Update pages/Introduction.py

pages/Introduction.py

@@ -15,7 +15,7 @@ st.markdown("<p>NLP encompasses a wide array of techniques that aimed at enablin
 
 st.write("**1. Text Processing and Preprocessing In NLP**")
 st.write("Before performing any analysis or modeling, raw text data must be cleaned and prepared.")
-st.write("**a. Tokenization**")
+st.markdown('<p style="color:lightcoral;"><b>a. Tokenization</b></p>', unsafe_allow_html=True)
 st.write("Splits text into smaller units like words or sentences.")
 st.write("**Types:**")
 
@@ -25,21 +25,22 @@ st.write("Example: _'I love NLP'_ → [‘I’, ‘love’, ‘NLP’]")
 st.write("**(ii) Sentence Tokenization:** Breaking text into sentences.")
 st.write("Example: _'I love NLP. It’s fascinating!'_ → [‘I love NLP.’, ‘It’s fascinating!’]")
 
-st.write("**b. Stopword Removal**")
+st.markdown('<p style="color:lightcoral;"><b>b. Stopword Removal</b></p>', unsafe_allow_html=True)
 st.write("Removes common words like “the,” “and,” “is” that do not contribute much to analysis.")
 
-st.write("**c. Stemming and Lemmatization**")
+
+st.markdown('<p style="color:lightcoral;"><b>c. Stemming and Lemmatization</b></p>', unsafe_allow_html=True)
 st.write("Stemming: Reduces words to their base or root form by chopping off suffixes (may not produce valid words).")
 st.write("Example: _“running” _ → “run”")
 
 st.write("Lemmatization: Converts words to their base form using vocabulary and grammar")
 st.write("Example: _“good” _ → “better”")
 
-st.write("**d. Part-of-Speech (POS) Tagging**")
+st.markdown('<p style="color:lightcoral;"><b>d. Part-of-Speech (POS) Tagging</b></p>', unsafe_allow_html=True)
 st.write("Labels words with their grammatical roles (noun, verb, adjective, etc.)")
 st.write("Example: _The cat sleeps”_ → [“The/DET”, “cat/NOUN”, “sleeps/VERB”]")
 
-st.write("**e. Named Entity Recognition (NER)**")
+st.markdown('<p style="color:lightcoral;"><b>e. Named Entity Recognition (NER)</b></p>', unsafe_allow_html=True)
 st.write("Identifies and classifies entities in text (e.g., names, dates, locations)")
 st.write("Example: _ “Barack Obama was born in Hawaii. _ ” → [Barack Obama: PERSON, Hawaii: LOCATION]")