api/modules/database/init/init_school.py

import os
from modules.logger_tool import initialise_logger
logger = initialise_logger(__name__, os.getenv("LOG_LEVEL"), os.getenv("LOG_PATH"), 'default', True)
from modules.database.schemas.nodes.schools.schools import SchoolNode
from modules.database.supabase.utils.client import SupabaseServiceRoleClient, CreateBucketOptions
import modules.database.init.init_school_timetable as init_school_timetable
import modules.database.tools.neontology_tools as neon

def create_school_buckets(school_id: str, school_type: str, school_name: str, admin_access_token: str) -> dict:
    """Create storage buckets for a school
    Args:
        school_id: The unique identifier for the school
        school_type: The type of school (e.g., 'development')
        school_name: The display name of the school
        admin_access_token: The admin access token for Supabase operations
    Returns:
        Dictionary containing results of bucket creation operations
    """
    logger.info(f"Creating storage buckets for school {school_name} ({school_type}/{school_id})")
    
    storage_client = SupabaseServiceRoleClient.for_admin(admin_access_token)
    base_path = f"cc.institutes.{school_type}.{school_id}"
    
    buckets = [
        # Main school buckets
        {
            "id": f"{base_path}.public",
            "options": CreateBucketOptions(
                name=f"{school_type.title()} School Files - {school_name} - Public Files",
                public=True,
                file_size_limit=50 * 1024 * 1024,
                allowed_mime_types=[
                    'image/*', 'video/*', 'application/pdf',
                    'application/msword', 'application/vnd.openxmlformats-officedocument.*',
                    'text/plain', 'text/csv', 'application/json'
                ]
            )
        },
        {
            "id": f"{base_path}.private",
            "options": CreateBucketOptions(
                name=f"{school_type.title()} School Files - {school_name} - Private Files",
                public=False,
                file_size_limit=50 * 1024 * 1024,
                allowed_mime_types=[
                    'image/*', 'video/*', 'application/pdf',
                    'application/msword', 'application/vnd.openxmlformats-officedocument.*',
                    'text/plain', 'text/csv', 'application/json'
                ]
            )
        },
        # Curriculum buckets
        {
            "id": f"{base_path}.curriculum.public",
            "options": CreateBucketOptions(
                name=f"{school_type.title()} School Files - {school_name} - Curriculum Public Files",
                public=True,
                file_size_limit=50 * 1024 * 1024,
                allowed_mime_types=[
                    'image/*', 'video/*', 'application/pdf',
                    'application/msword', 'application/vnd.openxmlformats-officedocument.*',
                    'text/plain', 'text/csv', 'application/json'
                ]
            )
        },
        {
            "id": f"{base_path}.curriculum.private",
            "options": CreateBucketOptions(
                name=f"{school_type.title()} School Files - {school_name} - Curriculum Private Files",
                public=False,
                file_size_limit=50 * 1024 * 1024,
                allowed_mime_types=[
                    'image/*', 'video/*', 'application/pdf',
                    'application/msword', 'application/vnd.openxmlformats-officedocument.*',
                    'text/plain', 'text/csv', 'application/json'
                ]
            )
        }
    ]
    
    results = {}
    for bucket in buckets:
        try:
            result = storage_client.create_bucket(bucket["id"], bucket["options"])
            results[bucket["id"]] = {
                "status": "success",
                "result": result
            }
            logger.info(f"Successfully created bucket {bucket['id']}")
        except Exception as e:
            logger.error(f"Error creating school bucket {bucket['id']}: {str(e)}")
            results[bucket["id"]] = {
                "status": "error",
                "error": str(e)
            }
    
    return results

def create_school(db_name: str, id: str, name: str, website: str, school_type: str, is_public: bool = True, school_node: SchoolNode | None = None, dataframes=None):
    if not name or not id or not website or not school_type:
        logger.error("School name, id, website and school_type are required to create a school.")
        raise ValueError("School name, id, website and school_type are required to create a school.")
    
    logger.info(f"Initialising neo4j connection...")
    neon.init_neontology_connection()

    # Create School Node if not provided
    if not school_node:
        if is_public:
            school_node = SchoolNode(
                unique_id=f'School_{id}',
                tldraw_snapshot="",
                id=id,
                name=name,
                website=website,
                school_type=school_type
            )
        else:
            # Create private school node with default values
            school_node = SchoolNode(
                unique_id=f'School_{id}',
                tldraw_snapshot="",
                id=id,
                name=name,
                website=website,
                school_type=school_type,
                establishment_number="0000",
                establishment_name=name,
                establishment_type="Default",
                establishment_status="Open",
                phase_of_education="All",
                statutory_low_age=11,
                statutory_high_age=18,
                school_capacity=1000
            )
    
    # First create/merge the school node in the main cc.institutes database
    logger.info(f"Creating school node in main cc.institutes database...")
    neon.create_or_merge_neontology_node(school_node, database="cc.institutes", operation='merge')
    
    # Then create/merge the school node in the specific school database
    logger.info(f"Creating school node in specific database {db_name}...")
    neon.create_or_merge_neontology_node(school_node, database=db_name, operation='merge')
    
    school_nodes = {
        'school_node': school_node,
        'db_name': db_name
    }
    
    if dataframes is not None:
        logger.info(f"Creating school timetable for {name} with {len(dataframes)} dataframes...")
        school_timetable_nodes = init_school_timetable.create_school_timetable(dataframes, db_name, school_node)
        school_nodes['school_timetable_nodes'] = school_timetable_nodes
    else:
        logger.warning(f"No dataframes provided for {name}, skipping school timetable...")
    
    logger.info(f"School {name} created successfully...")
    return school_nodes