diff --git a/api/mian.py b/api/mian.py
index b2fe016..ec9fdbd 100644
--- a/api/mian.py
+++ b/api/mian.py
@@ -9,12 +9,12 @@ import paddle.nn.functional as F  # 用于 Softmax
 from typing import List, Dict
 from pydantic import ValidationError
 
-from api.intentRecognition import IntentRecognition
-from api.slotRecognition import SlotRecognition
+from intentRecognition import IntentRecognition
+from slotRecognition import SlotRecognition
 
 # 常量
-MODEL_ERNIE_PATH = R"E:\workingSpace\PycharmProjects\Intention_dev\ernie\output\checkpoint-4160"
-MODEL_UIE_PATH = R"E:\workingSpace\PycharmProjects\Intention_dev\uie\uie_ner\checkpoint-4320"
+MODEL_ERNIE_PATH = R"../ernie/output/checkpoint-4160"
+MODEL_UIE_PATH = R"../uie/output/checkpoint-4320"
 
 # 类别名称列表
 labels = [
@@ -36,7 +36,6 @@ label_map = {
     10: 'B-riskLevel', 20: 'I-riskLevel'
 }
 
-
 # 初始化工具类
 intent_recognizer = IntentRecognition(MODEL_ERNIE_PATH, labels)
 
@@ -45,6 +44,7 @@ slot_recognizer = SlotRecognition(MODEL_UIE_PATH, label_map)
 # 设置Flask应用
 app = Flask(__name__)
 
+
 # 统一的异常处理函数
 @app.errorhandler(Exception)
 def handle_exception(e):
@@ -117,7 +117,7 @@ def intent_reco():
             return user_validation_error
 
         # 调用predict方法进行意图识别
-        predicted_label, predicted_probability,predicted_id = intent_recognizer.predict(text)
+        predicted_label, predicted_probability, predicted_id = intent_recognizer.predict(text)
 
         return jsonify(
             code=200,
@@ -190,19 +190,30 @@ def agent():
             predicted_label, predicted_probability, predicted_id = intent_recognizer.predict(query)
             # 再进行槽位抽取
             entities = slot_recognizer.recognize(query)
-            status, sk = check_lost(predicted_label, entities)
 
-            # 返回意图和槽位识别的结果
-            return jsonify({
-                "code": 200,
-                "msg": "成功",
-                "answer": {
-                    "int": predicted_id,
-                    "label": predicted_label,
-                    "probability": predicted_probability,
-                    "slot": entities
-                },
-            })
+            print(f"意图识别后的label:{predicted_label}, id:{predicted_id},槽位抽取后的实体：{entities},message:{messages}")
+            #必须槽位缺失检查
+            status, sk = check_lost(predicted_id, entities)
+            if status == 1:
+                return jsonify({
+                    "code": 200,
+                    "msg": "成功",
+                    "answer": {
+                        "miss": sk
+                    },
+                })
+
+            else:
+                return jsonify({
+                    "code": 200,
+                    "msg": "成功",
+                    "answer": {
+                        "int": predicted_id,
+                        "label": predicted_label,
+                        "probability": predicted_probability,
+                        "slot": entities
+                    },
+                })
 
         # 如果是后续轮次（多轮对话），这里只做示例，可能需要根据具体需求进行处理
         else:
@@ -219,27 +230,24 @@ def agent():
         return jsonify({"error": str(e)}), 500  # 捕捉其他错误并返回
 
 
+# def check_lost(int_res, slot):
+#     return 0, ""
 def check_lost(int_res, slot):
-    # mapping = {
-    #     "页面切换":[['页面','应用']],
-    #     "作业计划数量查询":[['时间']],
-    #     "周计划查询":[['时间']],
-    #     "作业内容":[['时间']],
-    #     "施工人数":[['时间']],
-    #     "作业考勤人数":[['时间']],
-    # }
+    #labels: ["天气查询","互联网查询","页面切换","日计划数量查询","周计划数量查询","日计划作业内容","周计划作业内容","施工人数","作业考勤人数","知识问答"]
     mapping = {
-        1: [['date', 'area']],
-        3: [['page'], ['app'], ['module']],
+        2: [['page'], ['app'], ['module']],
+        3: [['date']],
         4: [['date']],
         5: [['date']],
         6: [['date']],
         7: [['date']],
-        8: [[]],
-        9: [[]],
+        8: [['date']],
     }
+    #3:"页面切换",
+    intention_mapping = {2: "页面切换", 3: "日计划数量查询", 4: "周计划数量查询", 5: "日计划作业内容",
+                        6: "周计划作业内容",7: "施工人数",8: "作业考勤人数"}
     if not mapping.__contains__(int_res):
-        return 0, []
+        return 0, ""
     cur_k = list(slot.keys())
     idx = -1
     idx_len = 99
@@ -258,8 +266,12 @@ def check_lost(int_res, slot):
     if idx_len == 0:  # 匹配通过
         return 0, cur_k
     left = [x for x in mapping[int_res][idx] if x not in cur_k]
-    return 1, left  # mapping[int_res][idx]
+    apologize_str = "非常抱歉，"
+    if int_res == 2:
+        return 1, f"{apologize_str}请问你想查询哪个页面？"
+    elif int_res in [3, 4, 5, 6, 7, 8]:
+        return 1, f"{apologize_str}请问你想查询什么时间的{intention_mapping[int_res]}？"
 
 
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5000, debug=True)
+    app.run(host='0.0.0.0', port=18074, debug=True)
diff --git a/uie/train.py b/uie/train.py
index 269ed31..491b160 100644
--- a/uie/train.py
+++ b/uie/train.py
@@ -63,8 +63,8 @@ def preprocess_function(example, tokenizer):
 
 
 # === 3. 加载 UIE 预训练模型 ===
-model = ErnieForTokenClassification.from_pretrained("uie-base", num_classes=21)  # 3 类 (O, B, I)
-tokenizer = ErnieTokenizer.from_pretrained("uie-base")
+model = ErnieForTokenClassification.from_pretrained(r"/mnt/d/weiweiwang/intention/models/uie-base", num_classes=21)  # 3 类 (O, B, I)
+tokenizer = ErnieTokenizer.from_pretrained(r"/mnt/d/weiweiwang/intention/models/uie-base")
 
 # === 4. 加载数据集 ===
 train_dataset = load_dataset("data/data_part1.json")  # 训练数据集
@@ -81,7 +81,7 @@ data_collator = DataCollatorForTokenClassification(tokenizer, padding=True)
 
 # === 7. 训练参数 ===
 training_args = TrainingArguments(
-    output_dir="./uie_ner",
+    output_dir="./output",
     evaluation_strategy="epoch",
     save_strategy="epoch",
     per_device_train_batch_size=16,  # 你的显存较大，可调整 batch_size