Update app.py

app.py

@@ -1,78 +1,155 @@
 import streamlit as st
-import pickle
-import tensorflow as tf
 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
-from nltk.corpus import stopwords  
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-
-# Ensure necessary downloads
+# Download necessary resources
 nltk.download('punkt')
 nltk.download('stopwords')
 nltk.download('wordnet')
 
+import tensorflow
+import keras
+from keras.utils import pad_sequences
+
+import pickle
+
+# Streamlit UI
+st.set_page_config(page_title="News Category Classifier", page_icon="📰", layout="centered")
+
+def set_background(image_path):
+    bg_image_style = f"""
+    <style>
+        .stApp {{
+            background: url('{image_path}') no-repeat center center fixed;
+            background-size: cover;
+        }}
+    </style>
+    """
+    st.markdown(bg_image_style, unsafe_allow_html=True)
+
+# Call this function with the path to your image
+set_background("News image 2.png")  # Ensure the image is in the same directory
+
+# Initialize stopwords and lemmatizer
+stop_words = set(stopwords.words('english')).union({"pm"})
 lemmatizer = WordNetLemmatizer()
-stop_words = set(stopwords.words('english'))  
-
-@st.cache_data
-def pre_process(text):
-    text = text.lower()
-    text = re.sub("<.*?>", "", text)
-    text = re.sub("http[s]?://\\S+", "", text)
-    text = re.sub("[@#]\\S+", "", text)
-    text = re.sub(r"\\_+", " ", text)
-    text = re.sub("^[A-Za-z.].*\\s-\\s", "", text)
-    text = emoji.demojize(text)
-    text = re.sub(":.*?:", "", text)
-    text = re.sub("[^a-zA-Z0-9\\s_]", "", text)
-    words = word_tokenize(text)
-    words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]
-    return " ".join(words)
+
+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 = tf.keras.models.load_model("news_model.keras")
-    vectorizer = tf.keras.models.load_model("news_tv_model.keras")
-    with open("label_encoder.pkl", 'rb') as file:
+    model_path = "news_model.keras"
+    vectorizer_path = "news_tv_model.keras"
+    label_encoder_path = "label_encoder.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
 
-# Load models
 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=128)
+    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]
 
-# ✅ Set Background Image
-image_path = "News image 2.png"  # Ensure this image is inside the app directory
-
-page_bg_img = f"""
-<style>
-.stApp {{
-    background: url('{image_path}') no-repeat center center fixed;
-    background-size: cover;
-}}
-</style>
-"""
-
-st.markdown(page_bg_img, unsafe_allow_html=True)
+# UI
+st.markdown(
+    """
+    <style>
+    .title {
+        color: #ffffff;
+        font-size: 2.4em;
+        text-align: center;
+        font-weight: 700;
+        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;
+        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%);
+        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;
+    }
+    .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);
+    }
+    .result-text {
+        font-size: 1.8em;
+        color: #ffffff;
+        font-weight: 900;
+        text-shadow: 3px 3px 10px rgba(0, 0, 0, 0.5);
+        animation: fadeIn 0.8s ease-in-out;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
 
-st.title("News Classification App")
+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
-user_text = st.text_area("Enter your news content for classification.")
+user_input = st.text_area("Enter text here:", height=150, placeholder="Type your news text here...")
 
-if st.button("Predict Category"):
-    if user_text.strip():
-        category = predict_category(user_text)
-        st.success(f"Predicted Category: {category}")
+if st.button("Analyze 🏷️"):
+    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.warning("Please enter some text to classify.")
+        st.warning("⚠️ Please enter some text to analyze.")