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-# 1. 파이썬 버전을 3.11으로 설정
-FROM python:3.11-slim
-
-# 2. 필요한 시스템 패키지 및 git-lfs 설치
-RUN pip install --no-cache-dir hf_transfer>=0.1.6
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    git-lfs \
-    && rm -rf /var/lib/apt/lists/*
-
-# Ollama 설치
-RUN curl -fsSL https://ollama.com/install.sh | sh
-
-# ✅ 핵심 변경: /tmp 디렉토리 사용
-ENV OLLAMA_MODEL=gemma2:9b
-ENV OLLAMA_HOME=/tmp/ollama-home
-ENV OLLAMA_MODELS=/tmp/ollama-home
-
-# 빌드 중 모델 다운로드 (/tmp 사용)
-RUN set -eux; \
-    mkdir -p /tmp/ollama-home; \
-    OLLAMA_HOME=/tmp/ollama-home OLLAMA_MODELS=/tmp/ollama-home ollama serve & pid=$!; \
-    i=0; \
-    until curl -fsS http://127.0.0.1:11434/api/tags >/dev/null 2>&1; do \
-      i=$((i+1)); \
-      [ "$i" -lt 60 ] || { echo "Ollama not ready"; kill "$pid"; exit 1; }; \
-      sleep 1; \
-    done; \
-    OLLAMA_HOME=/tmp/ollama-home OLLAMA_MODELS=/tmp/ollama-home ollama pull gemma2:9b; \
-    kill "$pid" || true
-
-# 3. 작업 디렉터리 설정
-WORKDIR /app
-
-# 6. 파이썬 라이브러리 설치
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-# 4. 소스 코드 복사
-COPY . /app
-
-# 5. Git LFS 설정 및 대용량 파일 다운로드
-RUN git lfs install && git lfs pull || true
-
-# 환경변수 설정
-ENV HOME=/app
-ENV STREAMLIT_HOME=/app/.streamlit
-RUN mkdir -p /app/.streamlit && chmod -R 777 /app/.streamlit
-
-# 캐시 경로도 /tmp로 통일
-ENV HF_HOME=/tmp/hf-home \
-    TRANSFORMERS_CACHE=/tmp/hf-cache \
-    HUGGINGFACE_HUB_CACHE=/tmp/hf-cache \
-    TORCH_HOME=/tmp/torch-cache \
-    XDG_CACHE_HOME=/tmp/xdg-cache
-
-ENV OLLAMA_HOST=http://127.0.0.1:11434
-EXPOSE 8501
-EXPOSE 11434
-
-# ✅ 간단하고 안전한 CMD
-CMD bash -lc '\
-  export OLLAMA_MODEL=${OLLAMA_MODEL:-gemma2:9b}; \
-  export OLLAMA_HOME=/tmp/ollama-home; \
-  export OLLAMA_MODELS=/tmp/ollama-home; \
-  echo "Starting Ollama with model: $OLLAMA_MODEL"; \
-  echo "Using Ollama home: $OLLAMA_HOME"; \
-  mkdir -p /tmp/ollama-home; \
-  ollama serve & \
-  for i in {1..120}; do \
-    if curl -fsS http://127.0.0.1:11434/api/version >/dev/null 2>&1; then \
-      echo "Ollama server is ready"; \
-      break; \
-    fi; \
-    echo "Waiting for Ollama... ($i/120)"; \
-    sleep 1; \
-  done; \
-  echo "Checking if model exists..."; \
-  if ! ollama list | grep -q "$OLLAMA_MODEL"; then \
-    echo "Model not found, pulling: $OLLAMA_MODEL"; \
-    ollama pull "$OLLAMA_MODEL" || { echo "Failed to pull model"; exit 1; }; \
-  else \
-    echo "Model already available: $OLLAMA_MODEL"; \
-  fi; \
-  echo "Starting Streamlit app"; \
-  streamlit run app.py --server.address=0.0.0.0 --server.port=${PORT:-8501}'