff81, 0 0 30px #ccff66;
        text-align: center;
    }
    .tagline {
        font-size: 20px;
        color: #ffffff;
        text-align: center;
        margin-bottom: 30px;
    }
    </style>
    <div class='radium'>🛡️ PressGuard</div>
    <div class='tagline'>Classify and Filter Trustworthy News</div>
""", unsafe_allow_html=True)

st.markdown(
    """
    <style>
        body {
            background-image: url('https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/ut9wBSlRR2CCpw95V5ej8.jpeg');
            background-size: cover;
            background-repeat: no-repeat;
            background-attachment: fixed;
        }
    </style>
    """,
    unsafe_allow_html=True
)


# Background Image and Enhanced Styling
st.markdown(
    """
    <style>
        body {
            background-image: url('https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/ut9wBSlRR2CCpw95V5ej8.jpeg');
            background-size: cover;
            background-repeat: no-repeat;
            background-attachment: fixed;
        }

        .centered-container { 
            text-align: center; 
        }
        
        .title {
            font-size: 60px; 
            font-weight: bold; 
            color: white;
            background: linear-gradient(60deg, #880E4F, #4A235A, #311B92, #000000);
            padding: 20px; 
            border-radius: 20px;
            box-shadow: 0 8px 25px rgba(136, 14, 79, 0.5),
                        0 4px 15px rgba(74, 35, 90, 0.6),
                        inset 0 2px 10px rgba(49, 27, 146, 0.4);
            display: inline-block;
            margin-bottom: 20px;
            animation: elegantFadeSlide 1.5s ease-out forwards;
        }
        
        .prompt-box {
            font-size: 22px;
            font-weight: bold;
            color: white;
            text-align: center;
            background: linear-gradient(135deg, #33ccff, #ff99cc, #33ff99, #ffcc00);
            background-size: 400% 400%;
            animation: gradientAnimation 8s ease infinite;
            padding: 15px;
            border-radius: 15px;
            box-shadow: 0 0 15px rgba(255, 255, 255, 0.7),
                        0 0 25px rgba(136, 14, 79, 0.7),
                        0 0 35px rgba(49, 27, 146, 0.7);
            transition: all 0.4s ease-in-out;
        }

        .prompt-box:hover {
            transform: scale(1.05) rotate(1deg);
            box-shadow: 0 0 25px rgba(255, 255, 255, 0.9),
                        0 0 35px rgba(136, 14, 79, 0.9),
                        0 0 45px rgba(49, 27, 146, 0.9);
        }

        @keyframes gradientAnimation {
            0% { background-position: 0% 50%; }
            50% { background-position: 100% 50%; }
            100% { background-position: 0% 50%; }
        }

        .analyze-button {
            width: 180px;
            height: 60px;
            border-radius: 50px;
            background: linear-gradient(45deg, #880E4F, #4A235A, #311B92, #000000);
            font-size: 20px; 
            font-weight: bold; 
            color: white; 
            border: none;
            box-shadow: 0 8px 25px rgba(136, 14, 79, 0.5),
                        0 4px 15px rgba(74, 35, 90, 0.6),
                        0 2px 10px rgba(49, 27, 146, 0.7),
                        inset 0 1px 5px rgba(0, 0, 0, 0.4);
            cursor: pointer;
            transition: all 0.4s ease-in-out;
        }
        
        .analyze-button:hover {
            transform: scale(1.1);
            background: linear-gradient(225deg, #880E4F, #4A235A, #311B92, #000000);
            box-shadow: 0 12px 35px rgba(49, 27, 146, 0.8),
                        0 8px 25px rgba(74, 35, 90, 0.7),
                        0 4px 15px rgba(136, 14, 79, 0.6);
        }
        
        .result-box {
            text-align: center; 
            font-size: 28px; 
            font-weight: bold;
            background: linear-gradient(60deg, #880E4F, #4A235A, #311B92, #000000);
            color: white; 
            padding: 30px; 
            border-radius: 20px;
            display: inline-block; 
            margin-top: 30px;
            box-shadow: 0 6px 20px rgba(74, 35, 90, 0.5),
                        0 3px 15px rgba(136, 14, 79, 0.4),
                        inset 0 2px 10px rgba(49, 27, 146, 0.3);
        }
    </style>
    """,
    unsafe_allow_html=True
)

# Title and Prompt

st.markdown("<div class='prompt-box'>Paste the article content below to analyze its category with PressGuard🛡️</div>", unsafe_allow_html=True)

# Check if NLTK resources are already downloaded
nltk_data_path = os.path.expanduser('~/nltk_data')
if not os.path.exists(nltk_data_path):
    os.makedirs(nltk_data_path)

try:
    nltk.data.find('tokenizers/punkt')
except LookupError:
    nltk.download('punkt', download_dir=nltk_data_path)

try:
    nltk.data.find('corpora/stopwords')
except LookupError:
    nltk.download('stopwords', download_dir=nltk_data_path)

try:
    nltk.data.find('corpora/wordnet')
except LookupError:
    nltk.download('wordnet', download_dir=nltk_data_path)

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

import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')


# Preprocessing Function
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

# Load Model
@st.cache_resource
def load_model():
    model = keras.models.load_model("model_m3_new.keras")
    vectorizer = keras.models.load_model("vec_text_m3_new.keras")
    with open("label_encoder_m5.pkl", 'rb') as file:
        label_encoder = pickle.load(file)
    return model, vectorizer, label_encoder

model, vectorizer, label_encoder = load_model()

# Prediction Function
def predict_category(text):
    processed_text = [pre_process(text)]
    text_vectorized = pad_sequences(vectorizer(processed_text).numpy().tolist(), padding='pre', maxlen=128)
    prediction = model.predict(text_vectorized)
    category_idx = np.argmax(prediction, axis=1)[0]
    return label_encoder.inverse_transform([category_idx])[0]

# User Input
input_text = st.text_area("Enter News Article:", height=200)

if st.button("Analyze", key="analyze-btn", help="Click to classify the news article"):
    if input_text:
        category = predict_category(input_text)
        st.markdown(f"<div class='result-box'>Predicted Category: {category}</div>", unsafe_allow_html=True)
    else:
        st.warning("Please enter some text to analyze.")