lens0021 / objection_engine Goto Github PK

Code that allows you to convert chains of comments into ace attorney scenes. It's meant to be used by bots or other apps. List of users:

• Twiitter Bot
• Telegram Bot
• Discord Bot

This is a fork of a wonderful Reddit bot

• Python 3 with pip
• FFMPEG instalation. In most Linux distros it should be available in the default package manager. In Windows systems it'd include downloading a pre-compiled zip folder, extracting it and adding the /bin folder into the system path

• Google Translation API Credentials: These are needed for language support other than English. Other languages may work even without this credentials as the system will fallback to TextBlob's translation system.
• Libraqm: Improves text rendering on right-to-left languages. In windows refer to the faq.md

Clone the repository

git clone https://github.com/LuisMayo/objection_engine

Install dependencies (in case any problems are encountered please check faq.md)

python -m pip install -r requirements.txt

(optional) In case you want language support outside English install polyglot and its dependencies: (if on windows check faq.md)

pip install pyICU pycld2 morfessor polyglot
python -m polyglot download TASK:sentiment2

Check the exmaple python example.py

• Add it as a library with git submodule add https://github.com/LuisMayo/objection_engine.git

• Import it into your python file

import sys
sys.path.append('objection_engine/')
import objection_engine
# You can also import the components like this
from objection_engine.renderer import render_comment_list
from objection_engine.beans.comment import Comment

• Create a list of comments

foo = [objection_engine.comment.Comment(), objection_engine.comment.Comment(text_content='Second comment',  user_name="Second user")]

• Render the list

objection_engine.renderer.render_comment_list(foo)

For a list of arguments to the class and method check both https://github.com/LuisMayo/objection_engine/blob/main/renderer.py and https://github.com/LuisMayo/objection_engine/blob/main/beans/comment.py

There is a complete example in https://github.com/LuisMayo/objection-engine-testing

cp example.py docker-entrypoint.py

docker build --tag objection-engine .
docker run --rm \
  --volume $(pwd)/docker-entrypoint.py:/app/entrypoint.py:ro \
  --volume $(pwd)/assets:/app/assets \
  --volume $(pwd)/outputs:/app/outputs \
  objection-engine

The video will be in the /outputs directory.

You can download Polyglot models by setting oe_polyglot_models environment variable and preserve the data by mounting /root/polyglot_data:

docker run --rm \
  --volume $(pwd)/docker-entrypoint.py:/app/entrypoint.py:ro \
  --volume $(pwd)/assets:/app/assets \
  --volume $(pwd)/outputs:/app/outputs \
  --volume $(pwd)/polyglot_data:/root/polyglot_data \
  --env oe_polyglot_models="de fr" \
  objection-engine

The following environment variables are honored by objection_engine:

• oe_bypass_sentiment: If on any value other than the empty string, the sentiment analysis is bypassed
• oe_polyglot_models: (docker only) If on polyglot model(s), the data for the model will be downloaded when starting the container.

Since this is a tiny project we don't have strict rules about contributions. Just open a Pull Request to fix any of the project issues or any improvement you have percieved on your own. Any contributions which improve or fix the project will be accepted as long as they don't deviate too much from the project objectives. If you have doubts about whether the PR would be accepted or not you can open an issue before coding to ask for my opinion.