diff --git a/exp_multimodal/exp_multimodal_gui.py b/exp_multimodal/exp_multimodal_gui.py
index c57e14f..7b6ac23 100644
--- a/exp_multimodal/exp_multimodal_gui.py
+++ b/exp_multimodal/exp_multimodal_gui.py
@@ -15,7 +15,7 @@ from tkinterdnd2 import DND_FILES, TkinterDnD
 from exp_multimodal.labels import build_labels, _normalize, _base_ingredient
 from exp_multimodal.vlm_classifier import classify_image
 from exp_multimodal.ollama_client import OLLAMA_URL as DEFAULT_OLLAMA_URL, DEFAULT_MODEL as DEFAULT_VLM_MODEL
-import ollama_client as _ollama_mod
+from exp_multimodal import ollama_client as _ollama_mod
 
 
 ctk.set_appearance_mode("System")
diff --git a/exp_multimodal/prompts.py b/exp_multimodal/prompts.py
index adaaf84..a612d46 100644
--- a/exp_multimodal/prompts.py
+++ b/exp_multimodal/prompts.py
@@ -22,13 +22,19 @@ def build_closedset_prompt(
     )
 
     return (
-        "你是食品图像分类专家。请严格在给定的标签集合中选择最符合图片内容的一个标签。\n"
-        "只允许返回一个标签，禁止输出集合外的词。请同时给出0-1置信度。\n"
+        "【重要】你必须且只能输出JSON格式，严禁输出任何其他内容（包括英文描述、解释、分析等）。\n\n"
+        "你是食品图像分类专家。任务：从给定标签集合中选择最符合图片的一个标签。\n"
+        f"标签集合（封闭集，中文）：{label_str}\n\n"
         f"{constraint}"
-        f"标签集合（封闭集，中文）：{label_str}\n"
         f"{hints}\n"
-        '请仅输出合法 JSON：{"label":"<标签>","confidence":0-1}\n'
-        "不要输出任何额外解释或文本。"
+        "要求：\n"
+        "1. 必须从标签集合中选择，禁止输出集合外的词\n"
+        "2. 给出0-1的置信度分数\n"
+        "3. 严格按以下JSON格式输出，不允许有任何偏差\n\n"
+        '输出格式（必须严格遵守）：\n{"label":"<标签>","confidence":0.95}\n\n'
+        "4. 如果明显不在这些分类中，输出格式如下："
+        ' \n{"label":"<未知>","confidence":0.95}\n\n'
+        "【再次强调】只输出上述JSON，不要有任何额外文字、描述、解释或其他内容。"
     )
 
 
diff --git a/exp_multimodal/vlm_classifier.py b/exp_multimodal/vlm_classifier.py
index bd88149..7a2e024 100644
--- a/exp_multimodal/vlm_classifier.py
+++ b/exp_multimodal/vlm_classifier.py
@@ -49,8 +49,10 @@ def classify_image(
         f"ingredient_only={ingredient_only} fewshot={bool(fewshot_hints)}"
     )
     prompt = build_closedset_prompt(labels, fewshot_hints, ingredient_only)
-    _preview_prompt = (prompt[:120]).replace("\n", " ")
-    print(f"[VLM] Prompt length={len(prompt)} preview={_preview_prompt}...")
+    # _preview_prompt = (prompt[:120]).replace("\n", " ")
+    final_prompt = prompt.replace("\n", " ")
+    # print(f"[VLM] Prompt length={len(prompt)} preview={_preview_prompt}...")
+    print(f"[VLM] Prompt length={len(prompt)} preview={final_prompt}")
 
     print("[VLM] Calling chat_vision...")
     text = chat_vision(prompt, [image_path], temperature=0.1)