60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
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"""Classify the sentiment of a sentence.
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This is the first demo where there isn't a plain-Python alternative to
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compare against. Pre-2017, "tell me if this review is positive or negative"
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was a research problem — papers, labeled datasets, custom-trained models.
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Here it's a few lines of prompt.
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"""
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from __future__ import annotations
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from ai_function import ask
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def ai_sentiment(text: str) -> str:
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return ask(
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"Classify the sentiment of the following sentence as exactly one of: "
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"positive, negative, neutral. Output only the single word.\n"
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'Sentence: "I love this product, it changed my life."\n'
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"Sentiment: positive\n"
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'Sentence: "It arrived broken and customer service ignored me."\n'
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"Sentiment: negative\n"
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'Sentence: "The package arrived on Tuesday."\n'
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"Sentiment: neutral\n"
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f'Sentence: "{text}"\n'
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"Sentiment: "
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)
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def _canned_examples() -> None:
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samples = [
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"Honestly the best coffee I've had in months.",
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"The hotel was fine, nothing memorable either way.",
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"Worst flight of my life, I'll never fly this airline again.",
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"It works, but the instructions could be a lot clearer.",
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]
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for s in samples:
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print(f"input: {s}")
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print(f"sentiment: {ai_sentiment(s)}\n")
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def _interactive() -> None:
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print("--- interactive ---")
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print("Type a sentence to classify, or 'q' to quit.\n")
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while True:
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try:
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text = input("> ").strip()
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except (EOFError, KeyboardInterrupt):
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print()
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return
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if text.lower() in {"q", "quit", "exit"}:
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return
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if not text:
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continue
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print(f"sentiment: {ai_sentiment(text)}\n")
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if __name__ == "__main__":
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_canned_examples()
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_interactive()
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