Update app.py

app.py

@@ -1,91 +1,92 @@
 
 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 the page configuration for the Streamlit app.
-# This function sets the app's title, icon, layout, and other parameters.
+# Set the Streamlit page configuration
 set_page_config()
 
-# Display the main title of the Streamlit app.
+# Display main app title
 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.
+# App description with markdown formatting
 st.markdown("""
     Welcome to CodeGen Hub - A platform for training and using code generation models with Hugging Face integration.
     
     ### Core Features:
-    - Upload and preprocess Python code datasets for model training
-    - Configure and train models with customizable parameters
-    - Generate code predictions using trained models through an interactive interface
-    - Monitor training progress with visualizations and detailed logs
-    - Seamless integration with Hugging Face Hub for model management
+    - 📂 Upload and preprocess Python code datasets for model training
+    - 🎛️ Configure and train models with customizable parameters
+    - 🤖 Generate code predictions using trained models through an interactive interface
+    - 📊 Monitor training progress with visualizations and detailed logs
+    - 🔗 Seamless integration with Hugging Face Hub for model management
     
     Navigate through the different sections using the sidebar menu.
 """)
 
-# Display the sidebar for the Streamlit app.
-# The sidebar will contain navigation options and other functionality.
-display_sidebar()
+# Sidebar navigation using session state
+def navigate(page):
+    st.session_state["current_page"] = page
 
-# 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 = {}
+# Initialize session state variables using a loop
+session_defaults = {
+    "datasets": {},        # Stores uploaded datasets
+    "trained_models": {},  # Stores trained model details
+    "training_logs": [],   # Stores training logs
+    "training_progress": {},  # Tracks active training jobs
+    "current_page": "home"  # Default landing page
+}
 
-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 = {}
+for key, value in session_defaults.items():
+    if key not in st.session_state:
+        st.session_state[key] = value
 
-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 = []
+# Display sidebar with navigation buttons
+with st.sidebar:
+    st.header("Navigation")
+    if st.button("🏗️ Dataset Management"):
+        navigate("dataset_management")
+    if st.button("🎯 Model Training"):
+        navigate("model_training")
+    if st.button("🔮 Code Generation"):
+        navigate("code_generation")
 
-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 = {}
+# Render content dynamically based on session state
+if st.session_state["current_page"] == "dataset_management":
+    st.subheader("Dataset Management")
+    st.write("Upload and manage your datasets here.")
+elif st.session_state["current_page"] == "model_training":
+    st.subheader("Model Training")
+    st.write("Configure and train your models.")
+elif st.session_state["current_page"] == "code_generation":
+    st.subheader("Code Generation")
+    st.write("Generate predictions using your trained models.")
+else:
+    st.subheader("Getting Started")
+    col1, col2 = st.columns(2)
 
-# Display a "Getting Started" section with instructions for the user.
-st.subheader("Getting Started")
-col1, col2 = st.columns(2)
+    with col1:
+        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 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.
-    """)
+    with col2:
+        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 dynamically
 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))
-    
+    st.metric("📂 Datasets Available", len(st.session_state.get("datasets", {})))
+
 with col2:
-    # Display the number of trained models.
-    st.metric("Trained Models", len(st.session_state.trained_models))
-    
+    st.metric("📦 Trained Models", len(st.session_state.get("trained_models", {})))
+
 with col3:
-    # 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)
+    active_jobs = sum(1 for progress in st.session_state["training_progress"].values() 
+                      if progress.get("status") == "running")
+    st.metric("🚀 Active Training Jobs", active_jobs)