添加相关fast

fastapi内网可显示界面.py

@@ -0,0 +1,104 @@
+import os
+from typing import Union, List
+from urllib.parse import urlparse
+
+import pydantic
+import uvicorn
+from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+from starlette import status
+from starlette.middleware.cors import CORSMiddleware
+from starlette.responses import JSONResponse, RedirectResponse
+
+
+async def document():
+    return RedirectResponse(url="/docs")
+
+
+class BaseResponse(BaseModel):
+    code: int = pydantic.Field(200, description="HTTP status code")
+    msg: str = pydantic.Field("success", description="HTTP status message")
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "code": 200,
+                "msg": "success",
+            }
+        }
+
+
+class ListDocsResponse(BaseResponse):
+    data: List[dict] = pydantic.Field(..., description="List of document names")
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "code": 200,
+                "msg": "success",
+                "data": ["doc1.docx", "doc2.pdf", "doc3.txt"],
+            }
+        }
+
+
+# 原文链接：https://blog.csdn.net/wenxingchen/article/details/129013509
+def respSuccessJson(data: Union[list, dict, str] = None, msg: str = "Success"):
+    """ 接口成功返回 """
+    return JSONResponse(
+        status_code=status.HTTP_200_OK,
+        content={
+            'code': 200,
+            'msg': msg,
+            'data': data or {}
+        }
+    )
+
+
+def uri_validator(x):
+    try:
+        result = urlparse(x)
+        return all([result.scheme, result.netloc])
+    except:
+        return False
+
+
+async def cli(audio_path: str, task_id: str):
+    pass
+
+
+def api_start(host, port):
+    global app
+    global local_doc_qa
+
+    app = FastAPI()
+    root = os.path.abspath(os.path.join(os.path.basename(__file__), "../.."))
+    print(root)
+
+    app.mount("/static", StaticFiles(directory=f"{root}/static"), name="static")
+    # 允许跨域
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+    # app.websocket("/local_doc_qa/stream-chat/{knowledge_base_id}")(stream_chat)
+
+    app.get("/", response_model=BaseResponse)(document)
+
+    app.post("/convert-text", response_model=BaseResponse)(cli)
+
+    uvicorn.run(app, host=host, port=port)
+
+
+"""
+解决内网环境 FastAPI访问/docs接口文档显示空白、js/css无法加载
+https://zhuanlan.zhihu.com/p/517645846?utm_id=0
+https://blog.csdn.net/jaket5219999/article/details/135003381
+"""
+if __name__ == '__main__':
+    host = "0.0.0.0"
+    port = 8000
+    api_start(host, port)

qwen官方样例.py

@@ -0,0 +1,128 @@
+# -*- coding: utf-8 -*-
+# -------------------------------
+# @项目：pyProject
+# @文件：qwen官方样例.py
+# @时间：2024/9/9 14:40
+# @作者：xming
+# -------------------------------
+import json
+import os
+
+from qwen_agent.llm import get_chat_model
+
+
+#硬编码返回相同天气的示例虚拟函数
+#在生产中，这可能是您的后端API或外部API
+def get_current_weather(location, unit='fahrenheit'):
+    """Get the current weather in a given location"""
+    if 'tokyo' in location.lower():
+        return json.dumps({
+            'location': 'Tokyo',
+            'temperature': '10',
+            'unit': 'celsius'
+        })
+    elif 'san francisco' in location.lower():
+        return json.dumps({
+            'location': 'San Francisco',
+            'temperature': '72',
+            'unit': 'fahrenheit'
+        })
+    elif 'paris' in location.lower():
+        return json.dumps({
+            'location': 'Paris',
+            'temperature': '22',
+            'unit': 'celsius'
+        })
+    else:
+        return json.dumps({'location': location, 'temperature': 'unknown'})
+
+
+def test():
+    llm = get_chat_model({
+        # 使用DashScope提供的模型服务：
+        'model': 'qwen-max',
+        'model_server': 'dashscope',
+        'api_key': os.getenv('DASHSCOPE_API_KEY'),
+
+        # 使用Together提供的模型服务。人工智能：
+        # 'model': 'Qwen/Qwen2-7B-Instruct',
+        # 'model_server': 'https://api.together.xyz',  # api_base
+        # 'api_key': os.getenv('TOGETHER_API_KEY'),
+
+        # Use your own model service compatible with OpenAI API:
+        # 'model': 'Qwen/Qwen2-72B-Instruct',
+        # 'model_server': 'http://localhost:8000/v1',  # api_base
+        # 'api_key': 'EMPTY',
+    })
+
+    # Step 1:将对话和可用功能发送给模型
+    messages = [{
+        'role': 'user',
+        'content': "What's the weather like in San Francisco?"
+    }]
+    functions = [{
+        'name': 'get_current_weather',
+        'description': 'Get the current weather in a given location',
+        'parameters': {
+            'type': 'object',
+            'properties': {
+                'location': {
+                    'type': 'string',
+                    'description':
+                    'The city and state, e.g. San Francisco, CA',
+                },
+                'unit': {
+                    'type': 'string',
+                    'enum': ['celsius', 'fahrenheit']
+                },
+            },
+            'required': ['location'],
+        },
+    }]
+
+    print('# Assistant Response 1:')
+    responses = []
+    for responses in llm.chat(messages=messages,
+                              functions=functions,
+                              stream=True):
+        print(responses)
+
+    messages.extend(responses)  # 用助理的回复扩展对话
+
+    # Step 2: 检查模型是否要调用函数
+    last_response = messages[-1]
+    if last_response.get('function_call', None):
+
+        # Step 3:调用函数
+        # 注意：JSON响应可能并不总是有效的；一定要处理错误
+        available_functions = {
+            'get_current_weather': get_current_weather,
+        }  # 在这个例子中只有一个函数，但你可以有多个
+        function_name = last_response['function_call']['name']
+        function_to_call = available_functions[function_name]
+        function_args = json.loads(last_response['function_call']['arguments'])
+        function_response = function_to_call(
+            location=function_args.get('location'),
+            unit=function_args.get('unit'),
+        )
+        print('# Function Response:')
+        print(function_response)
+
+        # Step 4: 将每个函数调用和函数响应的信息发送到模型
+        messages.append({
+            'role': 'function',
+            'name': function_name,
+            'content': function_response,
+        })  # 用功能响应扩展对话
+
+        print('# Assistant Response 2:')
+        for responses in llm.chat(
+                messages=messages,
+                functions=functions,
+                stream=True,
+        ):  # 从模型中获取新的响应，在那里可以看到函数响应
+            print(responses)
+
+
+if __name__ == '__main__':
+    test()

文件/删除java文件中注释行.py

@@ -9,7 +9,7 @@
 import re
 
 # 改这个目录！！！
-top_dir = r"C:\Users\zhouhuilin\Desktop\daoda-zhongbao-meta"
+top_dir = r"C:\Users\zhouhuilin\Desktop\test\daoda-zhongbao-meta"
 # 状态
 S_INIT = 0
 S_SLASH = 1 # 斜杠
@@ -32,15 +32,15 @@ def trim_dir(path):
 def trim_file(path):
     print("文件：" + path)
     # 使用 re.match() 函数检查路径是否匹配正则表达式模式
-    if re.match(r".*java", path):
+    if re.match(r".*xml", path):
         print(" 处理")
     else:
         print(" 忽略")
         return
     bak_file = path + ".bak"
     os.rename(path, bak_file)
-    fp_src = open(bak_file,encoding='utf-8')
-    fp_dst = open(path, 'w')
+    fp_src = open(bak_file,encoding='utf-8', errors='replace')
+    fp_dst = open(path, 'w',encoding='utf-8')
     # 初始状态定义为代码
     state = S_INIT
     for line in fp_src.readlines():