Update app.py

app.py

@@ -4,7 +4,6 @@ import numpy as np
 import re
 import emoji
 import os
-
 import nltk
 from nltk.tokenize import word_tokenize
 from nltk.corpus import stopwords
@@ -21,8 +20,7 @@ from keras.preprocessing.sequence import pad_sequences
 import pickle
 
 # ✅ Enable full-width mode for Hugging Face
-st.set_page_config(page_title="Intelligent News Classifier", page_icon="🧠 ", layout="wide")
-
+st.set_page_config(page_title="Intelligent News Classifier", page_icon="🧠", layout="wide")
 
 # ✅ Function to set background image
 def set_background(image_path):
@@ -46,15 +44,13 @@ def set_background(image_path):
     """
     st.markdown(bg_image_style, unsafe_allow_html=True)
 
-# ✅ Set background image (Make sure the image is in the same directory)
+# ✅ Set background image
 set_background("Images/picture.png")
 
-
 # ✅ Initialize stopwords and lemmatizer
 stop_words = set(stopwords.words('english')).union({"pm"})
 lemmatizer = WordNetLemmatizer()
 
-
 # ✅ Text Preprocessing Function
 def pre_process(text):
     text = text.lower()
@@ -70,7 +66,6 @@ def pre_process(text):
     text = " ".join([lemmatizer.lemmatize(word) for word in words])
     return text
 
-
 # ✅ Cache Model Loading for Performance
 @st.cache_resource
 def load_model():
@@ -86,11 +81,9 @@ def load_model():
 
     return model, vectorizer, label_encoder
 
-
 # ✅ Load the models
 model, vectorizer, label_encoder = load_model()
 
-
 # ✅ Prediction Function
 def predict_category(text):
     processed_text = [pre_process(text)]
@@ -99,10 +92,18 @@ def predict_category(text):
     category_idx = np.argmax(prediction, axis=1)[0]
     return label_encoder.inverse_transform([category_idx])[0]
 
+# ✅ Category Color Mapping
+category_colors = {
+    "Sports": "#27ae60",  # Green
+    "Politics": "#2980b9",  # Blue
+    "Entertainment": "#8e44ad",  # Purple
+    "Technology": "#e67e22",  # Orange
+    "Business": "#c0392b",  # Red
+    "Default": "#ffffff"  # White
+}
 
 # ✅ Streamlit UI Design
-st.markdown(
-    """
+st.markdown("""
     <style>
     .title {
         color: #ffffff;
@@ -121,51 +122,17 @@ st.markdown(
         text-shadow: 1px 1px 6px rgba(0, 0, 0, 1.0);
         padding: 10px;
     }
-    .classify-button {
-        background-color: #3498db;
-        color: white;
-        font-size: 1.3em;
-        padding: 14px 28px;
-        border: none;
-        border-radius: 10px;
-        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: 25px;
         border-radius: 12px;
         text-align: center;
         margin-top: 30px;
-        position: relative;
-        overflow: hidden;
-        border: 3px 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: 2em;
-        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
-)
+    """, unsafe_allow_html=True)
 
 # ✅ Page Title
 st.markdown("<div class='title'>🧠 Intelligent News Classifier</div>", unsafe_allow_html=True)
@@ -175,9 +142,17 @@ st.markdown("<div class='subtitle'>Find out what type of news you're reading!.</
 user_input = st.text_area("Enter text here:", height=150, placeholder="Type your news text here...")
 
 # ✅ Analyze Button
-if st.button("Analyze 🍿", key="analyze_button"): 
+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)
+        color = category_colors.get(category, category_colors["Default"])
+        st.markdown(
+            f"""
+            <div class='result-box' style='color: {color};'>
+                🗂️ Predicted Category: <strong>{category}</strong>
+            </div>
+            """,
+            unsafe_allow_html=True
+        )
     else:
         st.warning("⚠️ Please enter some text to analyze.")