Update app.py

app.py

@@ -5,7 +5,7 @@ st.title("NLP Theory Blog")
 
 # Sidebar for navigation
 st.sidebar.title("Navigation")
-pages = ["Introduction to NLP", "NLP Techniques"]
+pages = ["Introduction to NLP", "NLP Techniques", "NLP Life Cycle"]
 page = st.sidebar.radio("Go to:", pages)
 
 # Content for each page
@@ -41,14 +41,103 @@ elif page == "NLP Techniques":
         These techniques are often used in combination to build sophisticated NLP applications.
     """)
 
-# Pagination buttons
-if st.button("Previous Page"):
-    if page == "NLP Techniques":
-        st.experimental_set_query_params(page=pages[0])
+elif page == "NLP Life Cycle":
+    # NLP Life Cycle Page with Sub-navigation
+    st.header("NLP Life Cycle")
 
-if st.button("Next Page"):
-    if page == "Introduction to NLP":
-        st.experimental_set_query_params(page=pages[1])
+    # Sidebar navigation for NLP Life Cycle sub-pages
+    life_cycle_pages = ["Problem Definition", "Data Collection", "Data Preprocessing", "Feature Engineering", 
+                        "Modeling", "Model Evaluation", "Model Optimization", "Model Deployment", 
+                        "Post-Deployment Maintenance", "End-User Interaction"]
+    life_cycle_page = st.sidebar.radio("Select a step in the NLP Life Cycle:", life_cycle_pages)
+
+    # Content for each sub-page of NLP Life Cycle
+    if life_cycle_page == "Problem Definition":
+        st.write("""
+            In this phase, the problem you're trying to solve with NLP is defined. Examples include:
+            - Sentiment analysis
+            - Named entity recognition (NER)
+            - Text classification
+            - Machine translation
+            - Language generation
+        """)
+
+    elif life_cycle_page == "Data Collection":
+        st.write("""
+            Gather relevant textual data. Sources include:
+            - Web scraping (e.g., using BeautifulSoup or Scrapy)
+            - APIs (e.g., Twitter API)
+            - Pre-existing datasets (e.g., Kaggle, UCI repositories)
+            - User-generated content (e.g., reviews, social media)
+        """)
+
+    elif life_cycle_page == "Data Preprocessing":
+        st.write("""
+            Prepare the data for modeling by performing tasks such as:
+            - Text cleaning (removing unnecessary characters, punctuation)
+            - Tokenization (splitting text into words/sentences)
+            - Stopword removal
+            - Stemming or lemmatization
+            - Part-of-speech tagging
+        """)
+
+    elif life_cycle_page == "Feature Engineering":
+        st.write("""
+            Convert text data into numerical form for model consumption:
+            - Bag of Words (BoW)
+            - TF-IDF (Term Frequency-Inverse Document Frequency)
+            - Word embeddings (Word2Vec, GloVe)
+            - Contextual embeddings (BERT, GPT)
+        """)
+
+    elif life_cycle_page == "Modeling":
+        st.write("""
+            Train machine learning or deep learning models using the preprocessed text data:
+            - Supervised learning (e.g., Logistic Regression, SVM)
+            - Unsupervised learning (e.g., K-means clustering)
+            - Deep learning (e.g., RNNs, LSTMs, BERT)
+        """)
+
+    elif life_cycle_page == "Model Evaluation":
+        st.write("""
+            Evaluate the model's performance using metrics like:
+            - Accuracy
+            - Precision, Recall, F1-Score
+            - Confusion Matrix
+            - Cross-validation
+        """)
+
+    elif life_cycle_page == "Model Optimization":
+        st.write("""
+            Improve model performance by:
+            - Hyperparameter tuning (e.g., grid search)
+            - Regularization (e.g., L2 regularization, dropout)
+            - Ensemble methods (e.g., Random Forest, XGBoost)
+        """)
+
+    elif life_cycle_page == "Model Deployment":
+        st.write("""
+            Deploy the trained model into production:
+            - Expose the model via APIs (using Flask or FastAPI)
+            - Integrate with applications (e.g., chatbots, recommendation systems)
+            - Monitor the model's performance
+        """)
+
+    elif life_cycle_page == "Post-Deployment Maintenance":
+        st.write("""
+            Keep the model updated with new data:
+            - Retraining the model with fresh data
+            - Error analysis and model refinement
+            - Collecting user feedback for continuous improvement
+        """)
+
+    elif life_cycle_page == "End-User Interaction":
+        st.write("""
+            Present the model's results in an understandable way:
+            - Data visualization (e.g., charts, word clouds)
+            - Interactive dashboards (e.g., using Streamlit or Dash)
+            - Interface design (e.g., web or mobile apps)
+        """)
 
 # Footer
 st.sidebar.write("---")