Update app.py

app.py

@@ -1,12 +1,19 @@
+```python
 import streamlit as st
+# Import the `set_page_config` and `display_sidebar` functions from the `utils` module.
+# These functions are responsible for setting up the Streamlit app's page configuration and displaying the sidebar, respectively.
 from utils import set_page_config, display_sidebar
 import os
 
-# Set page configuration
+# Set the page configuration for the Streamlit app.
+# This function sets the app's title, icon, layout, and other parameters.
 set_page_config()
 
-# Title and description
+# Display the main title of the Streamlit app.
 st.title("CodeGen Hub")
+
+# Display the description of the Streamlit app using Markdown formatting.
+# This provides a more formatted and readable way to present the app's purpose and features.
 st.markdown("""
     Welcome to CodeGen Hub - A platform for training and using code generation models with Hugging Face integration.
     
@@ -20,52 +27,66 @@ st.markdown("""
     Navigate through the different sections using the sidebar menu.
 """)
 
-# Display sidebar
+# Display the sidebar for the Streamlit app.
+# The sidebar will contain navigation options and other functionality.
 display_sidebar()
 
-# Create the session state for storing information across app pages
+# Create session state variables to store information across app pages.
+# These variables will persist data between user interactions.
 if 'datasets' not in st.session_state:
+    # Initialize the 'datasets' dictionary in the session state.
+    # This dictionary will store information about the uploaded datasets.
     st.session_state.datasets = {}
 
 if 'trained_models' not in st.session_state:
+    # Initialize the 'trained_models' dictionary in the session state.
+    # This dictionary will store information about the trained models.
     st.session_state.trained_models = {}
 
 if 'training_logs' not in st.session_state:
+    # Initialize the 'training_logs' list in the session state.
+    # This list will store the training logs for the models.
     st.session_state.training_logs = []
 
 if 'training_progress' not in st.session_state:
+    # Initialize the 'training_progress' dictionary in the session state.
+    # This dictionary will store the progress information for the training jobs.
     st.session_state.training_progress = {}
 
-
-
-# Display getting started card
+# Display a "Getting Started" section with instructions for the user.
 st.subheader("Getting Started")
 col1, col2 = st.columns(2)
 
 with col1:
+    # Display the first set of instructions in the left column.
     st.info("""
         1. 📊 Start by uploading or selecting a Python code dataset in the **Dataset Management** section.
         2. 🛠️ Configure and train your model in the **Model Training** section.
     """)
     
 with col2:
+    # Display the second set of instructions in the right column.
     st.info("""
         3. 💡 Generate code predictions using your trained models in the **Code Generation** section.
         4. 🔄 Access your models on Hugging Face Hub for broader use.
     """)
 
-# Display platform statistics if available
+# Display platform statistics if available.
 st.subheader("Platform Statistics")
 col1, col2, col3 = st.columns(3)
 
 with col1:
+    # Display the number of datasets available.
     st.metric("Datasets Available", len(st.session_state.datasets))
     
 with col2:
+    # Display the number of trained models.
     st.metric("Trained Models", len(st.session_state.trained_models))
     
 with col3:
-    # Calculate active training jobs
+    # Calculate the number of active training jobs.
     active_jobs = sum(1 for progress in st.session_state.training_progress.values() 
                      if progress.get('status') == 'running')
+    # Display the number of active training jobs.
     st.metric("Active Training Jobs", active_jobs)
+```