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Ryan Barrett fed9e40145
AP users: generalize sending class in Protocol.receive
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activitypub.py AP users: generalize receiving class in Protocol.receive 2023-06-12 19:01:50 -07:00
app.py
app.yaml
appengine_config.py
common.py webfinger: infer protocol from brid.gy subdomain in request or resource 2023-06-12 15:50:00 -07:00
config.py
convert.py AP users: add Protocol.ABBREV, Protocol.OTHER_LABELS 2023-06-11 08:14:17 -07:00
flask_app.py
follow.py AP users: generalize receiving class in Protocol.receive 2023-06-12 19:01:50 -07:00
index.yaml AP users: Object schema change, domains => users 2023-06-09 12:56:45 -07:00
indieauth_client_id
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models.py AP users: add Protocol.ABBREV, Protocol.OTHER_LABELS 2023-06-11 08:14:17 -07:00
oauth_dropins_fonts
oauth_dropins_static
pages.py noop, minor protocol generalization and cleanup 2023-06-10 15:07:26 -07:00
protocol.py AP users: generalize sending class in Protocol.receive 2023-06-12 20:52:42 -07:00
redirect.py noop, minor protocol generalization and cleanup 2023-06-10 15:07:26 -07:00
requirements.txt build(deps): bump ujson from 5.7.0 to 5.8.0 2023-06-12 05:59:36 -07:00
superfeedr.py
ui.py
web.py AP users: add Protocol.ABBREV, Protocol.OTHER_LABELS 2023-06-11 08:14:17 -07:00
webfinger.py AP users: generalize sending class in Protocol.receive 2023-06-12 20:52:42 -07:00
xrpc_actor.py
xrpc_feed.py
xrpc_graph.py

README.md

Bridgy Fed Circle CI Coverage Status

Bridgy Fed connects your web site to Mastodon and the fediverse via ActivityPub, webmentions, and microformats2. Your site gets its own fediverse profile, posts and avatar and header and all. Bridgy Fed translates likes, reposts, mentions, follows, and more back and forth. See the user docs and developer docs for more details.

https://fed.brid.gy/

Also see the original design blog posts.

License: This project is placed in the public domain.

Development

Development reference docs are at bridgy-fed.readthedocs.io. Pull requests are welcome! Feel free to ping me in #indieweb-dev with any questions.

First, fork and clone this repo. Then, install the Google Cloud SDK and run gcloud components install beta cloud-datastore-emulator to install the datastore emulator. Once you have them, set up your environment by running these commands in the repo root directory:

gcloud config set project bridgy-federated
python3 -m venv local
source local/bin/activate
pip install -r requirements.txt

Now, run the tests to check that everything is set up ok:

gcloud beta emulators datastore start --use-firestore-in-datastore-mode --no-store-on-disk --host-port=localhost:8089 --quiet < /dev/null >& /dev/null &
python3 -m unittest discover

Finally, run this in the repo root directory to start the web app locally:

GAE_ENV=localdev FLASK_ENV=development flask run -p 8080

If you send a pull request, please include (or update) a test for the new functionality!

If you hit an error during setup, check out the oauth-dropins Troubleshooting/FAQ section.

You may need to change granary, oauth-dropins, mf2util, or other dependencies as well as as Bridgy Fed. To do that, clone their repo locally, then install them in "source" mode with e.g.:

pip uninstall -y granary
pip install -e <path to granary>

To deploy to the production instance on App Engine - if @snarfed has added you as an owner - run:

gcloud -q beta app deploy --no-cache --project bridgy-federated *.yaml

Stats

I occasionally generate stats and graphs of usage and growth via BigQuery, like I do with Bridgy. Here's how.

  1. Export the full datastore to Google Cloud Storage. Include all entities except MagicKey. Check to see if any new kinds have been added since the last time this command was run.

    gcloud datastore export --async gs://bridgy-federated.appspot.com/stats/ --kinds Follower,Response
    

    Note that --kinds is required. From the export docs:

    Data exported without specifying an entity filter cannot be loaded into BigQuery.

  2. Wait for it to be done with gcloud datastore operations list | grep done.

  3. Import it into BigQuery:

    for kind in Follower Response; do
      bq load --replace --nosync --source_format=DATASTORE_BACKUP datastore.$kind gs://bridgy-federated.appspot.com/stats/all_namespaces/kind_$kind/all_namespaces_kind_$kind.export_metadata
    done
    
  4. Check the jobs with bq ls -j, then wait for them with bq wait.

  5. Run the full stats BigQuery query. Download the results as CSV.

  6. Open the stats spreadsheet. Import the CSV, replacing the data sheet.

  7. Check out the graphs! Save full size images with OS or browser screenshots, thumbnails with the Download Chart button.