Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# 모델 로드
+tokenizer = AutoTokenizer.from_pretrained("noahkim/KoT5_news_summarization")
+model = AutoModelForSeq2SeqLM.from_pretrained("noahkim/KoT5_news_summarization")
+
+# GPU 설정
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+# 텍스트 요약 함수
+def summarize_text(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt", padding="max_length", truncation=True, max_length=2048)
+    inputs = {key: value.to(device) for key, value in inputs.items()}
+    
+    summary_text_ids = model.generate(
+        input_ids=inputs['input_ids'],
+        attention_mask=inputs['attention_mask'],
+        max_length=512,
+        min_length=128,
+        num_beams=6,
+        repetition_penalty=1.5,
+        no_repeat_ngram_size=15,
+    )
+    
+    summary_text = tokenizer.decode(summary_text_ids[0], skip_special_tokens=True)
+    return summary_text
+
+# Gradio 인터페이스 정의
+iface = gr.Interface(
+    fn=summarize_text, 
+    inputs=gr.Textbox(label="Input Text"), 
+    outputs=gr.Textbox(label="Summary")
+)
+
+# Space에서 바로 실행할 수 있도록 실행
+iface.launch()