Update app.py

app.py

@@ -2,11 +2,12 @@ import streamlit as st
 import pandas as pd
 import requests
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+import os
 
 # 딥러닝 모델 로드
 @st.cache_resource
 def load_model():
-    model_name = "bucketresearch/politicalBiasBERT"  # 정치 성향 분석에 특화된 모델
+    model_name = "bucketresearch/politicalBiasBERT"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForSequenceClassification.from_pretrained(model_name)
     return pipeline("text-classification", model=model, tokenizer=tokenizer)
@@ -15,22 +16,26 @@ def load_model():
 def fetch_naver_news(query, display=5):
     url = "https://openapi.naver.com/v1/search/news.json"
     headers = {
-        "X-Naver-Client-Id": "I_8koTJh3R5l4wLurQbG",
-        "X-Naver-Client-Secret": "W5oWYlAgur",
+        "X-Naver-Client-Id": os.getenv("NAVER_CLIENT_ID"),
+        "X-Naver-Client-Secret": os.getenv("NAVER_CLIENT_SECRET"),
     }
     params = {"query": query, "display": display, "sort": "sim"}
-    response = requests.get(url, headers=headers, params=params)
-    response.raise_for_status()
-    return response.json()
+    try:
+        response = requests.get(url, headers=headers, params=params)
+        response.raise_for_status()  # HTTP 오류 처리
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        st.error(f"API 호출 중 오류 발생: {e}")
+        return None
 
 # 정치 성향 분류
 def classify_sentiment(text, classifier):
     result = classifier(text, truncation=True, max_length=512)
     label = result[0]['label']
     score = result[0]['score']
-    if label in ['LABEL_0', 'LABEL_1']:
+    if label in ['LABEL_0', 'LABEL_1']:  # 라벨에 따라 수정 필요
         return "보수", score
-    elif label in ['LABEL_4']:
+    elif label in ['LABEL_4']:  # 라벨에 따라 수정 필요
         return "진보", score
     else:
         return "중립", score
@@ -71,6 +76,8 @@ if st.button("분석 시작"):
         try:
             # 네이버 뉴스 데이터 수집
             news_data = fetch_naver_news(query, display=10)
+            if news_data is None:
+                return
             news_items = news_data["items"]
 
             # 모델 로드