Update app.py

app.py

@@ -60,18 +60,18 @@ 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:", [
-        "Overview of the NLP Life Cycle 🌐",
-        "Problem Definition 🔧",
-        "Data Collection 📊",
-        "Simple EDA 📈",
-        "Data Preprocessing 🧹",
-        "Feature Engineering 📝",
-        "Model Training 🏋️‍♂️",
-        "Evaluation 🏅",
-        "Deployment 🚀"
+        "🌐Overview of the NLP Life Cycle",
+        "🎯 Problem Definition",
+        "📊Data Collection",
+        "📈Simple EDA",
+        "🧹Data Preprocessing ",
+        "📝Feature Engineering",
+        "🏋️‍♂️Model Training",
+        "🏅Evaluation",
+        "🚀Deployment"
     ])
 
-    if lifecycle_option == "Overview of the NLP Life Cycle":
+    if lifecycle_option == "Overview of the NLP Life Cycle 🌐":
         st.write("""
         #### 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.
@@ -111,7 +111,7 @@ elif st.session_state.selected_page == "NLP Lifecycle 🔄":
             10. **Monitoring and Maintenance** 🛠️
             """)
 
-    elif lifecycle_option == "Problem Definition":
+    elif lifecycle_option == "🎯 Problem Definition":
         st.write("""
         #### 🔧 1. Problem Definition
         - The first step in the NLP lifecycle is defining the problem. This means understanding the goal and figuring out how NLP can help solve the problem.
@@ -124,7 +124,7 @@ elif st.session_state.selected_page == "NLP Lifecycle 🔄":
         **Example of a problem statement**: The goal could be to classify customer reviews as either positive or negative, or to find the main topics in product reviews.
         """)
 
-    elif lifecycle_option == "Data Collection":
+    elif lifecycle_option == "📊Data Collection":
         st.write("""
            #### 📚 2. Data Collection
            Data collection is the second step in the NLP lifecycle. It involves gathering data from various sources based on the problem statement, so it can be analyzed and processed.
@@ -182,7 +182,7 @@ elif st.session_state.selected_page == "NLP Lifecycle 🔄":
         """)
     
     
-    elif lifecycle_option == "Simple EDA":
+    elif lifecycle_option == "📈Simple EDA":
         st.write("""
             #### 🔍 3. Simple EDA
            #### Simple Exploratory Data Analysis (Simple EDA)
@@ -217,7 +217,7 @@ elif st.session_state.selected_page == "NLP Lifecycle 🔄":
     
     
     
-    elif lifecycle_option == "Data Preprocessing":
+    elif lifecycle_option == "🧹Data Preprocessing ":
         st.write("""
         #### 🧹 4. Text Preprocessing
         Text preprocessing prepares raw text for further analysis. This stage involves cleaning and transforming the data into a structured format that machine learning models can understand.
@@ -275,7 +275,7 @@ elif st.session_state.selected_page == "NLP Lifecycle 🔄":
         """)
     
     
-    elif lifecycle_option == "Feature Engineering":
+    elif lifecycle_option == "📝Feature Engineering":
         st.write("""
         #### 📝 5. Text Representation
         After preprocessing, the text data needs to be converted into a numerical format for use in machine learning models. There are several methods for text representation:
@@ -288,7 +288,7 @@ elif st.session_state.selected_page == "NLP Lifecycle 🔄":
         - Vector: [1, 1, 1] (word frequency representation)
         """)
     
-    elif lifecycle_option == "Model Training":
+    elif lifecycle_option == "🏋️‍♂️Model Training":
         st.write("""
         #### 🏋️‍♂️ 6. Model Training
         In the model training stage, machine learning algorithms are trained on the preprocessed and represented text data. The choice of model depends on the task:
@@ -299,7 +299,7 @@ elif st.session_state.selected_page == "NLP Lifecycle 🔄":
         **Example**: Training a Naive Bayes classifier to categorize news articles into topics such as "Sports", "Politics", etc.
         """)
     
-    elif lifecycle_option == "Evaluation":
+    elif lifecycle_option == "🏅Evaluation":
         st.write("""
         #### 🏅 7. Evaluation
         After training the model, it's important to evaluate its performance using metrics such as accuracy, precision, recall, and F1-score.
@@ -311,7 +311,7 @@ elif st.session_state.selected_page == "NLP Lifecycle 🔄":
         **Example**: Evaluating a sentiment analysis model using accuracy and F1-score on a test dataset.
         """)
     
-    elif lifecycle_option == "Deployment":
+    elif lifecycle_option == "🚀Deployment":
         st.write("""
         #### 🚀 8. Deployment
         Once the model is evaluated and tuned, it is deployed into production where it can be used by end users. Deployment involves: