# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("snunlp/KR-FinBert-SC")
model = AutoModelForSequenceClassification.from_pretrained("snunlp/KR-FinBert-SC")

import os
import tensorflow as tf
from absl import logging

# 환경 변수 설정
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'  # oneDNN 최적화 비활성화

# 로그 초기화
logging.set_verbosity(logging.INFO)
logging.use_absl_handler()

# GPU 설정
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        print("GPU 메모리 증가 허용 설정 완료")
    except RuntimeError as e:
        print(f"GPU 설정 오류: {e}")

# TensorFlow 및 시스템 정보 확인
print("TensorFlow 버전:", tf.__version__)
print("사용 가능한 장치:", tf.config.list_physical_devices())

import os
import tensorflow as tf

# oneDNN 최적화 비활성화
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'

# GPU 비활성화 (CUDA 문제 해결 시)
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

# TensorFlow GPU 메모리 설정
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        print("GPU 메모리 증가 허용 설정 완료")
    except RuntimeError as e:
        print(f"GPU 설정 오류: {e}")

# TensorFlow 실행 확인
print("TensorFlow 버전:", tf.__version__)
print("사용 가능한 장치:", tf.config.list_physical_devices())

# Base image
FROM python:3.10-slim

# Install Python packages
RUN pip install --no-cache-dir pip==22.3.1 \
    && pip install --no-cache-dir datasets "huggingface-hub>=0.19" \
    "hf-transfer>=0.1.4" "protobuf<4" "click<8.1" "pydantic~=1.0"

# Install system packages
RUN apt-get update && apt-get install -y \
    git git-lfs ffmpeg libsm6 libxext6 cmake rsync libgl1-mesa-glx \
    && rm -rf /var/lib/apt/lists/*

# Work directory
WORKDIR /home/user/app

# Copy requirements and install additional Python packages
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY . .

# Set default command
CMD ["streamlit", "run", "app.py"]

import streamlit as st

st.title("Hello, Streamlit!")
st.write("This is a sample Streamlit app.")