From 7bbd56155b0f2118971da9bd1d0737b1bc33a3af Mon Sep 17 00:00:00 2001
From: Anthony Wang
Date: Sun, 27 Feb 2022 18:00:22 -0600
Subject: WIP: Add Pleroma and Misskey support

---
 README.md |  8 +++++---
 bot.py    | 26 +++++++++++++++++++-------
 2 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index f10a28f..f7f5725 100644
--- a/README.md
+++ b/README.md
@@ -2,15 +2,17 @@
 
 Fediverse ebooks bot using neural networks
 
+
 ## Usage
 
-First, install Python dependencies with `pip install psycopg2 torch transformers datasets Mastodon.py`. Currently only Mastodon with PostgreSQL is supported, but it should be easy to add support for other platforms and databases.
+First, install Python dependencies using your distro's package manager or `pip`: [psycopg2](https://www.psycopg.org), [torch](https://pytorch.org/), [transformers](https://huggingface.co/docs/transformers/index), and [datasets](https://huggingface.co/docs/datasets/). Additionally, for Mastodon and Pleroma, install [Mastodon.py](https://mastodonpy.readthedocs.io/en/stable/), and for Misskey, install [Misskey.py](https://misskeypy.readthedocs.io/ja/latest/). If your database or platform isn't supported, don't worry! It's easy to add support for other platforms and databases, and contributions are welcome!
 
-Generate the training data from the Mastodon database using `python data.py -d 'dbname=test user=postgres password=secret'`. You can skip this step if you have training data from another source.
+Now generate the training data from your fediverse server's database using `python data.py -d 'dbname=test user=postgres password=secret host=localhost port=5432'`. You can skip this step if you have collected training data from another source.
 
 Next, train the network with `python train.py`, which may take several hours. It's a lot faster when using a GPU. If you need advanced features when training, you can also train using [run_clm.py](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py).
 
-Finally, create a Mastodon application for your bot account and run the bot with `python bot.py -i mastodon.instance -t access_token -n 'Initial text'`. You can create a cron job to run this periodically. Enjoy!
+Finally, create an application for your bot account and generate an access token. Run the bot with `python bot.py -b server_type -i fediverse.instance -t access_token`. You can omit `-b server_type` for Mastodon and Pleroma. To run the bot periodically, create a cron job. Enjoy!
+
 
 ## Resources
 
diff --git a/bot.py b/bot.py
index 76f2952..a9d661f 100644
--- a/bot.py
+++ b/bot.py
@@ -1,11 +1,12 @@
 from argparse import ArgumentParser
 from random import randint, choice
 
-from mastodon import Mastodon
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 
 parser = ArgumentParser()
+parser.add_argument('-b', '--backend', choices=['mastodon', 'misskey'], default='mastodon',
+                    help='fediverse server type')
 parser.add_argument('-i', '--instance', help='Mastodon instance hosting the bot')
 parser.add_argument('-t', '--token', help='Mastodon application access token')
 parser.add_argument('-n', '--input', help='initial input text')
@@ -76,12 +77,23 @@ output = tokenizer.decode(model.generate(
 print(output)
 
 
-# Post it to Mastodon
-mastodon = Mastodon(
-    access_token=args.token,
-    api_base_url=args.instance
-)
+# Prepare the post
 post = output.split('\n')[0]
 if len(post) < 200:
     post = output.split('\n')[0] + '\n' + output.split('\n')[1]
-mastodon.status_post(post[:500])
+
+
+# Post it!
+if args.backend == 'mastodon':
+    from mastodon import Mastodon
+
+    mastodon = Mastodon(
+        access_token=args.token,
+        api_base_url=args.instance
+    )
+    mastodon.status_post(post[:500])
+elif args.backend == 'misskey':
+    from Misskey import Misskey
+
+    misskey = Misskey(args.instance, i=args.token)
+    misskey.notes_create(post[:500])
-- 
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