Schemas are defined in code, not SQL. ORM models are generated dynamically. MySQL tables are created and seeded on demand. Docker/CI builds can bootstrap databases automatically on startup. Queries automatically adapt to MySQL vs. SQLite and dataset-specific differences. #288
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asherpasha
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Dec 4, 2025
api/__init__.py.save
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Why do we have this file?
asherpasha
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Dec 4, 2025
vendor/flask_sqlacodegen/LICENSE
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Do we need this whole vendor directory?
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GitHub actions need to pass. |
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@asherpasha Reena updated GH workflow yml files from v2 to v3, small minor changes. Can you see and approve if it's ok? |
![github-advanced-security[bot]](https://avatars.githubusercontent.com/in/57789?s=60&v=4){kind=small}
| """ | ||
| server_engine = create_engine(server_url) | ||
| with server_engine.begin() as conn: | ||
| conn.execute(text(f"CREATE DATABASE IF NOT EXISTS `{db_name}` DEFAULT CHARACTER SET {charset}")) |
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Code scanning / CodeQL
SQL query built from user-controlled sources High
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api/models/efp_schemas.py
Defines every “simple” eFP schema once in Python: columns, types, defaults, indexes, seed rows, metadata.
This is the single source of truth.
Add or edit schema entries here to support new datasets. No SQL files required.
api/models/efp_dynamic.py
Generates SQLAlchemy ORM models dynamically at import time from the schema definitions.
Each bind key (e.g., cannabis, dna_damage) becomes a mapped model class.
The app keeps full ORM functionality without writing model files to disk.
api/services/efp_bootstrap.py
Builds real MySQL databases and tables using SQLAlchemy DDL helpers.
Creates databases.
Creates tables.
Optionally inserts seed rows.
All structure is derived from the schema definitions in memory—no static .sql dumps.
scripts/bootstrap_simple_efp_dbs.py
CLI wrapper that bootstraps the simple eFP databases.
Can target all or selected databases:
python scripts/bootstrap_simple_efp_dbs.py --databases cannabis dna_damage embryo
Useful for Docker init scripts or CI setup.
config/init.sh
Runs the bootstrap script before loading any legacy dumps.
This ensures simple eFP databases are created and seeded during container startup.
api/services/efp_data.py
Unified query engine for both dynamic and legacy datasets.
Handles:
- bind key resolution
- MySQL vs SQLite fallback
- AGI → probeset mapping
- sample filtering
- schema-specific column differences
All eFP datasets now follow a single query path.
api/resources/efp_proxy.py
Public endpoints exposing the dynamic system:
GET /efp_proxy/expression → uses query_efp_database_dynamic
POST /efp_proxy/bootstrap/simple → triggers the bootstrap process over HTTP