huggingface_hub VS Flask

Something to think about:

https://github.com/huggingface/huggingface_hub

Yes! We have a well documented Python library (https://github.com/huggingface/huggingface_hub) and public endpoints (https://huggingface.co/docs/hub/api#endpoints-table) you can use to retrieve information about the models and potentially build UIs with specific use cases in mind

i think if you search for pixel art here there are some models worth checking out: https://huggingface.co/

I received another error saying OSError: We couldn't connect to 'https://huggingface.co' to load this model, couldn't find it in the cached files and it looks like ./vae is not the path to a directory containing a file named diffusion_pytorch_model.bin

I plan to release it on https://huggingface.co/, where all this cool AI stuff is available for free for everyone that wishes to try it.

For this I used mainly the chainner application with models from here but I also used the google colab automatic1111 stable diffusion webui (for example for Lanczos) and also spaces fromhuggingface like this one or then from the replicate.com website super resolution collection.

you will now need to create a huggingface account ( https://huggingface.co/) if you haven't already. When you have, go here and accept the terms, https://huggingface.co/runwayml/stable-diffusion-v1-5. When you have done both, click on your profile icon and go to settings. Click access tokens and then create token, name it whatever you want, select "write". When you are finished with all this, then you can run the next cell which is the hugging face cell. It will ask for a token, you copy and paste what you just created.

I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:

https://github.com/pallets/flask

#!/usr/bin/python # # https://flask.palletsprojects.com/en/3.0.x/installation/ # from flask import Flask, jsonify, request contacts = [ { "id": "1", "firstname": "Lorem", "lastname": "Ipsum", "email": "[email protected]", }, { "id": "2", "firstname": "Mauris", "lastname": "Quis", "email": "[email protected]", }, { "id": "3", "firstname": "Donec Purus", "lastname": "Purus", "email": "[email protected]", } ] app = Flask(__name__, static_url_path='', static_folder='public',) @app.route("/contact//save", methods=["PUT"]) def save_contact(id): data = request.json contacts[id - 1] = data return jsonify(contacts[id - 1]) @app.route("/contact/", methods=["GET"]) @app.route("/contact//edit", methods=["GET"]) def get_contact(id): return jsonify(contacts[id - 1]) @app.route('/') def root(): return app.send_static_file('index.html') if __name__ == '__main__': app.run(debug=True)

I was looking at all the big projects like numpy, pytorch, flask, etc.

Explore here.

I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.

Now...

Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?

- [0]: https://flask.palletsprojects.com

- [1]: https://flask-admin.readthedocs.io/en/latest

- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery

- [3]: https://sentry.io

- [4]: https://posthog.com

- [5]: https://www.docker.com

In the lab to follow, we will be setting up an end-to-end DevOps workflow for a Flask microservice with GitHub Actions, using a self-managed custom runner for maximal control over the pipeline execution environment and automating deployments to a local Kubernetes cluster. Furthermore, we will construct separate pipelines for our "development" and "production" environments to further elaborate on the concepts of continuous deployment and delivery.

Flask Micro web Framework: You will use Flask to build a Flask web application.

In an earlier post I mentioned a bunch of Open Source web applications. Let's now focus on the ones written in Python using Flask the light-weight web framework.