Update app.py

app.py

@@ -1,11 +1,18 @@
 import streamlit as st
-from func import fetch_news, analyze_sentiment, extract_org_entities
-import time
 
-# 必须放在最前
+# ✅ 必须作为第一个 Streamlit 命令
 st.set_page_config(page_title="Stock News Sentiment Analysis", layout="centered")
 
-# ----------- Header Section with Logo and Title (Safe for all environments) -----------
+from func import (
+    get_sentiment_pipeline,
+    get_ner_pipeline,
+    fetch_news,
+    analyze_sentiment,
+    extract_org_entities,
+)
+import time
+
+# ----------- Header Section with Logo and Title -----------
 col1, col2 = st.columns([1, 10])
 with col1:
     st.image(
@@ -15,7 +22,7 @@ with col1:
 with col2:
     st.markdown("<h1 style='margin-bottom: 0;'>Stock News Sentiment Analysis</h1>", unsafe_allow_html=True)
 
-# ----------- Description Below Title -----------
+# ----------- Description -----------
 st.markdown("""
 <p style='font-size:17px; margin-top: 0.5rem;'>
     Analyze the latest news sentiment of companies mentioned in your input.
@@ -28,20 +35,22 @@ st.markdown("""
 </p>
 """, unsafe_allow_html=True)
 
-# ----------- User Input -----------
+# ----------- Input Area -----------
 free_text = st.text_area("Enter text mentioning companies:", height=100)
-tickers = extract_org_entities(free_text)
+ner_pipeline = get_ner_pipeline()
+tickers = extract_org_entities(free_text, ner_pipeline)
 
 if tickers:
     st.markdown(f"\U0001F50E **Identified Tickers:** `{', '.join(tickers)}`")
 else:
     tickers = []
 
-# ----------- Button Trigger Logic -----------
+# ----------- Action Button -----------
 if st.button("Get News and Sentiment"):
     if not tickers:
         st.warning("Please mention at least one recognizable company.")
     else:
+        sentiment_pipeline = get_sentiment_pipeline()
         progress_bar = st.progress(0)
         total_stocks = len(tickers)
 
@@ -52,7 +61,7 @@ if st.button("Get News and Sentiment"):
             if news_list:
                 sentiments = []
                 for news in news_list:
-                    sentiment = analyze_sentiment(news['title'])
+                    sentiment = analyze_sentiment(news['title'], sentiment_pipeline)
                     sentiments.append(sentiment)
 
                 positive_count = sentiments.count("Positive")