remove fuzzy

app.py

@@ -60,35 +60,6 @@ def normalize(text: str) -> str:
     text = text.replace("เแ", "แ").replace("เเ", "แ")
     return text.strip().lower()
 
-def smart_tokenize(text: str) -> list:
-    tokens = word_tokenize(text.strip(), engine="newmm")
-    if not tokens or len("".join(tokens)) < len(text.strip()) * 0.5:
-        return [text.strip()]
-    return tokens
-
-def correct_query_merge_phrases(query: str, whitelist, threshold=75, max_ngram=3):
-    query_norm = normalize(query)
-    tokens = smart_tokenize(query_norm)
-    corrected = []
-    i = 0
-
-    while i < len(tokens):
-        matched = False
-        for n in range(min(max_ngram, len(tokens) - i), 0, -1):
-            phrase = "".join(tokens[i:i+n])
-            match, score, _ = process.extractOne(phrase, whitelist, scorer=fuzz.ratio)
-            if score >= threshold:
-                corrected.append(match)
-                i += n
-                matched = True
-                break
-        if not matched:
-            corrected.append(tokens[i])
-            i += 1
-
-    # ✅ ตัดคำที่มีความยาว 1 ตัวอักษรและไม่ได้อยู่ใน whitelist
-    cleaned = [word for word in corrected if len(word) > 1 or word in whitelist]
-    return " ".join(cleaned)
 
 # 🌟 Main search function
 def search_product(query, model_name):
@@ -97,7 +68,7 @@ def search_product(query, model_name):
         return "<p>❌ ไม่พบโมเดล</p>"
 
     latest_query_result["raw_query"] = query
-    corrected_query = correct_query_merge_phrases(query,keyword_whitelist)
+    corrected_query = normalize(query)
 
     query_embed = model_config[model_name]["func"](corrected_query)
     collection_name = model_config[model_name]["collection"]