d5d4ffbd7a
* Conducts sentiment analysis and provides a happy or assuring message sometimes * Just skeletal: we can refine it but the logic is here
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
# Required
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# pip3 install -q transformers
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# pip3 install tensorflow
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import os
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import sys
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import random
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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os.environ['TRANSFORMERS_VERBOSITY'] = 'critical'
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from transformers import pipeline
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# get data
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commit_msg = sys.stdin.read()
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sentiment_pipeline = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment")
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data = [commit_msg,]
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stmt = sentiment_pipeline(data)
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positive_titles = [
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"Fuck yeah!",
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"Awesome",
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"Proud of you",
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"Schwing!",
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"Killing it!",
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"Fairest of them all",
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]
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positive_messages = [
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"I hope I'm half as happy as you are!",
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"Excellent commit. 10/10. No notes.",
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"You're doing a great job.",
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"Your collaborators are real lucky to have you.",
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"Smooth as butter.",
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"Nothing but net.",
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]
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negative_titles = [
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"Woah there!",
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"Deep breaths",
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"You've got this",
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"A little annoyed?",
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"Perhaps a walk?",
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]
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negative_messages = [
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"Maybe get some fresh air -- you seem a little agitated.",
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"Picking up some tense vibes. Maybe time for a snack?",
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"Take five and text someone you care about!",
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"Don't let the code win. It's a smug bastard and it'll never let you hear the end of it!",
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"I suggest grabbing a drink of water and a little stretch.",
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]
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if stmt[0]["label"] == 'LABEL_0':
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# negative
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if stmt[0]["score"] > 0.60:
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os.system('notify-send "{}" "{}"'.format(random.choice(negative_titles), random.choice(negative_messages)))
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elif stmt[0]["label"] == 'LABEL_2':
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if stmt[0]["score"] > 0.60:
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os.system('notify-send "{}" "{}"'.format(random.choice(positive_titles), random.choice(positive_messages)))
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