diff --git a/AI/Text2Sql/YunXiao.py b/AI/Text2Sql/YunXiao.py
index 25f27c1c..6e251308 100644
--- a/AI/Text2Sql/YunXiao.py
+++ b/AI/Text2Sql/YunXiao.py
@@ -16,47 +16,6 @@ from Util.EchartsUtil import *
 3、应该有类似于 保存为用例，查询历史等功能，让用户方便利旧。
 '''
 
-
-def infer_axes_fields(field_names, sample_data):
-    """
-    使用 AI 大模型推断 X 轴和 Y 轴的字段
-    :param field_names: 数据字段名列表
-    :param sample_data: 数据示例（前几行）
-    :return: X 轴字段, Y 轴字段
-    """
-    client = OpenAI(api_key=MODEL_API_KEY, base_url=MODEL_API_URL)  # 初始化OpenAI客户端
-
-    # 构造提示词
-    prompt = f"""
-    以下是数据的字段名和示例数据：
-    字段名: {field_names}
-    示例数据: {sample_data}
-
-    请根据这些数据，推荐适合用于柱状图的 X 轴和 Y 轴字段。
-    X 轴应为分类字段（如学段、科目、行政区划等），规则如下：
-    1. 如果学段、科目同时存在，返回学段+科目。
-    2. 如果学段、科目都不存在，返回学段。
-    3. 如果只有学段或科目存在，返回存在的字段。
-    Y 轴应为数值字段（如课程数量、数量等）。
-    请直接返回 X 轴和 Y 轴的字段名，格式为：X_轴字段, Y_轴字段
-    """
-
-    # 调用 AI 大模型
-    response = client.chat.completions.create(
-        model=MODEL_NAME,
-        messages=[
-            {"role": "system", "content": "你是一个数据分析助手，帮助用户选择合适的字段生成图表。"},
-            {"role": "user", "content": prompt}
-        ],
-        max_tokens=50
-    )
-
-    # 解析 AI 返回的结果
-    result = response.choices[0].message.content.strip()
-    x_column, y_column = result.split(", ")
-    return x_column, y_column
-
-
 if __name__ == "__main__":
     vn = VannaUtil()
 
@@ -70,7 +29,7 @@ if __name__ == "__main__":
         ddl=ddl
     )
     # 添加有关业务术语或定义的文档
-    vn.train(documentation="Sql/AreaSchoolLesson.md")
+    # vn.train(documentation="Sql/AreaSchoolLesson.md")
 
     # 使用 SQL 进行训练
     with open('Sql/AreaSchoolLessonGenerate.sql', 'r', encoding='utf-8') as file:
@@ -127,26 +86,20 @@ if __name__ == "__main__":
         field_names = list(_data[0].keys()) if _data else []
         sample_data = _data[:3]  # 取前 3 行作为示例数据
 
-        # 推断 X 轴和 Y 轴字段
-        x_column, y_column = infer_axes_fields(field_names, sample_data)
-
-        x_columns = x_column.split('+')
-        y_columns = y_column.split('+')
-
         # 1、生成柱状图
         generate_bar_chart(
             _data=_data,
             title="学段+科目课程数量柱状图",
-            x_columns=x_columns,  # 动态指定 X 轴列
-            y_columns=y_columns,  # 动态指定 Y 轴列
+            x_columns=['学段', '科目'],  # 动态指定 X 轴列
+            y_columns=['课程数量'],  # 动态指定 Y 轴列
             output_file="d:/lesson_bar_chart.html"
         )
         # 2、生成饼状图
         generate_pie_chart(
             _data=_data,
             title="学段+科目分布",
-            category_columns=x_columns,  # 多列组合参数
-            value_column=y_columns[0],
+            category_columns=['学段', '科目'],  # 多列组合参数
+            value_column='课程数量',
             output_file="d:/lesson_pie_chart.html"
         )
 
diff --git a/AI/Text2Sql/__pycache__/app.cpython-310.pyc b/AI/Text2Sql/__pycache__/app.cpython-310.pyc
new file mode 100644
index 00000000..df88b321
Binary files /dev/null and b/AI/Text2Sql/__pycache__/app.cpython-310.pyc differ
diff --git a/AI/Text2Sql/app.py b/AI/Text2Sql/app.py
new file mode 100644
index 00000000..28e50597
--- /dev/null
+++ b/AI/Text2Sql/app.py
@@ -0,0 +1,188 @@
+import re
+
+from fastapi import FastAPI, HTTPException, Depends
+from pydantic import BaseModel
+from sqlalchemy import create_engine, Column, Integer, String, SmallInteger, Date
+from sqlalchemy.orm import declarative_base  # 更新导入路径
+from sqlalchemy.orm import sessionmaker, Session
+import uvicorn  # 导入 uvicorn
+from starlette.staticfiles import StaticFiles
+
+from Text2Sql.Util.VannaUtil import VannaUtil
+
+# 数据库连接配置
+DATABASE_URL = "postgresql+psycopg2://postgres:DsideaL147258369@10.10.14.71:5432/szjz_db"
+
+# 创建数据库引擎
+engine = create_engine(DATABASE_URL)
+
+# 创建 SessionLocal 类
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+# 创建 Base 类
+Base = declarative_base()
+
+# 定义 t_bi_question 表模型
+class TBIQuestion(Base):
+    __tablename__ = "t_bi_question"
+
+    id = Column(Integer, primary_key=True, index=True)
+    question = Column(String(255), nullable=False)
+    state_id = Column(SmallInteger, nullable=False, default=0)
+    sql = Column(String(2048))
+    bar_chart_x_columns = Column(String(255))
+    bar_chart_y_columns = Column(String(255))
+    pie_chart_category_columns = Column(String(255))
+    pie_chart_value_column = Column(String(255))
+    session_id = Column(String(255), nullable=False)
+    excel_file_name = Column(String(255))
+    bar_chart_file_name = Column(String(255))
+    pie_chart_file_name = Column(String(255))
+    report_file_name = Column(String(255))
+    create_time = Column(Date, nullable=False, default="CURRENT_TIMESTAMP")
+    is_system = Column(SmallInteger, nullable=False, default=0)
+    is_collect = Column(SmallInteger, nullable=False, default=0)
+
+# 创建 Pydantic 模型
+class TBIQuestionCreate(BaseModel):
+    question: str
+    state_id: int = 0
+    sql: str = None
+    bar_chart_x_columns: str = None
+    bar_chart_y_columns: str = None
+    pie_chart_category_columns: str = None
+    pie_chart_value_column: str = None
+    session_id: str
+    excel_file_name: str = None
+    bar_chart_file_name: str = None
+    pie_chart_file_name: str = None
+    report_file_name: str = None
+    is_system: int = 0
+    is_collect: int = 0
+
+class TBIQuestionUpdate(BaseModel):
+    question: str = None
+    state_id: int = None
+    sql: str = None
+    bar_chart_x_columns: str = None
+    bar_chart_y_columns: str = None
+    pie_chart_category_columns: str = None
+    pie_chart_value_column: str = None
+    session_id: str = None
+    excel_file_name: str = None
+    bar_chart_file_name: str = None
+    pie_chart_file_name: str = None
+    report_file_name: str = None
+    is_system: int = None
+    is_collect: int = None
+
+# 初始化 FastAPI
+app = FastAPI()
+
+@app.get("/")
+def read_root():
+    return {"message": "Hello, World!"}
+
+# 获取数据库会话
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+# 创建记录
+@app.post("/questions/", response_model=TBIQuestionCreate)
+def create_question(question: TBIQuestionCreate, db: Session = Depends(get_db)):
+    db_question = TBIQuestion(**question.dict())
+    db.add(db_question)
+    db.commit()
+    db.refresh(db_question)
+    return db_question
+
+# 读取记录
+@app.get("/questions/{question_id}", response_model=TBIQuestionCreate)
+def read_question(question_id: int, db: Session = Depends(get_db)):
+    db_question = db.query(TBIQuestion).filter(TBIQuestion.id == question_id).first()
+    if db_question is None:
+        raise HTTPException(status_code=404, detail="Question not found")
+    return db_question
+
+# 更新记录
+@app.put("/questions/{question_id}", response_model=TBIQuestionCreate)
+def update_question(question_id: int, question: TBIQuestionUpdate, db: Session = Depends(get_db)):
+    db_question = db.query(TBIQuestion).filter(TBIQuestion.id == question_id).first()
+    if db_question is None:
+        raise HTTPException(status_code=404, detail="Question not found")
+    for key, value in question.dict().items():
+        if value is not None:
+            setattr(db_question, key, value)
+    db.commit()
+    db.refresh(db_question)
+    return db_question
+
+# 删除记录
+@app.delete("/questions/{question_id}")
+def delete_question(question_id: int, db: Session = Depends(get_db)):
+    db_question = db.query(TBIQuestion).filter(TBIQuestion.id == question_id).first()
+    if db_question is None:
+        raise HTTPException(status_code=404, detail="Question not found")
+    db.delete(db_question)
+    db.commit()
+    return {"message": "Question deleted successfully"}
+
+@app.post("/questions/generate_sql")
+def generate_sql(question: str):
+    # 指定学段
+    question = '''
+                    查询:
+                    1、发布时间是2024年度
+                    2、每个学段，每个科目，上传课程数量，按由多到少排序
+                    3、字段名: 学段,科目,排名,课程数量
+                    '''
+    common_prompt = '''
+        返回的信息要求：
+        1、行政区划为NULL 或者是空字符的不参加统计
+        2、目标数据库是Postgresql 16
+        '''
+    question = question + common_prompt
+    # 开始查询
+    print("开始查询...")
+    # 获取完整 SQL
+    sql = vn.generate_sql(question)
+    print("生成的查询 SQL:\n", sql)
+
+# 添加 main 函数
+def main():
+    # 开始训练
+    print("开始训练...")
+    # 打开AreaSchoolLesson.sql文件内容
+    with open("Sql/AreaSchoolLessonDDL.sql", "r", encoding="utf-8") as file:
+        ddl = file.read()
+    # 训练数据
+    vn.train(
+        ddl=ddl
+    )
+    # 添加有关业务术语或定义的文档
+    # vn.train(documentation="Sql/AreaSchoolLesson.md")
+
+    # 使用 SQL 进行训练
+    with open('Sql/AreaSchoolLessonGenerate.sql', 'r', encoding='utf-8') as file:
+        sql_content = file.read()
+    # 使用正则表达式提取注释和 SQL 语句
+    sql_pattern = r'/\*(.*?)\*/(.*?);'
+    sql_snippets = re.findall(sql_pattern, sql_content, re.DOTALL)
+
+    # 打印提取的注释和 SQL 语句
+    for i, (comment, sql) in enumerate(sql_snippets, 1):
+        vn.train(sql=comment.strip() + '\n' + sql.strip() + '\n')
+
+
+    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)
+
+
+
+# 确保直接运行脚本时启动 FastAPI 应用
+if __name__ == "__main__":
+    vn = VannaUtil()
+    main()
\ No newline at end of file