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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
"""
)
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