remove merge_common_prefixes until the best implementation found

app.py

@@ -27,33 +27,6 @@ MODEL_LIST = [
     "Epiculous/Violet_Twilight-v0.2",
 ]
 
-def merge_common_prefixes(suggestions, min_len=2):
-    """
-    合併具有共同前綴的建議：
-    - 找出所有長度 ≥ min_len 的共同前綴
-    - 將這些前綴作為新建議，移除原有被合併的項目
-    """
-    prefixes = []
-    to_remove = set()
-
-    for i in range(len(suggestions)):
-        for j in range(i+1, len(suggestions)):
-            s1, s2 = suggestions[i], suggestions[j]
-            # 計算字元級共同前綴
-            common = ''.join(c1 for c1, c2 in zip(s1, s2) if c1 == c2)
-            if len(common) >= min_len:
-                prefixes.append(common)
-                to_remove.update([s1, s2])
-
-    # 去重前綴
-    unique_prefixes = []
-    for p in prefixes:
-        if p not in unique_prefixes:
-            unique_prefixes.append(p)
-
-    # 剩下沒有被合併的建議
-    remainder = [s for s in suggestions if s not in to_remove]
-    return unique_prefixes + remainder
 
 @lru_cache(maxsize=8)
 def get_pipeline(model_name):
@@ -111,7 +84,7 @@ def suggest_next(text, model_name, k, m, num_beam_groups, diversity_penalty):
 
     # 合併共同前綴
     cprint(f'unique_suggestions: {unique_suggestions}','blue')
-    merged_prefixes = merge_common_prefixes(unique_suggestions, min_len=1)
+    merged_prefixes = unique_suggestions
     cprint(f"merged_prefixes: {merged_prefixes}",'red')
 
     # 最終去重並移除空項 (基於 strip 後內容)