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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)
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