Skip to main content

Flask blueprints with SQLAlchemy for APIs -- what can I do to simplify?

I am designing an app that has a database, a CRUD web interface to manage its content, and exposing a REST API to be called by an external UI written in React. I am using native Flask Blueprints, SQLAlchemy, along with Marshmallow for API serialization.

The way I have structured Blueprints, based on what I learned from the Flask docs, seems unnecessarily complicated:

  • app.py -> __init__.py.create_app() registers blueprints
    • "factory" Flask pattern
    • each blueprint imports from a routes module
    • register_blueprint specifies two blueprints one for HTML, another for API as each has a different url_prefix
  • each routes module __init__.py
    • imports Blueprint, then
    • creates the blueprint instances for html and api, then
    • imports the actual routes for the module (e.g. from routes.user import routes)
  • each routes implementation
    • imports the blueprints created in the module
    • imports the SQLAlchemy model and Marshmallow schema
    • creates the code for the routes, and decorates with the correct blueprint depending on HTML or API
  • each model imports required elements for SQLAlchemy

So the SQLAlchemy approach seems just right: in one place I define all the characteristics of the model and thus the associated database tables, as well as the schema for JSON output of my API.

But the Blueprints approach seems convoluted, with dependencies scattered about -- to define one new route, I typically have to modify three files:

  • app module (__init.py__)
  • routes module for the specific route (routes/user/__init.py__)
  • routes implementation for the specific route (routes/user/routes.py)
  • and each of these implicitly depend on the model for the entity (e.g. user)

All of this is functional, but I am assuming there's a simpler way to approach the problem that I have not yet fully grasped. Is there a simpler, cleaner way to implement this?



source https://stackoverflow.com/questions/77664284/flask-blueprints-with-sqlalchemy-for-apis-what-can-i-do-to-simplify

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...
Post a Comment
Read more

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings
Post a Comment
Read more

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...
Post a Comment
Read more