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Create app.py
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app.py
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pip install transformers
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from transformers import pipeline
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# ๊ฐ์ ๋ถ๋ฅ ํ์ดํ๋ผ์ธ ์์ฑ
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classifier = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
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# ๊ฐ์ ๋ถ๋ฅ ํจ์ ์ ์
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def classify_emotion(text):
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result = classifier(text)[0]
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label = result['label']
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score = result['score']
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return label, score
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# ์ผ๊ธฐ ์์ฑ ํจ์ ์ ์
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def generate_diary(emotion):
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prompts = {
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"positive": "์ค๋์ ์ ๋ง ์ข์ ๋ ์ด์์ด์. ",
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"negative": "์ค๋์ ํ๋ ํ๋ฃจ์์ด์. ",
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"neutral": "์ค๋์ ๊ทธ๋ฅ ํ๋ฒํ ํ๋ฃจ์์ด์. "
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}
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prompt = prompts.get(emotion, "์ค๋์ ๊ธฐ๋ถ์ด ๋ณต์กํ ๋ ์ด์์ด์. ")
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diary = prompt + "์ค๋์ ์ผ๊ธฐ๋ฅผ ๋ง์นฉ๋๋ค."
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return diary
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# ์ฌ์ฉ์ ์
๋ ฅ ๋ฐ๊ธฐ
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user_input = input("์ค๋์ ๊ฐ์ ์ ํ ๋ฌธ์ฅ์ผ๋ก ํํํด์ฃผ์ธ์: ")
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# ๊ฐ์ ๋ถ๋ฅ
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emotion_label, _ = classify_emotion(user_input)
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# ๊ฐ์ ๊ธฐ๋ฐ ์ผ๊ธฐ ์์ฑ
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diary = generate_diary(emotion_label)
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# ์์ฑ๋ ์ผ๊ธฐ ์ถ๋ ฅ
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print("=== ์์ฑ๋ ์ผ๊ธฐ ===")
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print(diary)
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