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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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import torch.nn.functional as F
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model_name = "yangheng/deberta-v3-base-absa-v1.1"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSequenceClassification.from_pretrained(model_name)
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labels = ["Negative", "Neutral", "Positive"]
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def analyze_sentiment(sentence, aspects_text):
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aspects = [a.strip() for a in aspects_text.split(",") if a.strip()]
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output = ""
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for aspect in aspects:
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combined = f"{sentence} [ASP] {aspect}"
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inputs = tokenizer(combined, return_tensors="pt", truncation=True)
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with torch.no_grad():
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outputs = model(**inputs)
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probs = F.softmax(outputs.logits, dim=1)
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pred = torch.argmax(probs, dim=1).item()
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sentiment = labels[pred]
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output += f"- **{aspect}** → **{sentiment}**\n"
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return output
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iface = gr.Interface(
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fn=analyze_sentiment,
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inputs=[
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gr.Textbox(label="문장 입력", placeholder="예: The battery is good but the screen is dim."),
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gr.Textbox(label="속성 목록 (쉼표로 구분)", placeholder="예: battery, screen")
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],
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outputs=gr.Markdown(label="감정 분석 결과"),
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title="ABSA 감정 분석기",
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description="Hugging Face Transformers 모델 사용"
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)
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iface.launch()
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