test 배포

Dockerfile

@@ -1,15 +1,13 @@
-FROM python:3.10-slim AS builder
-
-WORKDIR /install
-COPY requirements.txt ./
-RUN pip install --upgrade pip && \
-    pip install --no-cache-dir --prefix=/install/packages -r requirements.txt
-
 FROM python:3.10-slim
+
 WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app ./app
+
 ENV PYTHONPATH=/app
 
-COPY --from=builder /install/packages /usr/local/
-COPY ./app /app
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
 
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

app/main.py

@@ -1,5 +1,5 @@
 from fastapi import FastAPI
-from routes import skt_classify
+from app.routes import skt_classify
 
 app = FastAPI(title="SKT KoBERT Text Classification API with URL Extraction")
 

app/models/kobert_classifier.py

@@ -9,7 +9,7 @@
 
 # 문장 임베딩 생성 함수
 #테스트용 문장
-
+#ㄱㄱ
 def get_sentence_embedding(text: str):
     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
     with torch.no_grad():

app/server.py

@@ -1,5 +1,4 @@
 import uvicorn
 
 if __name__ == "__main__":
-	# 서버 구동 이후 디버깅을 위해 reload=True 옵션을 추가한다
-    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
+    uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True)

requirements.txt

@@ -5,4 +5,5 @@ transformers
 numpy
 sentencepiece
 protobuf
-scikit-learn
+scikit-learn
+sentence-transformers