CS4300_Flask_template | Template for INFO/CS4300 project , using Flask

 by   CornellNLP Python Version: Current License: No License

kandi X-RAY | CS4300_Flask_template Summary

kandi X-RAY | CS4300_Flask_template Summary

CS4300_Flask_template is a Python library. CS4300_Flask_template has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Template for INFO/CS4300 project, using Flask.
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              CS4300_Flask_template has a low active ecosystem.
              It has 12 star(s) with 219 fork(s). There are 5 watchers for this library.
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              It had no major release in the last 6 months.
              CS4300_Flask_template has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of CS4300_Flask_template is current.

            kandi-Quality Quality

              CS4300_Flask_template has no bugs reported.

            kandi-Security Security

              CS4300_Flask_template has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              CS4300_Flask_template does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              CS4300_Flask_template releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed CS4300_Flask_template and discovered the below as its top functions. This is intended to give you an instant insight into CS4300_Flask_template implemented functionality, and help decide if they suit your requirements.
            • Return a HTTP resource response
            • Return a JSON response
            • Return errors as JSON
            Get all kandi verified functions for this library.

            CS4300_Flask_template Key Features

            No Key Features are available at this moment for CS4300_Flask_template.

            CS4300_Flask_template Examples and Code Snippets

            No Code Snippets are available at this moment for CS4300_Flask_template.

            Community Discussions

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            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install CS4300_Flask_template

            We assume by now all of you have seen and used virtualenv, but if not, go here to install and for dead-simple usage go here. (For Mac users, you may encounter an ERROR: Failed building wheel for greenlet. This can be fixed with xcode-select --install.). An aside note: In the above example, we created a virtualenv for a python3 environment. You will have python3.7.10 installed by default as we have used that version for assignments. This is what we will use for the application as well. NOTE: While you should be able to install these requirements in the virtualenv you used for the assignments, we advise using a fresh virtualenv so you can be sure that your virtualenv's installed packages and your repository's requirements.txt match exactly. This will be important when you add new dependencies.
            If you followed the quickstart guide, you should now have set up postgres. Rather than writing raw-SQL for this application, I have chosen to utilize SQLAlchemy (specifically, Flask-SQLAlchemy) as a database Object-Relational-Model (ORM, for short). In addition, for the purposes of serialization (turning these database entities into organized JSONs that we can send over the wire) and deserialization (turning a JSON into a entity once again), I have chosen to use Marshmallow (specifically, marshmallow-SQLAlchemy). Several modules are needed to completely integrate Postgres into a Flask app, but several of these modules are co-dependent on one another. I have included all of these in the requirements.txt file, these modules include: flask-migrate marshmallow-sqlalchemy psycopg2.
            Now that we have setup our database and have handled our manage.py script, we can create our config.py script, which involves the database and various other configuration information specific to Flask. This file will be used in our initialization of the Flask app in the app module in the near future.
            DEBUG indicates whether or not debug stack traces will be logged by the server.
            CSRF_ENABLED, CSRF_SESSION_KEY, and SECRET_KEY all relate to Cross-Site-Request-Forgery, which you can read more about here.
            SQLALCHEMY_DATABASE_URI refers to the database URL (a server running your database). In the above example, I refer to an environment variable 'DATABASE_URL'. I will be discussing environment variables in the next section, so stay tuned.
            Up until now, we haven't been able to run our server.
            You can setup a database quickly by doing the following:. And everything else is self-explanatory. Upon creation it will give you an instance link that you will use for the DATABASE_URL.
            Open up the Google Cloud Platform Console
            Go to Storage > SQL
            Create Instance
            Create a PostgreSQL Instance

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            https://github.com/CornellNLP/CS4300_Flask_template.git

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            gh repo clone CornellNLP/CS4300_Flask_template

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            git@github.com:CornellNLP/CS4300_Flask_template.git

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