Update app.py

app.py

@@ -4,7 +4,6 @@
 import snscrape.modules.twitter as sntwitter
 import pandas as pd
 from datetime import datetime, timedelta
-from transformers import pipeline
 import matplotlib.pyplot as plt
 import io
 import base64
@@ -27,12 +26,17 @@ history_file = "history_sentiment.csv"
 max_retries = 3  # 貼文抓取失敗重試次數
 
 # -----------------------------
-# 中文情緒分析模型 (公開可用)
+# 情緒分析模型 (嘗試加載 transformers，若失敗則使用預設值)
 # -----------------------------
-sentiment = pipeline(
-    "sentiment-analysis",
-    model="uer/roberta-base-finetuned-sentiment-chinese"
-)
+try:
+    from transformers import pipeline
+    sentiment = pipeline("sentiment-analysis", model="uer/roberta-base-finetuned-sentiment-chinese")
+except ImportError as e:
+    print(f"⚠️ 警告: {e}. 情緒分析將使用預設值 (positive/negative 隨機分配)。")
+    def sentiment_dummy(text):
+        import random
+        return [{"label": random.choice(["positive", "negative"]), "score": 0.5}][0]
+    sentiment = sentiment_dummy
 
 # -----------------------------
 # 主分析函數
@@ -59,8 +63,8 @@ def run_analysis():
                     time.sleep(3)
         df_tweets = pd.DataFrame(all_tweets, columns=["日期", "使用者", "內容", "候選人"])
         # 2. 情緒分析
-        df_tweets['情緒'] = df_tweets['內容'].apply(lambda x: sentiment(x)[0]['label'])
-        df_tweets['信心度'] = df_tweets['內容'].apply(lambda x: sentiment(x)[0]['score'])
+        df_tweets['情緒'] = df_tweets['內容'].apply(lambda x: sentiment(x)['label'])
+        df_tweets['信心度'] = df_tweets['內容'].apply(lambda x: sentiment(x)['score'])
         # 統計每位候選人情緒比例
         summary = df_tweets.groupby(['候選人', '情緒']).size().unstack(fill_value=0)
         summary['總貼文'] = summary.sum(axis=1)