Update app.py

app.py

@@ -18,13 +18,13 @@ nltk.download('wordnet')
 import tensorflow as tf
 import keras
 from keras.preprocessing.sequence import pad_sequences
-
 import pickle
 
-# Set Streamlit page configuration
-st.set_page_config(page_title="News Category Classifier", page_icon="📰", layout="centered")
+# ✅ Enable full-width mode for Hugging Face
+st.set_page_config(page_title="News Category Classifier", page_icon="📰", layout="wide")
+
 
-# Function to set background image correctly
+# ✅ Function to set background image
 def set_background(image_path):
     if not os.path.exists(image_path):
         st.error(f"❌ Background image not found: {image_path}")
@@ -36,37 +36,42 @@ def set_background(image_path):
     bg_image_style = f"""
     <style>
         .stApp {{
-            background: url("data:image/jpg;base64,{encoded_img}") no-repeat center center fixed;
+            background-image: url("data:image/jpg;base64,{encoded_img}");
             background-size: cover;
+            background-repeat: no-repeat;
+            background-position: center;
+            background-attachment: fixed;
         }}
     </style>
     """
     st.markdown(bg_image_style, unsafe_allow_html=True)
 
-# Set background image
-set_background("Images/News image.jpg")
+# ✅ Set background image (Make sure the image is in the same directory)
+set_background("Images/News_image.jpg")
 
 
-# Initialize stopwords and lemmatizer
+# ✅ Initialize stopwords and lemmatizer
 stop_words = set(stopwords.words('english')).union({"pm"})
 lemmatizer = WordNetLemmatizer()
 
-# Preprocessing function
-def pre_process(x):
-    x = x.lower()
-    x = re.sub("<.*?>", "", x)  # Remove HTML tags
-    x = re.sub(r"http[s]?://\S+", "", x)  # Remove URLs
-    x = re.sub(r"[@#]\S+", "", x)  # Remove mentions and hashtags
-    x = re.sub(r"\_+", " ", x)  # Replace underscores with space
-    x = emoji.demojize(x)  # Convert emojis to text
-    x = re.sub(r":.*?:", "", x)  # Remove emoji text
-    x = re.sub(r"[^a-zA-Z0-9\s_]", "", x)  # Remove special characters
-    words = word_tokenize(x)
+
+# ✅ Text Preprocessing Function
+def pre_process(text):
+    text = text.lower()
+    text = re.sub("<.*?>", "", text)  # Remove HTML tags
+    text = re.sub("http[s]?://\\S+", "", text)  # Remove URLs
+    text = re.sub("[@#]\\S+", "", text)  # Remove mentions and hashtags
+    text = re.sub(r"\\_+", " ", text)  # Replace underscores with spaces
+    text = emoji.demojize(text)  # Convert emojis to text
+    text = re.sub(":.*?:", "", text)  # Remove emoji text
+    text = re.sub("[^a-zA-Z0-9\\s_]", "", text)  # Remove special characters
+    words = word_tokenize(text)
     words = [word for word in words if word not in stop_words]
-    x = " ".join([lemmatizer.lemmatize(word) for word in words])
-    return x
+    text = " ".join([lemmatizer.lemmatize(word) for word in words])
+    return text
+
 
-# Cache model loading to improve performance
+# ✅ Cache Model Loading for Performance
 @st.cache_resource
 def load_model():
     model_path = "news_model.keras"
@@ -75,16 +80,18 @@ def load_model():
 
     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 the models
+
+# ✅ Load the models
 model, vectorizer, label_encoder = load_model()
 
-# Prediction function
+
+# ✅ Prediction Function
 def predict_category(text):
     processed_text = [pre_process(text)]
     text_vectorized = pad_sequences(vectorizer(processed_text).numpy().tolist(), padding='pre', maxlen=82)
@@ -92,34 +99,35 @@ def predict_category(text):
     category_idx = np.argmax(prediction, axis=1)[0]
     return label_encoder.inverse_transform([category_idx])[0]
 
-# Streamlit UI
+
+# ✅ Streamlit UI Design
 st.markdown(
     """
     <style>
     .title {
         color: #ffffff;
-        font-size: 2.4em;
+        font-size: 2.8em;
         text-align: center;
         font-weight: 700;
         text-transform: uppercase;
         text-shadow: 2px 2px 8px rgba(0, 0, 0, 1.0);
-        padding: 10px;
+        padding: 15px;
     }
     .subtitle {
-        color: #ffff;
-        font-size: 1.3em;
+        color: #ffffff;
+        font-size: 1.5em;
         text-align: center;
         font-weight: 600;
         text-shadow: 1px 1px 6px rgba(0, 0, 0, 1.0);
-        padding: 5px;
+        padding: 10px;
     }
     .classify-button {
         background-color: #3498db;
         color: white;
-        font-size: 1.2em;
-        padding: 12px 24px;
+        font-size: 1.3em;
+        padding: 14px 28px;
         border: none;
-        border-radius: 8px;
+        border-radius: 10px;
         cursor: pointer;
         display: block;
         margin: 20px auto;
@@ -130,13 +138,13 @@ st.markdown(
     }
     .result-box {
         background: linear-gradient(135deg, #6284FF 30%, #FF0000 70%);
-        padding: 20px;
-        border-radius: 10px;
+        padding: 25px;
+        border-radius: 12px;
         text-align: center;
         margin-top: 30px;
         position: relative;
         overflow: hidden;
-        border: 2px solid transparent;
+        border: 3px solid transparent;
         background-clip: padding-box, border-box;
         border-image: linear-gradient(135deg, #6284FF 30%, #FF0000 70%);
         border-image-slice: 0;
@@ -148,7 +156,7 @@ st.markdown(
                     0px 10px 30px rgba(255, 0, 0, 0.8);
     }
     .result-text {
-        font-size: 1.8em;
+        font-size: 2em;
         color: #ffffff;
         font-weight: 900;
         text-shadow: 3px 3px 10px rgba(0, 0, 0, 0.5);
@@ -159,14 +167,14 @@ st.markdown(
     unsafe_allow_html=True
 )
 
-# Page title
+# ✅ Page Title
 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 Input
 user_input = st.text_area("Enter text here:", height=150, placeholder="Type your news text here...")
 
-# Button to analyze
+# ✅ Analyze Button
 if st.button("Analyze 🍿", key="analyze_button"): 
     if user_input.strip():
         category = predict_category(user_input)