Update app.py

app.py

@@ -56,6 +56,7 @@ if st.session_state.selected_page == 'What is NLP?':
 # Content for NLP Lifecycle
 elif st.session_state.selected_page == "NLP Lifecycle":
     lifecycle_option = sidebar.radio("Select NLP Lifecycle Step:", [
+        "What is NLP Lifyecycle"
         "Data Collection",
         "Text Preprocessing",
         "Text Representation",
@@ -63,6 +64,41 @@ elif st.session_state.selected_page == "NLP Lifecycle":
         "Evaluation",
         "Deployment"
     ])
+
+    if lifecycle_option == "What is NLP Lifyecycle":
+        st.write("""
+        #### The NLP life cycle is a structured process for building, using, and maintaining systems that work with human language.
+             It turns unstructured text into meaningful insights or automated actions. This process ensures continuous improvement and adapts to real-world needs.
+
+             Overview of the NLP Life Cycle
+             How It Flows:
+             1.The process starts with identifying the problem and collecting the required text data.
+             2.Then, the data is cleaned and prepared for analysis.
+             3.Models are built and tested before being deployed for use.
+             4.Regular checks and updates ensure the solution keeps working well.
+             Flexible and Adaptive:
+             1.Since languages and data change (e.g., new words, trends), the process is repeated as needed.
+             2.Models may need updates or retraining to stay accurate.
+             Combines Different Fields:
+             The process involves skills from language studies, programming, and data analysis to make sure language is understood effectively.
+             Designed for Practical Use:
+             The goal is to create solutions that can handle tasks like analyzing text, identifying emotions, powering chatbots, or translating languages accurately and efficiently.
+             Key Challenges Solved:
+             -Managing the complexity of language (e.g., meaning, structure).
+             -Working with large and messy datasets.
+             -Handling multiple languages and specific industries.
+             -Ensuring solutions are fast and efficient.
+             - **Steps in the NLP Life Cycle**:
+                  1.Problem Definition: Identify the problem and define the objective.
+                  2. Data Collection: Gather relevant text data from various sources.
+                  3.Data Preprocessing: Clean and prepare the data (e.g., remove noise, tokenize).
+                  4.Feature Engineering: Convert text into structured data formats.
+                  5.Model Selection and Training: Choose and train the appropriate NLP model.
+                  6.Model Evaluation: Assess the model's performance using suitable metrics.
+                  7.Model Tuning: Optimize the model for better accuracy and efficiency.
+                  8.Deployment: Integrate the trained model into real-world applications.
+                  9.Monitoring and Maintenance: Continuously monitor and update the model to handle new data patterns.
+        """)
     
     if lifecycle_option == "Data Collection":
         st.write("""