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@@ -2,115 +2,31 @@ import os
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from modules.logger_tool import initialise_logger
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logger = initialise_logger(__name__, os.getenv("LOG_LEVEL"), os.getenv("LOG_PATH"), 'default', True)
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from modules.database.schemas.nodes.schools.schools import SchoolNode
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from modules.database.supabase.utils.client import SupabaseServiceRoleClient, CreateBucketOptions
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import modules.database.init.init_school_timetable as init_school_timetable
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import modules.database.tools.neontology_tools as neon
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import modules.database.tools.supabase_storage_tools as storage_tools
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def create_school_buckets(school_id: str, school_type: str, school_name: str, admin_access_token: str) -> dict:
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"""Create storage buckets for a school
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Args:
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school_id: The unique identifier for the school
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school_type: The type of school (e.g., 'development')
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school_name: The display name of the school
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admin_access_token: The admin access token for Supabase operations
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Returns:
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Dictionary containing results of bucket creation operations
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"""
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logger.info(f"Creating storage buckets for school {school_name} ({school_type}/{school_id})")
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def create_school(db_name: str, uuid_string: str, name: str, website: str, school_type: str, is_public: bool = True, school_node: SchoolNode | None = None, dataframes=None):
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if not name or not uuid_string or not website or not school_type:
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logger.error("School name, uuid_string, website and school_type are required to create a school.")
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raise ValueError("School name, uuid_string, website and school_type are required to create a school.")
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storage_client = SupabaseServiceRoleClient.for_admin(admin_access_token)
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base_path = f"cc.institutes.{school_type}.{school_id}"
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buckets = [
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# Main school buckets
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{
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"id": f"{base_path}.public",
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"options": CreateBucketOptions(
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name=f"{school_type.title()} School Files - {school_name} - Public Files",
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public=True,
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file_size_limit=50 * 1024 * 1024,
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allowed_mime_types=[
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'image/*', 'video/*', 'application/pdf',
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'application/msword', 'application/vnd.openxmlformats-officedocument.*',
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'text/plain', 'text/csv', 'application/json'
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]
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)
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},
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{
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"id": f"{base_path}.private",
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"options": CreateBucketOptions(
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name=f"{school_type.title()} School Files - {school_name} - Private Files",
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public=False,
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file_size_limit=50 * 1024 * 1024,
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allowed_mime_types=[
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'image/*', 'video/*', 'application/pdf',
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'application/msword', 'application/vnd.openxmlformats-officedocument.*',
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'text/plain', 'text/csv', 'application/json'
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]
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)
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},
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# Curriculum buckets
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{
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"id": f"{base_path}.curriculum.public",
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"options": CreateBucketOptions(
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name=f"{school_type.title()} School Files - {school_name} - Curriculum Public Files",
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public=True,
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file_size_limit=50 * 1024 * 1024,
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allowed_mime_types=[
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'image/*', 'video/*', 'application/pdf',
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'application/msword', 'application/vnd.openxmlformats-officedocument.*',
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'text/plain', 'text/csv', 'application/json'
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]
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)
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},
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{
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"id": f"{base_path}.curriculum.private",
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"options": CreateBucketOptions(
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name=f"{school_type.title()} School Files - {school_name} - Curriculum Private Files",
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public=False,
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file_size_limit=50 * 1024 * 1024,
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allowed_mime_types=[
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'image/*', 'video/*', 'application/pdf',
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'application/msword', 'application/vnd.openxmlformats-officedocument.*',
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'text/plain', 'text/csv', 'application/json'
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]
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)
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}
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]
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results = {}
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for bucket in buckets:
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try:
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result = storage_client.create_bucket(bucket["id"], bucket["options"])
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results[bucket["id"]] = {
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"status": "success",
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"result": result
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}
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logger.info(f"Successfully created bucket {bucket['id']}")
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except Exception as e:
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logger.error(f"Error creating school bucket {bucket['id']}: {str(e)}")
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results[bucket["id"]] = {
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"status": "error",
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"error": str(e)
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}
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return results
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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):
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if not name or not id or not website or not school_type:
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logger.error("School name, id, website and school_type are required to create a school.")
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raise ValueError("School name, id, website and school_type are required to create a school.")
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logger.info(f"Initialising neo4j connection...")
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logger.info(f"Initialising Neontology connection...")
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neon.init_neontology_connection()
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# Initialize storage tools for school
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storage_tools_instance = storage_tools.SupabaseStorageTools(db_name, init_run_type="school")
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# Generate the storage path for the school node
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school_dir_created, node_storage_path = storage_tools_instance.create_school_storage_path(uuid_string)
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logger.info(f"Generated school storage path: {node_storage_path}")
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# Create School Node if not provided
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if not school_node:
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if is_public:
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school_node = SchoolNode(
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unique_id=f'School_{id}',
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tldraw_snapshot="",
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id=id,
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uuid_string=uuid_string,
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node_storage_path=node_storage_path,
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name=name,
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website=website,
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school_type=school_type
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@@ -118,9 +34,8 @@ def create_school(db_name: str, id: str, name: str, website: str, school_type: s
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else:
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# Create private school node with default values
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school_node = SchoolNode(
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unique_id=f'School_{id}',
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tldraw_snapshot="",
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id=id,
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uuid_string=uuid_string,
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node_storage_path=node_storage_path,
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name=name,
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website=website,
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school_type=school_type,
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@@ -133,6 +48,9 @@ def create_school(db_name: str, id: str, name: str, website: str, school_type: s
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statutory_high_age=18,
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school_capacity=1000
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)
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else:
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# Update existing school node with the storage path
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school_node.node_storage_path = node_storage_path
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# First create/merge the school node in the main cc.institutes database
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logger.info(f"Creating school node in main cc.institutes database...")
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@@ -144,15 +62,16 @@ def create_school(db_name: str, id: str, name: str, website: str, school_type: s
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school_nodes = {
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'school_node': school_node,
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'db_name': db_name
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'db_name': db_name,
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'storage_path': node_storage_path
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}
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if dataframes is not None:
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logger.info(f"Creating school timetable for {name} with {len(dataframes)} dataframes...")
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school_timetable_nodes = init_school_timetable.create_school_timetable(dataframes, db_name, school_node)
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school_timetable_nodes = init_school_timetable.create_school_timetable(dataframes, db_name, school_node, storage_tools_instance)
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school_nodes['school_timetable_nodes'] = school_timetable_nodes
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else:
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logger.warning(f"No dataframes provided for {name}, skipping school timetable...")
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logger.info(f"School {name} created successfully...")
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logger.info(f"School {name} created successfully with storage path: {node_storage_path}")
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return school_nodes
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