Upload 14 files

app.py

@@ -1,97 +1,71 @@
-
 import streamlit as st
+from utils import set_page_config, display_sidebar
+import os
 
-from utils import set_page_config
-
-# Set the Streamlit page configuration
+# Set page configuration
 set_page_config()
 
-# Display main app title
+# Title and description
 st.title("CodeGen Hub")
-
-# 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 sidebar
+display_sidebar()
 
-# Sidebar navigation using session state
-def navigate(page):
-    st.session_state["current_page"] = page
+# Create the session state for storing information across app pages
+if 'datasets' not in st.session_state:
+    st.session_state.datasets = {}
 
+if 'trained_models' not in st.session_state:
+    st.session_state.trained_models = {}
 
-# 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 'training_logs' not in st.session_state:
+    st.session_state.training_logs = []
 
-for key, value in session_defaults.items():
-    if key not in st.session_state:
-        st.session_state[key] = value
+if 'training_progress' not in st.session_state:
+    st.session_state.training_progress = {}
 
-# 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")
 
-# 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)
 
-    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.
-        """)
+# Display getting started card
+st.subheader("Getting Started")
+col1, col2 = st.columns(2)
 
-    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.
-        """)
+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 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 dynamically
+# Display platform statistics if available
 st.subheader("Platform Statistics")
 col1, col2, col3 = st.columns(3)
 
 with col1:
-    st.metric("📂 Datasets Available", len(st.session_state.get("datasets",
-                                                               {})))
-
+    st.metric("Datasets Available", len(st.session_state.datasets))
+    
 with col2:
-    st.metric("📦 Trained Models",
-              len(st.session_state.get("trained_models", {})))
-
+    st.metric("Trained Models", len(st.session_state.trained_models))
+    
 with col3:
-    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)
+    # Calculate active training 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)