mentorship-backend VS flask-sqlalchemy

In this program, I proposed a new project called Mentorship System, for women in the community to mentor each other via 1:1 relations. For this project, I developed both the backend REST API with Python - anitab-org/mentorship-backend - and Android application with Kotlin - anitab-org/mentorship-android.

Test manually a pull request code before it gets accepted to be merged, to confirm the code is working as expected. Be creative! Try to come up with test cases for the feature being implemented that perhaps the author of the pull request did not think of. Examples: testing REST API, testing bottom navigation bug in android app.

Se quiseres um exemplo de modelos de issues, um dos projectos que mantenho tem diferentes tipos de modelos para issues e exemplo de modelo de descrição de PRs. Também podes encontrar diferentes modelos neste repositorio - devspace/awesome-github-templates.

You should probably use a database for this, using something like sqlite a single file "database" is probably the quickest way to get started, I recommend you use it with Flask-SQLAlchemy which makes working with the db easy as pie.

Flask-SQLAlchemy: An extension that integrates SQLAlchemy with Flask. You can install it using pip install flask-sqlalchemy.

flask-sqlalchemy: ORM for database access

I've made several runs at stripping the essential paginating functionality of flask-sqlalchemy for use with things like lists. That's where I'd start.

You can either use an ORM, which might be sqlalchemy, possibly wrapped up as a flask plugin https://flask-sqlalchemy.palletsprojects.com/ or more directly: https://www.sqlalchemy.org/

You can learn more about this approach and see an example is this Flask-SQLAlchemy PR.

Flask SQL Alchemy

I used for this project Flask and OpenAI CLIP. For the vector search I used approximate nearest neighbors provided by spotify/annoy. I used Flask-SQLAlchemy with GeoAlchemy2 to query GPS coordinates. The embedding was done using UMAP using the CLIP image vectors.

This appears to be this: https://github.com/pallets/flask-sqlalchemy/issues/910

from sqlalchemy.engine.reflection import Inspector from sqlalchemy.schema import DropConstraint, DropTable, MetaData, Table from extensions import db # from dotmap import DotMap def try_add(object): """ Try to add the column 'object' to its table in the database and return it. """ try: db.session.add(object) db.session.commit() return object except Exception: db.session.rollback() raise def drop_all_cascade(): """(On a live db) drops all foreign key constraints before dropping all tables. Workaround for SQLAlchemy not doing DROP ## CASCADE for drop_all() (https://github.com/pallets/flask-sqlalchemy/issues/722) """ con = db.engine.connect() trans = con.begin() inspector = Inspector.from_engine(db.engine) # We need to re-create a minimal metadata with only the required things to # successfully emit drop constraints and tables commands for postgres # (based on the actual schema of the running instance) meta = MetaData() tables = [] all_fkeys = [] for table_name in inspector.get_table_names(): fkeys = [] for fkey in inspector.get_foreign_keys(table_name): if not fkey["name"]: continue fkeys.append(db.ForeignKeyConstraint((), (), name=fkey["name"])) tables.append(Table(table_name, meta, *fkeys)) all_fkeys.extend(fkeys) for fkey in all_fkeys: con.execute(DropConstraint(fkey)) for table in tables: con.execute(DropTable(table)) trans.commit()