AIOHTTP VS Flask

The error calls to a file that has been through multiple recent revisions. The most likely cause is an issue was introduced.

Try the other comments, if nothing works, you can maybe submit a bug to aiohttp repo. I feel like they may be better prepared to deal with this than us. The example you gave is great BTW

I googled much better and found this: https://github.com/aio-libs/aiohttp/issues/3203

I executed the script 3 times making 20 requests on each execution. Ok now lets replace the synchronous requests library by the asynchronous aiohttp library. Furthermore, we add the async/await keywords on the right positions. Our final code will look like this:

I do see some low level suggestions here: https://github.com/aio-libs/aiohttp/issues/2522 that we can investigate.

The goal of the back end is to act as a wrapper for a model so that it can be queried via HTTP requests from the front end, something referred to as model serving. In order to do so, one would typically use a web framework. For a long time, Flask was the standard for Python-based web frameworks, and is indeed still very popular. However, FastaAPI is quickly becoming the new favorite, thanks to impressive performance and native support for asynchronous operations. This article is a good starting point to understand how a simple model can be deployed using FastAPI, while this tutorial provides a complete overview of all the steps required to serve a PyTorch model with GPU support.

Not sure if it's what you're looking for but I typically use the tutorial or the tutorial github. This is really just a templates and static folder with the blueprints in the main dir though.

You can then make inside the docs directory to build them locally: https://github.com/pallets/flask/tree/main/docs

from collections import OrderedDict>>>>>> json_data = {... "11": {... "j_id": "out",... },... "aa": {... "j_id": "in",... },... "bb": {... "j_id": "out",... },... }>>>>>> jkeys=json_data.keys()>>> sorted_json = sorted(jkeys, key=lambda x: json_data[x]['j_id'], reverse=False)>>>>>> new_sorted=OrderedDict()>>> for rec in sorted_json:... new_sorted[rec]=json_data[rec]...>>> print('sort dict: {}'.format(new_sorted))sort dict: OrderedDict([('aa', {'j_id': 'in'}), ('11', {'j_id': 'out'}), ('bb', {'j_id': 'out'})]) ``` Unfortunately, when I pass this to jsonify() function and then for example console.log() output of flask data, the orderd becomes like that: 11, aa, bb. Additionally to that, I've done some research and found this stackoverflow answer, leading to some good documentation notes which clearly states that setting JSON_SORT_KEYS to False is not recommended way. Then I checked this github issue and it seems that problem is not fully resolved in flask.

Flask is a straightforward framework for Python web development that makes it easy to get started on your web applications.

We're using Flask as our web server because it is very lightweight, so we'll want to install that next.

If your app is really small/short, you're only fetching crime data and rendering a static image, you can probably do it all with a Cloud Function. This way you don't even need to have nor learn about nor use full web frameworks like Flask, Django, Pyramid, etc. Just raw code plus an HTML template and perhaps a tiny bit of Flask/Jinja2 to render the template.

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