Delete app.py

app.py

@@ -1,71 +0,0 @@
-import streamlit as st
-from utils import set_page_config, display_sidebar
-import os
-
-# Set page configuration
-set_page_config()
-
-# Title and description
-st.title("CodeGen Hub")
-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
-    
-    Navigate through the different sections using the sidebar menu.
-""")
-
-# Display sidebar
-display_sidebar()
-
-# 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 = {}
-
-if 'training_logs' not in st.session_state:
-    st.session_state.training_logs = []
-
-if 'training_progress' not in st.session_state:
-    st.session_state.training_progress = {}
-
-
-
-# Display getting started card
-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 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
-st.subheader("Platform Statistics")
-col1, col2, col3 = st.columns(3)
-
-with col1:
-    st.metric("Datasets Available", len(st.session_state.datasets))
-    
-with col2:
-    st.metric("Trained Models", len(st.session_state.trained_models))
-    
-with col3:
-    # 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)