import streamlit as st
from components.create_repository import create_repository_form

def render_repository_management():
    """Render the repository management page"""
    st.title("🗄️ Repository Management")

    st.markdown(
        """
        Create and manage your Hugging Face model repositories. 
        A repository is where you store model files, configuration, and documentation.
        """
    )

    # Create new repository section
    created, repo_id = create_repository_form()
    
    if created and repo_id:
        # If repository was created, navigate to model details page
        st.session_state.selected_model = repo_id
        st.session_state.page = "model_details"
        st.rerun()

    # Tips for repository creation
    with st.expander("Tips for creating a good repository"):
        st.markdown(
            """
        ### Best Practices for Model Repositories
        
        1. **Choose a descriptive name**
           - Use clear, lowercase names with hyphens (e.g., `bert-finetuned-sentiment`)
           - Avoid generic names like "test" or "model"
        
        2. **Add appropriate tags**
           - Tags help others discover your model
           - Include task types (e.g., "text-classification", "object-detection")
           - Add framework tags (e.g., "pytorch", "tensorflow")
        
        3. **Write a comprehensive model card**
           - Describe what the model does and how it was trained
           - Document model limitations and biases
           - Include performance metrics
           - Specify intended use cases
        
        4. **Organize your files**
           - Include all necessary files for model loading
           - Add configuration files
           - Include example scripts if helpful
        
        5. **License your model appropriately**
           - Choose an open-source license if possible
           - Document any usage restrictions
        """
        )