From cab782bce590e9aa93ba284ecadc53972ddc8fe0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E7=9F=A5=E5=BE=AE?= <zhiwei@mofin.local>
Date: Sun, 21 Jun 2026 01:34:22 +0800
Subject: [PATCH] =?UTF-8?q?fallback:=20=E5=B0=8F=E6=9E=9CAPI=E4=B8=8D?=
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---
 xiaoguo_news_processor.py | 46 +++++++++++++++++++++++++++++----------
 1 file changed, 35 insertions(+), 11 deletions(-)

diff --git a/xiaoguo_news_processor.py b/xiaoguo_news_processor.py
index 8d361f3..a29bff2 100644
--- a/xiaoguo_news_processor.py
+++ b/xiaoguo_news_processor.py
@@ -138,6 +138,29 @@ def translate_sentiment(s):
     return m.get(s.lower() if isinstance(s, str) else "", s)
 
 
+def fallback_classify(batch):
+    """关键词降级分类（小果API不可用时）"""
+    positive_kw = ['突破', '增长', '利好', '加单', '订单', '放量', '新高', '获批', '量产',
+                   '超预期', '投产', '融资', '增持', '回购', '降息', '减税', '补贴',
+                   '国产替代', '自主可控', '准入']
+    negative_kw = ['管制', '限制', '制裁', '利空', '减持', '抛售', '下跌', '跌停',
+                   '风险', '违约', '调查', '暂停', '取消', '下滑', '亏损', '裁员',
+                   '诉讼', '退市', '做空', '关税', '禁令']
+
+    for a in batch:
+        text = a['title'] + (a.get('content') or '')
+        pos = sum(1 for kw in positive_kw if kw in text)
+        neg = sum(1 for kw in negative_kw if kw in text)
+        if pos > neg:
+            a['sentiment'] = '利好'
+        elif neg > pos:
+            a['sentiment'] = '利空'
+        else:
+            a['sentiment'] = '中性'
+        a['summary'] = a['title'][:80]
+    return batch
+
+
 def main():
     conn = get_conn()
     signals = conn.execute(
@@ -199,17 +222,18 @@ def main():
 
     results = call_xiaoguo(batch)
     if not results:
-        print("  小果分析失败", flush=True)
-        conn.close()
-        return
-
-    # 合并结果（用索引位置匹配）
-    for i, r in enumerate(results):
-        if i < len(batch):
-            batch[i]["sentiment"] = translate_sentiment(r.get("sentiment", r.get("情感", "")))
-            batch[i]["summary"] = r.get("summary", r.get("摘要", ""))
-        else:
-            break
+        print("  小果API不可用，降级到关键词分类", flush=True)
+        fallback_classify(batch)
+        results = None  # batch already has sentiment/summary set
+    
+    if results and isinstance(results, list):
+        # 小果LLM返回结果，按索引匹配
+        for i, r in enumerate(results):
+            if i < len(batch):
+                batch[i]["sentiment"] = translate_sentiment(r.get("sentiment", r.get("情感", "")))
+                batch[i]["summary"] = r.get("summary", r.get("摘要", ""))
+            else:
+                break
 
     # 汇总情感
     sentiments = [a.get("sentiment", "中性") for a in batch if a.get("sentiment")]