init3

Dockerfile

@@ -3,16 +3,17 @@ FROM python:3.11-slim
 WORKDIR /app
 
 ENV TRANSFORMERS_CACHE=/app/cache
+ENV HF_HOME=/app/hf_home
 
-COPY requirements.txt .
+# 캐시 폴더 생성 + 권한 부여
+RUN mkdir -p /app/cache && chmod -R 777 /app/cache
+RUN mkdir -p /app/hf_home && chmod -R 777 /app/hf_home
 
-RUN pip install --no-cache-dir -r requirements.txt
 
-COPY main.py .
+COPY requirements.txt .
+RUN pip install -r requirements.txt
 
-# 캐시 디렉토리 생성
-RUN mkdir -p /app/cache
+COPY main.py .
 
 EXPOSE 7860
-
 CMD ["python", "main.py"]

main.py

@@ -4,6 +4,9 @@ import os
 
 local_path = "./models/roberta-large"
 
+os.environ["HF_HOME"] = "/app/hf_home"
+os.environ["TRANSFORMERS_CACHE"] = "/app/cache"  # 병행 사용 가능
+
 model_id = "klue/roberta-large"
 
 if os.path.exists(local_path):
@@ -12,14 +15,18 @@ if os.path.exists(local_path):
     tokenizer = AutoTokenizer.from_pretrained(local_path)
 else:
     print("⬇️ 모델 허깅페이스에서 다운로드 중...")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id)
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_id, cache_dir=os.environ["HF_HOME"]
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=os.environ["HF_HOME"])
     os.makedirs(local_path, exist_ok=True)
     model.save_pretrained(local_path)
     tokenizer.save_pretrained(local_path)
 
 app = Flask(__name__)
 
+print("🔄 모델 로드 완료")
+
 
 @app.route("/generate", methods=["POST"])
 def generate():