Update app.py

app.py

@@ -18,20 +18,23 @@ if sidebar_option != st.session_state.selected_page:
     st.session_state.selected_page = sidebar_option
 
 # Dynamically update the title based on the selected option
+def set_title(title, color="black"):
+    st.markdown(f"<h1 style='text-align: center; color: {color};'>{title}</h1>", unsafe_allow_html=True)
+
 if st.session_state.selected_page == 'What is NLP?':
-    st.markdown("<h1 style='text-align: center; color: black;'>Natural Language Processing (NLP)</h1>", unsafe_allow_html=True)
+    set_title('Natural Language Processing (NLP)')
     
 elif st.session_state.selected_page == 'NLP Lifecycle':
-    st.markdown("<h1 style='text-align: center; color: balck;'>'🔄 Natural Language Processing (NLP) Lifecycle'</h1>", unsafe_allow_html=True)
+    set_title('🔄 Natural Language Processing (NLP) Lifecycle')
     if sidebar_option == 'Problem Definition':
         st.title('🔧 Steps in the Natural Language Processing (NLP) lifecycle:')
+
 elif st.session_state.selected_page == 'NLP Techniques':
-    st.markdown("<h1 style='text-align: center; color: black;'>'⚙️ Techniques in Natural Language Processing (NLP)'</h1>", unsafe_allow_html=True)
-    
+    set_title('⚙️ Techniques in Natural Language Processing (NLP)')
 
 # Content for "What is NLP?"
 if st.session_state.selected_page == 'What is NLP?':
-    st.markdown("<h2 style='text-align: center; color: black;'>'Introduction'</h2>", unsafe_allow_html=True)
+    st.markdown("<h2 style='text-align: center; color: black;'>Introduction</h2>", unsafe_allow_html=True)
     st.write("""
     #### 🤖 What is NLP?
     Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and human (natural) languages. The goal of NLP is to enable machines to understand, interpret, and generate human language in a way that is meaningful.
@@ -184,22 +187,20 @@ elif st.session_state.selected_page == "NLP Lifecycle":
         - **Precision**: The percentage of relevant instances among the retrieved instances.
         - **Recall**: The percentage of relevant instances that were retrieved.
         - **F1-score**: The harmonic mean of precision and recall.
-    
-        **Example**: If a sentiment analysis model predicts positive sentiment in 80 out of 100 reviews, its accuracy is 80%.
+
+        **Example**: Evaluating a sentiment analysis model using accuracy and F1-score on a test dataset.
         """)
 
     elif lifecycle_option == "Deployment":
         st.write("""
         #### 🚀 7. Deployment
-        The final step is deploying the model for real-time use. This involves integrating it into a system or application where it can process live data.
-        - **Real-time Applications**: Chatbots, sentiment analysis for social media monitoring, text summarization for news.
-        - **Maintenance**: Continuously monitor the model to ensure its performance remains high. Updates might be necessary if the language evolves or new data emerges.
-        
-        **Example**: Deploying a chatbot to answer customer inquiries based on historical support tickets.
-        """)
-
-# Content for NLP Techniques
+        Once the model is evaluated and tuned, it is deployed into production where it can be used by end users. Deployment involves:
+        - **Integration** with web applications, chatbots, or other tools.
+        - **API Development**: Exposing the model through an API for real-time predictions.
+        - **Continuous Monitoring**: Tracking the model’s performance and retraining it as needed.
 
+        **Example**: Deploying a sentiment analysis model in a customer service chatbot that analyzes customer inquiries in real time.
+        """)
 
 
 # Content for "NLP Techniques"
@@ -221,7 +222,7 @@ elif st.session_state.selected_page == "NLP Techniques":
         st.write("""
         ### ⚙️ Techniques in NLP
         NLP uses a variety of techniques to process and analyze text data. Some of the most common techniques include:
-    
+        ### 🛠️ Common NLP Techniques
         1. **Tokenization**: Breaking down text into smaller units (e.g., words, sentences).
         2. **Part-of-Speech (POS) Tagging**: Identifying the grammatical roles of words in a sentence (e.g., noun, verb, adjective).
         3. **Named Entity Recognition (NER)**: Identifying entities such as names, dates, locations, etc.