👨🏼‍⚖️ reddit bot that turns comment chains into ace attorney scenes

This PR adds sentiment analysis capabilities to other languages other than english by performing the analysis over a translated version of the original text.

I'm well aware that Reddit speaks mainly in English, so this PR may not be useful. Please feel free to reject it. I had to type code anyway for my own purposes so I think you may be interested in it. As I said, feel free to reject the PR if you fell it doesn't add anything relevant.

Thanks

The use of "mmpeg" library was throwing "AttributeError: module 'ffmpeg' has no attribute 'input'"

That was due to the incorrect "ffmpeg" library being installed. It seems the intended library is in fact ffmpeg-python. More info in this issue: https://github.com/kkroening/ffmpeg-python/issues/174

EDIT: Thanks for the project, BTW ^^

I was unable to find the asset sources for certain things such as textbox4.png, arrow.png, or objection.gif. Is this also on court records?

I assume assets like helperstand.png can be found on this page.

Much help would be appreciated :)

First of all, sorry for using GitHub issues for this since it's not what they're for but I truly don't know what to do

My main problem seems to be a lack of codecs of both x264 and mp3. I've already tried to compile from source ffmpeg but it doesn't seem to work because it doesn't find mp3 encoder. I'm on debían

Just opening this issue in case someone struggled with it and has an idea of what to do

Thanks

Because having passwords stored in your computer in plaintext is bad practice I implemented an automatic way to authenticate using oauth2 refresh tokens.

this makes it so you need to run an authentication script (auth.py) once and give permissions from your browser. then you can run the other scripts indefinitely using the refresh token you obtained.

this however, requires the reddit app to be a web app and not a script. (hence the branch name) read more here

also added a .gitignore to ignore .refresh_token.txt and __pycache__

I'm not familiar with how streamable works but if it uses oauth2 it might be possible to create refresh token authentification for that too.

let me know what you think! :-)

edit: oh also i forgot to ask what scope the app needs. I set it as identity,read,submit but I'm not sure

changelog

• created new files auth.py and server.py
• created .gitignore
• changed praw.Reddit authentication method in bot_streamable.py
• added dependencies sys, socket, random

Old code of splitting sentences had same caveats, which include.

• If the input text was a single really really long sentence it wouldn't get split
• It could shuffle the order of the sentences, I had an instance where it would just return the same array but with the first sentence being the last.

• Data in case you want to replicate the bug. Data is in Spanish but it should be the same regardless the language if you manage to get sentences this long:

sentences =
['Absolutamente todos los profesores de informática que he tenido:', '¿', 'Oye sabéis que se ha detectado que los hombres que compran pañales también compran cerveza?']

This code has however, one problem as well:

• It only merges up to two small sentences even while more small sentences may be merged

Feel free to comment, change/review the code, accept or reject this PR. Thanks

Project won't work if spacy doesn't find en_core_web_sm.

Although spacy models are usually downloaded using python -m spacy download en_core_web_sm, they can also be downloaded using pip. This way, adding it to requirements.txt would make the project work transparently for users without them needing to manually download said model

• Moved package acquisition to be first and cache-busted when requirements.txt changes. Should speed up repeat builds by leveraging Docker's build cache.
• Cleared apt and pip caches after installing packages. Shaved ~200MB off the image size.

By not including a license with your code (usually in the form of a LICENSE file), people are legally not allowed to fork, contribute or otherwise use the code you've published in their own projects, kinda defeating the point of making it open source.

This PR contain 3 set of changes all aimed to make it easier to clone and start using the project.

1. I pinned specific versions in the requirements.txt file. Without specifying any version, pip was backtracking for over 10h trying to figure out which version of the dependencies to use. The versions could definitively be less strict but at the very least now it only takes a few second to setup. I would recommend testing on Linux/Docker before merging.
2. Since the README already mentioned the existence of the assets folder, I thought it would improve the experience for it to be part of the repository.
3. I documented the need to setup openh264 and FFmpeg for windows to be able to run the project.

I used this when hosting the bot myself, and I think others trying to do the same can benefit from this. You can now install the requirements with:

pip3 install -r requirements.txt

Rather than needing to find and install all the dependencies individually.

Hi, your project ace-attorney-reddit-bot requires "textblob==0.15.3" in its dependency. After analyzing the source code, we found that the following versions of textblob can also be suitable without affecting your project, i.e., textblob 0.11.1, 0.12.0, 0.13.0, 0.13.1, 0.14.0, 0.15.0, 0.15.1, 0.15.2. Therefore, we suggest to loosen the dependency on textblob from "textblob==0.15.3" to "textblob>=0.11.1,<=0.15.3" to avoid any possible conflict for importing more packages or for downstream projects that may use ace-attorney-reddit-bot.

May I pull a request to further loosen the dependency on textblob?

By the way, could you please tell us whether such dependency analysis may be potentially helpful for maintaining dependencies easier during your development?

We also give our detailed analysis as follows for your reference:

Your project ace-attorney-reddit-bot directly uses 3 APIs from package textblob.

textblob.blob.BaseBlob.translate, textblob.blob.BaseBlob.detect_language, textblob.blob.TextBlob.__init__

Beginning from the 3 APIs above, 3 functions are then indirectly called, including 3 textblob's internal APIs and 0 outsider APIs. The specific call graph is listed as follows (neglecting some repeated function occurrences).

[/micah5/ace-attorney-reddit-bot]
+--textblob.blob.BaseBlob.translate
+--textblob.blob.BaseBlob.detect_language
+--textblob.blob.TextBlob.__init__
|      +--textblob.blob.BaseBlob.__init__
|      |      +--textblob.utils.lowerstrip
|      |      |      +--textblob.utils.strip_punc
|      |      +--textblob.blob._initialize_models
|      |      |      +--textblob.blob._validated_param

We scan textblob's versions and observe that during its evolution between any version from [0.11.1, 0.12.0, 0.13.0, 0.13.1, 0.14.0, 0.15.0, 0.15.1, 0.15.2] and 0.15.3, the changing functions (diffs being listed below) have none intersection with any function or API we mentioned above (either directly or indirectly called by this project).

diff: 0.15.3(original) 0.11.1
['textblob.ordereddict.OrderedDict.__ne__', 'textblob.blob.WordList.__str__', 'textblob.classifiers.BaseClassifier.extract_features', 'textblob.classifiers.BaseClassifier', 'textblob.en.sentiments.PatternAnalyzer.analyze', 'textblob.en.taggers.NLTKTagger.tag', 'textblob.ordereddict.OrderedDict.__iter__', 'textblob.blob.WordList.__setitem__', 'textblob.blob.BaseBlob.pos_tags', 'textblob.blob.BaseBlob.sentiment_assessments', 'textblob.blob.WordList.stem', 'textblob.translate.Translator.detect', 'textblob.translate.Translator.translate', 'textblob._text._read', 'textblob.blob.BaseBlob.ngrams', 'textblob.classifiers.NLTKClassifier.update', 'textblob.blob.WordList', 'textblob.blob.WordList.append', 'textblob.ordereddict.OrderedDict.__eq__', 'textblob.ordereddict.OrderedDict.__reduce__', 'textblob.blob.WordList.__iter__', 'textblob.base.BaseTagger', 'textblob.en.taggers.NLTKTagger', 'textblob.blob.WordList.__getitem__', 'textblob.blob.WordList.__getslice__', 'textblob.en.taggers.PatternTagger', 'textblob.blob.BaseBlob', 'textblob.en.taggers.PatternTagger.tag', 'textblob.blob.BaseBlob.correct', 'textblob.ordereddict.OrderedDict.__init__', 'textblob.ordereddict.OrderedDict.copy', 'textblob._text.Sentiment.__call__', 'textblob.classifiers.basic_extractor', 'textblob.base.BaseTagger.tag', 'textblob.en.sentiments.PatternAnalyzer', 'textblob.blob.WordList.__init__', 'textblob.blob.Word', 'textblob.blob.WordList.extend', 'textblob.blob.Word.stem', 'textblob.ordereddict.OrderedDict', 'textblob.blob.WordList.__repr__', 'textblob.classifiers.NLTKClassifier.accuracy', 'textblob.tokenizers.SentenceTokenizer', 'textblob.translate._calculate_tk', 'textblob.blob.Word.lemmatize', 'textblob.blob.Word.lemma', 'textblob.ordereddict.OrderedDict.__repr__', 'textblob.classifiers.BaseClassifier.__init__', 'textblob._text.Sentiment', 'textblob.classifiers.NLTKClassifier', 'textblob.ordereddict.OrderedDict.__setitem__', 'textblob.blob.WordList.count', 'textblob.ordereddict.OrderedDict.clear', 'textblob.translate._unescape', 'textblob.translate.Translator', 'textblob.ordereddict.OrderedDict.__delitem__', 'textblob.ordereddict.OrderedDict.keys', 'textblob.ordereddict.OrderedDict.popitem', 'textblob.ordereddict.OrderedDict.__reversed__', 'textblob.ordereddict.OrderedDict.fromkeys']

diff: 0.15.3(original) 0.12.0
['textblob.blob.WordList.__str__', 'textblob.classifiers.BaseClassifier.extract_features', 'textblob.classifiers.BaseClassifier', 'textblob.en.sentiments.PatternAnalyzer.analyze', 'textblob.en.taggers.NLTKTagger.tag', 'textblob.blob.BaseBlob.pos_tags', 'textblob.blob.WordList.__setitem__', 'textblob.blob.BaseBlob.sentiment_assessments', 'textblob._text._read', 'textblob.classifiers.NLTKClassifier.update', 'textblob.blob.WordList', 'textblob.blob.WordList.append', 'textblob.blob.WordList.__iter__', 'textblob.base.BaseTagger', 'textblob.en.taggers.NLTKTagger', 'textblob.blob.WordList.__getitem__', 'textblob.blob.WordList.__getslice__', 'textblob.en.taggers.PatternTagger', 'textblob.blob.BaseBlob', 'textblob.en.taggers.PatternTagger.tag', 'textblob.blob.BaseBlob.correct', 'textblob._text.Sentiment.__call__', 'textblob.classifiers.basic_extractor', 'textblob.base.BaseTagger.tag', 'textblob.en.sentiments.PatternAnalyzer', 'textblob.blob.WordList.__init__', 'textblob.blob.Word', 'textblob.blob.WordList.extend', 'textblob.blob.WordList.__repr__', 'textblob.classifiers.NLTKClassifier.accuracy', 'textblob.tokenizers.SentenceTokenizer', 'textblob.blob.Word.lemmatize', 'textblob.blob.Word.lemma', 'textblob.classifiers.BaseClassifier.__init__', 'textblob._text.Sentiment', 'textblob.classifiers.NLTKClassifier', 'textblob.blob.WordList.count', 'textblob.translate._unescape']

diff: 0.15.3(original) 0.13.0
['textblob.blob.WordList.__str__', 'textblob.classifiers.BaseClassifier', 'textblob.en.sentiments.PatternAnalyzer.analyze', 'textblob.en.taggers.NLTKTagger.tag', 'textblob.blob.BaseBlob.pos_tags', 'textblob.blob.WordList.__setitem__', 'textblob.blob.BaseBlob.sentiment_assessments', 'textblob._text._read', 'textblob.blob.WordList', 'textblob.blob.WordList.append', 'textblob.blob.WordList.__iter__', 'textblob.base.BaseTagger', 'textblob.en.taggers.NLTKTagger', 'textblob.blob.WordList.__getitem__', 'textblob.blob.WordList.__getslice__', 'textblob.en.taggers.PatternTagger', 'textblob.blob.BaseBlob', 'textblob.en.taggers.PatternTagger.tag', 'textblob.blob.BaseBlob.correct', 'textblob._text.Sentiment.__call__', 'textblob.classifiers.basic_extractor', 'textblob.base.BaseTagger.tag', 'textblob.en.sentiments.PatternAnalyzer', 'textblob.blob.WordList.__init__', 'textblob.blob.Word', 'textblob.blob.WordList.extend', 'textblob.blob.WordList.__repr__', 'textblob.classifiers.NLTKClassifier.accuracy', 'textblob.tokenizers.SentenceTokenizer', 'textblob.blob.Word.lemmatize', 'textblob.blob.Word.lemma', 'textblob.classifiers.BaseClassifier.__init__', 'textblob._text.Sentiment', 'textblob.classifiers.NLTKClassifier', 'textblob.blob.WordList.count', 'textblob.translate._unescape']

diff: 0.15.3(original) 0.13.1
['textblob.blob.WordList.__str__', 'textblob.classifiers.BaseClassifier', 'textblob.en.sentiments.PatternAnalyzer.analyze', 'textblob.en.taggers.NLTKTagger.tag', 'textblob.blob.BaseBlob.pos_tags', 'textblob.blob.WordList.__setitem__', 'textblob.blob.BaseBlob.sentiment_assessments', 'textblob._text._read', 'textblob.blob.WordList', 'textblob.blob.WordList.append', 'textblob.blob.WordList.__iter__', 'textblob.base.BaseTagger', 'textblob.en.taggers.NLTKTagger', 'textblob.blob.WordList.__getitem__', 'textblob.blob.WordList.__getslice__', 'textblob.en.taggers.PatternTagger', 'textblob.blob.BaseBlob', 'textblob.en.taggers.PatternTagger.tag', 'textblob.blob.BaseBlob.correct', 'textblob.blob.WordList.__init__', 'textblob.classifiers.basic_extractor', 'textblob.base.BaseTagger.tag', 'textblob.en.sentiments.PatternAnalyzer', 'textblob.blob.Word', 'textblob.blob.WordList.extend', 'textblob.blob.WordList.__repr__', 'textblob.tokenizers.SentenceTokenizer', 'textblob.blob.Word.lemmatize', 'textblob.blob.Word.lemma', 'textblob.classifiers.BaseClassifier.__init__', 'textblob.blob.WordList.count', 'textblob.translate._unescape']

diff: 0.15.3(original) 0.14.0
['textblob.blob.WordList.__str__', 'textblob.classifiers.BaseClassifier', 'textblob.en.sentiments.PatternAnalyzer.analyze', 'textblob.blob.BaseBlob.pos_tags', 'textblob.blob.WordList.__setitem__', 'textblob.blob.BaseBlob.sentiment_assessments', 'textblob._text._read', 'textblob.blob.WordList', 'textblob.blob.WordList.append', 'textblob.blob.WordList.__iter__', 'textblob.blob.WordList.__getitem__', 'textblob.blob.WordList.__getslice__', 'textblob.blob.BaseBlob', 'textblob.blob.BaseBlob.correct', 'textblob.blob.WordList.__init__', 'textblob.classifiers.basic_extractor', 'textblob.en.sentiments.PatternAnalyzer', 'textblob.blob.Word', 'textblob.blob.WordList.extend', 'textblob.blob.WordList.__repr__', 'textblob.tokenizers.SentenceTokenizer', 'textblob.blob.Word.lemmatize', 'textblob.blob.Word.lemma', 'textblob.classifiers.BaseClassifier.__init__', 'textblob.blob.WordList.count', 'textblob.translate._unescape']

diff: 0.15.3(original) 0.15.0
['textblob.blob.WordList.__str__', 'textblob.blob.BaseBlob.pos_tags', 'textblob.blob.WordList.__setitem__', 'textblob._text._read', 'textblob.blob.WordList', 'textblob.blob.WordList.append', 'textblob.blob.WordList.__iter__', 'textblob.blob.WordList.__getitem__', 'textblob.blob.WordList.__getslice__', 'textblob.blob.BaseBlob', 'textblob.blob.BaseBlob.correct', 'textblob.blob.WordList.__init__', 'textblob.blob.Word', 'textblob.blob.WordList.extend', 'textblob.blob.WordList.__repr__', 'textblob.tokenizers.SentenceTokenizer', 'textblob.blob.Word.lemmatize', 'textblob.blob.Word.lemma', 'textblob.blob.WordList.count']

diff: 0.15.3(original) 0.15.1
['textblob.blob.BaseBlob', 'textblob.blob.BaseBlob.correct', 'textblob.blob.WordList.__str__', 'textblob.blob.WordList', 'textblob.blob.WordList.append', 'textblob.blob.WordList.__init__', 'textblob.blob.WordList.count', 'textblob.blob.BaseBlob.pos_tags', 'textblob.blob.WordList.__iter__', 'textblob.blob.WordList.__setitem__', 'textblob.blob.WordList.extend', 'textblob.blob.WordList.__repr__', 'textblob.tokenizers.SentenceTokenizer', 'textblob._text._read', 'textblob.blob.WordList.__getitem__', 'textblob.blob.WordList.__getslice__']

diff: 0.15.3(original) 0.15.2
['textblob.blob.BaseBlob', 'textblob.tokenizers.SentenceTokenizer', 'textblob.blob.BaseBlob.correct', 'textblob.blob.BaseBlob.pos_tags']

Therefore, we believe that it is quite safe to loose your dependency on textblob from "textblob==0.15.3" to "textblob>=0.11.1,<=0.15.3". This will improve the applicability of ace-attorney-reddit-bot and reduce the possibility of any further dependency conflict with other projects.

Although I understand that the assets are low-res since they come from a GBA game, the video is rendering at the resolution from said assets which affects the letters and make them (specially names) a bit hard to read.

We should be able to output, at least the letters, at a higher rest than the assets, which may mean probably upscaling.

It'll crash if there are more than 15 authors in the scene. I tried to solve it using this code

available_characters = list(
                filter(
                    lambda character: character not in characters, rnd_characters
                )
            )
            if (len(available_characters) == 0):
                available_characters = rnd_characters
            rnd_character = random.choice(available_characters)

But it crash at a later point due to how the code works Thanks

When the court music "03 - Turnabout Courtroom - Trial.mp3" ends, all sounds are then muted. I think sound should work past that time and music should loop.

Regards

when emojis/special characters are in the processed text, it causes a glitch in the text box that spams the typewriter effect for the duration of the scene. this can be fixed by adding some lines that detect emojis/special characters and removes them.

Being able to take a text/csv format and generate the animation. This is useful to integrate it with many other sites, or even make custom ones.

Ths can be done potentially by using:

characters, comments = load_from_file("file.txt/csv")
characters = anim.get_characters(characters)
anim.comments_to_scene(
    comments, characters, output_filename=output_filename
)