Update app.py

app.py

@@ -1,4 +1,3 @@
-# Hugging Face 모델 로드
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import streamlit as st
 import os
@@ -11,21 +10,12 @@ model = AutoModelForSequenceClassification.from_pretrained("snunlp/KR-FinBert-SC
 
 # 환경 변수 설정
 os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'  # oneDNN 최적화 비활성화
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # GPU 비활성화 (필요 시)
 
 # 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 정보 출력
 print("TensorFlow 버전:", tf.__version__)
 print("사용 가능한 장치:", tf.config.list_physical_devices())
@@ -37,6 +27,9 @@ st.write("This is a sample Streamlit app.")
 # 입력 필드 추가
 input_text = st.text_input("Enter some text:")
 if st.button("Analyze"):
-    inputs = tokenizer(input_text, return_tensors="pt")
-    outputs = model(**inputs)
-    st.write("Model Output:", outputs.logits.tolist())
+    try:
+        inputs = tokenizer(input_text, return_tensors="pt")
+        outputs = model(**inputs)
+        st.write("Model Output:", outputs.logits.tolist())
+    except Exception as e:
+        st.error(f"Error during model inference: {e}")