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app.py

@@ -1,37 +1,42 @@
 import gradio as gr
-from pyabsa import AspectSentimentClassification as ASC
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
 
-# 모델 로드
-aspect_extractor = ASC.SentimentClassifier(
-    checkpoint='yangheng/deberta-v3-base-absa-v1.1'
-)
-
-# 분석 함수
-def absa(text, aspect_input):
-    aspects = [a.strip() for a in aspect_input.split(",") if a.strip()]
-    pairs = [[text, aspect] for aspect in aspects]
+# 모델 로딩
+model_name = "yangheng/deberta-v3-base-absa-v1.1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-    results = aspect_extractor.infer(
-        inference_source=[text],
-        aspect_sentiment_pair_list=pairs
-    )
+labels = ["Negative", "Neutral", "Positive"]
 
+# 분석 함수
+def analyze_sentiment(sentence, aspects_text):
+    aspects = [a.strip() for a in aspects_text.split(",") if a.strip()]
     output = ""
-    for res in results:
-        for aspect, sentiment in res["aspect_sentiment_pair"]:
+
+    for aspect in aspects:
+        combined = f"{sentence} [ASP] {aspect}"
+        inputs = tokenizer(combined, return_tensors="pt", truncation=True)
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = F.softmax(outputs.logits, dim=1)
+            pred = torch.argmax(probs, dim=1).item()
+            sentiment = labels[pred]
             output += f"- **{aspect}** → **{sentiment}**\n"
+
     return output
 
-# Gradio 인터페이스 생성
+# Gradio 인터페이스
 iface = gr.Interface(
-    fn=absa,
+    fn=analyze_sentiment,
     inputs=[
-        gr.Textbox(label="문장 입력", placeholder="예: The battery is good, but the screen is dim."),
-        gr.Textbox(label="속성 입력 (쉼표로 구분)", placeholder="예: battery, screen")
+        gr.Textbox(label="문장 입력", placeholder="예: The battery is good but the screen is dim."),
+        gr.Textbox(label="속성 목록 (쉼표로 구분)", placeholder="예: battery, screen")
     ],
-    outputs=gr.Markdown(label="속성별 감정 분석 결과"),
-    title="ABSA 감정 분석기 (DeBERTa v3)",
-    description="문장과 속성을 입력하면 속성별 감정을 추출합니다. 모델: yangheng/deberta-v3-base-absa-v1.1"
+    outputs=gr.Markdown(label="감정 분석 결과"),
+    title="ABSA 감정 분석기",
+    description="Hugging Face Transformers 모델 사용"
 )
 
 iface.launch()

requirements.txt

@@ -1,6 +1,3 @@
 gradio
-git+https://github.com/yangheng95/ABSAgym
 transformers==4.28.1
-protobuf==4.25.2
-findfile
-pytorch_lightning==1.9.5
+torch