Update app.py

app.py

@@ -3,10 +3,38 @@ import streamlit as st
 # Apply custom styles using Streamlit's markdown
 st.markdown("""
     <style>
-    .main-title { color: #FF5733; font-size: 40px; font-weight: bold; text-align: center; }
-    .section-title { color: #2E86C1; font-size: 30px; font-weight: bold; margin-top: 20px; }
-    .sub-title { color: #27AE60; font-size: 24px; font-weight: bold; margin-top: 10px; }
-    .text { font-size: 18px; }
+    /* Link to Google Fonts */
+    @import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
+    
+    /* Custom styles */
+    .main-title { 
+        color: #FF5733; 
+        font-size: 40px; 
+        font-weight: bold; 
+        text-align: center; 
+        font-family: 'Roboto', sans-serif; /* Custom font */
+    }
+    
+    .section-title { 
+        color: #2E86C1; 
+        font-size: 30px; 
+        font-weight: bold; 
+        margin-top: 20px; 
+        font-family: 'Roboto', sans-serif; /* Custom font */
+    }
+    
+    .sub-title { 
+        color: #27AE60; 
+        font-size: 24px; 
+        font-weight: bold; 
+        margin-top: 10px; 
+        font-family: 'Roboto', sans-serif; /* Custom font */
+    }
+    
+    .text { 
+        font-size: 18px; 
+        font-family: 'Roboto', sans-serif; /* Custom font */
+    }
     </style>
 """, unsafe_allow_html=True)
 
@@ -89,64 +117,4 @@ elif selected_method == "TF-IDF":
 
     **Disadvantages:**
     - ❌ Still ignores word order  
-    - ❌ Does not capture deep semantics  
-    """)
-
-elif selected_method == "One-Hot Encoding":
-    st.markdown('<p class="sub-title">One-Hot Encoding</p>', unsafe_allow_html=True)
-    st.markdown("""
-    **Definition**: Represents words as binary vectors where each word has a unique position in a vocabulary.
-    """)
-    st.markdown("""
-    **Uses:**
-    - ✅ Simple NLP tasks  
-    - ✅ Word-level feature engineering  
-
-    **Advantages:**
-    - ✅ Simple to understand  
-    - ✅ Works well with small vocabulary sizes  
-
-    **Disadvantages:**
-    - ❌ Inefficient for large vocabularies  
-    - ❌ No information on word meaning  
-    """)
-
-elif selected_method == "Word Embeddings (Word2Vec)":
-    st.markdown('<p class="sub-title">Word Embeddings (Word2Vec)</p>', unsafe_allow_html=True)
-    st.markdown("""
-    **Definition**: Converts words into dense numerical vectors capturing semantic relationships.
-    """)
-    st.markdown("""
-    **Uses:**
-    - ✅ Machine translation  
-    - ✅ Speech recognition  
-    - ✅ Sentiment analysis  
-
-    **Advantages:**
-    - ✅ Captures semantic relationships  
-    - ✅ Works well for deep learning models  
-
-    **Disadvantages:**
-    - ❌ Requires large datasets to train  
-    - ❌ Computationally expensive  
-    """)
-
-    # Sample texts for Word2Vec model
-    from gensim.models import Word2Vec
-    texts = [
-        "Natural Language Processing is fascinating.",
-        "Natural Language Processing involves understanding human language.",
-        "The field of NLP is growing rapidly."
-    ]
-    model = Word2Vec(sentences=[text.split() for text in texts], vector_size=100, window=5, min_count=1, workers=4)
-    word_vectors = model.wv
-    word = 'natural'
-    if word in word_vectors:
-        st.markdown(f'Word2Vec Representation of "{word}":')
-        st.write(word_vectors[word])
-    else:
-        st.markdown(f'Word "{word}" not found in the vocabulary.')
-
-# Footer
-st.markdown('<hr>', unsafe_allow_html=True)
-st.markdown('<p class="text" style="text-align:center;">Developed with ❤️ using Streamlit for NLP enthusiasts.</p>', unsafe_allow_html=True)
+    - ❌ Does not capture deep