Update app.py

app.py

@@ -57,6 +57,7 @@ if st.session_state.selected_page == 'What is NLP?':
 elif st.session_state.selected_page == "NLP Lifecycle":
     lifecycle_option = sidebar.radio("Select NLP Lifecycle Step:", [
         "What is NLP Lifyecycle"
+        "Problem Definition"
         "Data Collection",
         "Text Preprocessing",
         "Text Representation",
@@ -65,42 +66,57 @@ elif st.session_state.selected_page == "NLP Lifecycle":
         "Deployment"
     ])
 
-    if lifecycle_option == "What is NLP Lifyecycle":
+    if lifecycle_option == "Overview of the NLP Life Cycle":
         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.
+        #### Overview of the NLP Life Cycle
+        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.
+    
+        - **How It Flows**:
+            - The process starts with identifying the problem and collecting the required text data.
+            - Then, the data is cleaned and prepared for analysis.
+            - Models are built and tested before being deployed for use.
+            - Regular checks and updates ensure the solution keeps working well.
+    
+        - **Flexible and Adaptive**:
+            - Since languages and data change (e.g., new words, trends), the process is repeated as needed.
+            - 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
+        2. Data Collection
+        3. Data Preprocessing
+        4. Feature Engineering
+        5. Model Selection and Training
+        6. Model Evaluation
+        7. Model Tuning
+        8. Deployment
+        9. Monitoring and Maintenance
         """)
+    elif lifecycle_option == "Problem Definition":
+    st.write("""
+    #### 1. Problem Definition
+    Problem definition is the first stage of the NLP lifecycle. It involves identifying the goal and understanding the problem that NLP can solve.
+    - **Key Questions**:
+        - What is the main objective of the analysis?
+        - What type of text data is being handled (e.g., reviews, social media, documents)?
+        - What output is expected (e.g., sentiment score, summary, classification)?
+    **Example**: Define whether the goal is to classify customer reviews as positive or negative or to extract key topics from product reviews.
+    """)
+
     
-    if lifecycle_option == "Data Collection":
+    elif lifecycle_option == "Data Collection":
         st.write("""
         #### 1. Data Collection
         Data collection is the first stage of the NLP lifecycle. It involves gathering relevant text data from various sources to analyze and process.