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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rmobmina wants to merge 14 commits intoBioAnalyticResource:devfrom
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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.#288rmobmina wants to merge 14 commits intoBioAnalyticResource:devfrom
rmobmina wants to merge 14 commits intoBioAnalyticResource:devfrom
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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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@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
- Replace manual REGEX with BARUtils validators for all species - Add descriptive error messages for each species (e.g., "Invalid Cannabis gene ID") - Add comprehensive Sphinx/reST docstrings to all ORM files - Improve validation logic to handle both AGI and probeset IDs correctly - Fix SQL injection vulnerability by validating schema identifiers - Add identifier validation (alphanumeric + underscore only) before SQL construction Security: Addresses CodeQL high-severity SQL injection alert by validating all database/table/column identifiers match safe pattern before use in queries
- Fix trailing whitespace and blank line issues in efp_proxy.py - Fix missing blank lines in microarray_gene_expression.py - Add noqa comments for E402 in test_efp_data.py (imports after sys.path modification) - Remove unused variable retry_url - Fix spacing after commas in long list - Fix indentation issues
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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