Update app.py

app.py

@@ -307,7 +307,7 @@ elif selected_page == "🔄 Lifecycle of NLP":
 elif selected_page == "⚙️ NLP Techniques":
     st.header("⚙️ NLP Techniques")
     if selected_subpoint:
-        if selected_subpoint == "Tokenization":
+        if selected_subpoint == " Tokenization":
             st.write("""
                 Breaking down text into smaller units such as words or sentences to make it manageable for analysis.
                 **Example:**
@@ -315,7 +315,7 @@ elif selected_page == "⚙️ NLP Techniques":
                 - Word Tokens: `["Artificial", "Intelligence", "is", "fascinating", "."]`
                 - Sentence Tokens: `["Artificial Intelligence is fascinating."]`
                 """)
-        elif selected_subpoint == "Stemming":
+        elif selected_subpoint == " Stemming":
             st.write("### 🌱 Stemming")
             st.write("""
                 Stemming reduces words to their root form by removing prefixes or suffixes, often resulting in a non-grammatical base.
@@ -530,7 +530,7 @@ elif selected_page == "⚙️ NLP Techniques":
                 ```
             """)
     
-        elif selected_subpoint == "Part-of-Speech (POS) Tagging":
+        elif selected_subpoint == " Part-of-Speech (POS) Tagging":
             st.write("### 🖇️ Part-of-Speech (POS) Tagging")
             st.write("""
                 Assigning grammatical labels to each word in a sentence, indicating its role in context.
@@ -539,7 +539,7 @@ elif selected_page == "⚙️ NLP Techniques":
                 - Output: `["Birds (NOUN)", "fly (VERB)", "high (ADJ)"]`
             """)
     
-        elif selected_subpoint == "Named Entity Recognition (NER)":
+        elif selected_subpoint == " Named Entity Recognition (NER)":
             st.write("### 🌍 Named Entity Recognition (NER)")
             st.write("""
                 Detecting and categorizing entities like names, dates, and locations from text.
@@ -548,7 +548,7 @@ elif selected_page == "⚙️ NLP Techniques":
                 - Output: `["Tesla (ORGANIZATION)", "Elon Musk (PERSON)", "California (LOCATION)"]`
             """)
     
-        elif selected_subpoint == "Sentiment Analysis":
+        elif selected_subpoint == " Sentiment Analysis":
             st.write("### 🎭 Sentiment Analysis")
             st.write("""
                 Classifying the emotional tone of a text into categories such as positive, negative, or neutral.