Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# 모델 불러오기
+model_name = "hun3359/mdistilbertV3.1-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# 모델 config에서 라벨 추출 (id2label이 없으면 기본값 사용)
+if hasattr(model.config, "id2label"):
+    labels = [label for _, label in sorted(model.config.id2label.items())]
+else:
+    # fallback: default labels
+    labels = ['기쁨', '분노', '불안', '슬픔', '중립']
+
+def predict_sentiment(text):
+    if not text.strip():
+        return "텍스트를 입력해주세요."
+    
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = F.softmax(outputs.logits, dim=1)
+        pred = torch.argmax(probs, dim=1).item()
+        confidence = probs[0][pred].item()
+
+        if pred < len(labels):
+            return f"감정: {labels[pred]} ({confidence:.2%} 확신)"
+        else:
+            return f"감정 인식 실패 (index {pred})"
+
+# Gradio 인터페이스
+gr.Interface(
+    fn=predict_sentiment,
+    inputs="text",
+    outputs="text",
+    title="한국어 감정 분석기",
+    description="문장을 입력하면 감정을 분석해 드립니다. (모델: mdistilbertV3.1)"
+).launch()