Update app.py

app.py

@@ -1,21 +1,26 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import pipeline
 
-model_name = "google/pegasus-cnn_dailymail"
+print("🚀 요약기 로딩 중...")
 
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+summarizer = pipeline(
+    "summarization",
+    model="facebook/bart-large-cnn"
+)
+
+print("✅ 모델 로딩 완료")
 
 def summarize(text):
-    inputs = tokenizer(text, truncation=True, padding="longest", return_tensors="pt")
-    summary_ids = model.generate(inputs["input_ids"], max_length=100, num_beams=4, early_stopping=True)
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    return summary
+    try:
+        result = summarizer(text, max_length=150, min_length=40, do_sample=False)
+        return result[0]["summary_text"]
+    except Exception as e:
+        return f"❌ 요약 실패: {e}"
 
 gr.Interface(
     fn=summarize,
-    inputs=gr.Textbox(lines=8, placeholder="요약할 긴 글을 입력하세요"),
+    inputs=gr.Textbox(lines=10, placeholder="요약할 텍스트를 입력하세요"),
     outputs="text",
-    title="CW AI 요약 비서 (Pegasus)",
-    description="Pegasus 모델을 기반으로 고정밀 요약을 생성합니다."
+    title="CW 요약 비서 - BART 모델",
+    description="facebook/bart-large-cnn 기반 뉴스/문서 요약기"
 ).launch()