Rename app.py to package-lock.json

app.py

@@ -1,271 +0,0 @@
-import os
-import time
-import json
-import grpc
-import asyncio
-from typing import List, Optional
-from fastapi import FastAPI, HTTPException, Request
-from fastapi.responses import JSONResponse, StreamingResponse
-from pydantic import BaseModel
-from dotenv import load_dotenv
-from grpc_tools import protoc
-from grpc._channel import _MultiThreadedRendezvous
-import re
-import importlib
-from google.protobuf.json_format import MessageToDict
-import uuid
-
-# 加载环境变量
-load_dotenv()
-
-# 配置类
-class Config:
-    def __init__(self):
-        self.API_PREFIX = os.getenv('API_PREFIX', '/')
-        self.API_KEY = os.getenv('API_KEY', '')
-        self.MAX_RETRY_COUNT = int(os.getenv('MAX_RETRY_COUNT', 3))
-        self.RETRY_DELAY = int(os.getenv('RETRY_DELAY', 5000))
-        self.COMMON_GRPC = 'runtime-native-io-vertex-inference-grpc-service-lmuw6mcn3q-ul.a.run.app'
-        self.COMMON_PROTO = 'protos/VertexInferenceService.proto'
-        self.GPT_GRPC = 'runtime-native-io-gpt-inference-grpc-service-lmuw6mcn3q-ul.a.run.app'
-        self.GPT_PROTO = 'protos/GPTInferenceService.proto'
-        self.PORT = int(os.getenv('PORT', 8787))
-        self.SUPPORTED_MODELS = [
-            "gpt-4o-mini", "gpt-4o", "gpt-4-turbo", "gpt-4", "gpt-3.5-turbo",
-            "claude-3-sonnet@20240229", "claude-3-opus@20240229", "claude-3-haiku@20240307",
-            "claude-3-5-sonnet@20240620", "gemini-1.5-flash", "gemini-1.5-pro",
-            "chat-bison", "codechat-bison"
-        ]
-
-    def is_valid_model(self, model):
-        regex_input = r'^(claude-3-(5-sonnet|haiku|sonnet|opus))-(\d{8})$'
-        match_input = re.match(regex_input, model)
-        normalized_model = f"{match_input.group(1)}@{match_input.group(3)}" if match_input else model
-        return normalized_model in self.SUPPORTED_MODELS
-
-# gRPC处理类
-class GRPCHandler:
-    def __init__(self, proto_file):
-        self.proto_file = proto_file
-        self._compile_proto()
-        self._load_proto()
-
-    def _compile_proto(self):
-        proto_dir = os.path.dirname(self.proto_file)
-        proto_file = os.path.basename(self.proto_file)
-        protoc.main((
-            '',
-            f'-I{proto_dir}',
-            f'--python_out=.',
-            f'--grpc_python_out=.',
-            os.path.join(proto_dir, proto_file)
-        ))
-
-    def _load_proto(self):
-        module_name = os.path.splitext(os.path.basename(self.proto_file))[0]
-        grpc_module = importlib.import_module(f"{module_name}_pb2_grpc")
-        pb2_module = importlib.import_module(f"{module_name}_pb2")
-        
-        if 'GPT' in module_name:
-            self.stub_class = getattr(grpc_module, "GPTInferenceServiceStub")
-        else:
-            self.stub_class = getattr(grpc_module, "VertexInferenceServiceStub")
-        
-        self.requests_class = getattr(pb2_module, "Requests")
-        self.response_class = getattr(pb2_module, "Response")
-        self.messages_class = getattr(pb2_module, "Messages")
-        self.args_class = getattr(pb2_module, "Args")
-
-    def _build_request(self, model, content, rules, temperature, top_p):
-        messages = self.messages_class(unknown=1, message=content)
-        args = self.args_class(messages=messages, rules=rules)
-        return self.requests_class(
-            models=model,
-            args=args
-        )
-
-    async def grpc_to_pieces(self, model, content, rules, temperature, top_p):
-        metadata = [
-            ('user-agent', 'dart-grpc/2.0.0')
-        ]
-        channel = grpc.aio.secure_channel(
-            config.COMMON_GRPC,
-            grpc.ssl_channel_credentials()
-        )
-        stub = self.stub_class(channel)
-
-        try:
-            request = self._build_request(model, content, rules, temperature, top_p)
-            print(f"Sending request: {request}")  # 记录发送的请求
-            response = await stub.Predict(request, metadata=metadata)
-            print(f"Received response: {response}")  # 记录接收到的响应
-            result = self._process_response(response, model)
-            print(f"Processed result: {result}")  # 记录处理后的结果
-            return result
-        except grpc.RpcError as e:
-            print(f"RPC failed: {e}")
-            return {"error": str(e)}
-        finally:
-            await channel.close()
-
-    async def grpc_to_pieces_stream(self, model, content, rules, temperature, top_p):
-        metadata = [
-            ('user-agent', 'dart-grpc/2.0.0')
-        ]
-        channel = grpc.aio.secure_channel(
-            config.COMMON_GRPC,
-            grpc.ssl_channel_credentials()
-        )
-        stub = self.stub_class(channel)
-
-        try:
-            request = self._build_request(model, content, rules, temperature, top_p)
-            async for response in stub.PredictWithStream(request, metadata=metadata):
-                result = self._process_stream_response(response, model)
-                if result:
-                    yield f"data: {json.dumps(result)}\n\n"
-        except grpc.RpcError as e:
-            print(f"Stream RPC failed: {e}")
-            yield f"data: {json.dumps({'error': str(e)})}\n\n"
-        finally:
-            await channel.close()
-
-    def _process_response(self, response, model):
-        response_dict = MessageToDict(response)
-        print(f"Response dict: {response_dict}")  # 记录转换后的响应字典
-        message = response_dict.get('args', {}).get('args', {}).get('args', {}).get('message', '')
-        print(f"Extracted message: {message}")  # 记录提取的消息
-        return chat_completion_with_model(message, model)
-
-    def _process_stream_response(self, response, model):
-        response_dict = MessageToDict(response)
-        return chat_completion_stream_with_model(response_dict.get('args', {}).get('args', {}).get('args', {}).get('message', ''), model)
-
-# 工具函数
-def messages_process(messages):
-    rules = ''
-    message = ''
-
-    for msg in messages:
-        role = msg.role
-        content = msg.content
-        
-        if isinstance(content, list):
-            content = ''.join([item.get('text', '') for item in content if item.get('text')])
-        
-        if role == 'system':
-            rules += f"system:{content};\r\n"
-        elif role in ['user', 'assistant']:
-            message += f"{role}:{content};\r\n"
-
-    return rules, message
-
-def chat_completion_with_model(message, model):
-    # 处理消息并创建响应
-    response = {
-        "id": "chatcmpl-" + str(uuid.uuid4()),
-        "object": "chat.completion",
-        "created": int(time.time()),
-        "model": model,
-        "usage": {
-            "prompt_tokens": 0,
-            "completion_tokens": 0,
-            "total_tokens": 0
-        },
-        "choices": [
-            {
-                "message": {
-                    "role": "assistant",
-                    "content": message
-                },
-                "finish_reason": "stop",
-                "index": 0
-            }
-        ]
-    }
-    print(f"Final response: {response}")  # 记录最终的响应
-    return response
-
-def chat_completion_stream_with_model(text: str, model: str):
-    return {
-        "id": "chatcmpl-Nekohy",
-        "object": "chat.completion.chunk",
-        "created": 0,
-        "model": model,
-        "choices": [
-            {
-                "index": 0,
-                "delta": {
-                    "content": text,
-                },
-                "finish_reason": None,
-            },
-        ],
-    }
-
-# 初始化配置
-config = Config()
-
-# 初始化 FastAPI 应用
-app = FastAPI()
-
-# 定义请求模型
-class ChatMessage(BaseModel):
-    role: str
-    content: str
-
-class ChatCompletionRequest(BaseModel):
-    model: str
-    messages: List[ChatMessage]
-    stream: Optional[bool] = False
-    temperature: Optional[float] = None
-    top_p: Optional[float] = None
-
-# 路由定义
-@app.get("/")
-async def root():
-    return {"message": "API 服务运行中~"}
-
-@app.get("/ping")
-async def ping():
-    return {"message": "pong"}
-
-@app.get(config.API_PREFIX + "/v1/models")
-async def list_models():
-    with open('cloud_model.json', 'r') as f:
-        cloud_models = json.load(f)
-    
-    models = [
-        {"id": model["unique"], "object": "model", "owned_by": "pieces-os"}
-        for model in cloud_models["iterable"]
-    ]
-    
-    return JSONResponse({
-        "object": "list",
-        "data": models
-    })
-
-@app.post(config.API_PREFIX + "/v1/chat/completions")
-async def chat_completions(request: ChatCompletionRequest):
-    proto_file = config.GPT_PROTO if request.model.startswith('gpt') else config.COMMON_PROTO
-    grpc_handler = GRPCHandler(proto_file)
-    
-    if request.stream:
-        return StreamingResponse(
-            grpc_handler.grpc_to_pieces_stream(
-                request.model, content, rules, request.temperature, request.top_p
-            ),
-            media_type="text/event-stream"
-        )
-    else:
-        response = await grpc_handler.grpc_to_pieces(
-            request.model, content, rules, request.temperature, request.top_p
-        )
-        return JSONResponse(content=response)
-    result = await grpc_handler.grpc_to_pieces(request.model, content, rules, request.temperature, request.top_p)
-    print(f"Response content: {result}")  # 添加这行来记录响应内容
-    return JSONResponse(content=result)
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=config.PORT)

package-lock.json

@@ -0,0 +1,463 @@
+{
+  "name": "pieces-os",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "pieces-os",
+      "version": "1.0.0",
+      "dependencies": {
+        "@grpc/proto-loader": "^0.7.13",
+        "@huayue/grpc-js": "^1.12.1",
+        "@whatwg-node/server": "^0.9.50",
+        "dotenv": "^16.4.5",
+        "itty-router": "^5.0.18"
+      }
+    },
+    "node_modules/@grpc/proto-loader": {
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+      "license": "Apache-2.0",
+      "dependencies": {
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+        "long": "^5.0.0",
+        "protobufjs": "^7.2.5",
+        "yargs": "^17.7.2"
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+      "bin": {
+        "proto-loader-gen-types": "build/bin/proto-loader-gen-types.js"
+      },
+      "engines": {
+        "node": ">=6"
+      }
+    },
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+      "license": "MIT",
+      "dependencies": {
+        "@kamilkisiela/fast-url-parser": "^1.1.4",
+        "busboy": "^1.6.0",
+        "fast-querystring": "^1.1.1",
+        "tslib": "^2.6.3"
+      },
+      "engines": {
+        "node": ">=18.0.0"
+      }
+    },
+    "node_modules/@whatwg-node/server": {
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+      "resolved": "https://mirrors.huaweicloud.com/repository/npm/@whatwg-node/server/-/server-0.9.50.tgz",
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+      "dependencies": {
+        "@whatwg-node/fetch": "^0.9.22",
+        "tslib": "^2.6.3"
+      },
+      "engines": {
+        "node": ">=18.0.0"
+      }
+    },
+    "node_modules/ansi-regex": {
+      "version": "5.0.1",
+      "resolved": "https://mirrors.huaweicloud.com/repository/npm/ansi-regex/-/ansi-regex-5.0.1.tgz",
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+      "license": "MIT",
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/ansi-styles": {
+      "version": "4.3.0",
+      "resolved": "https://mirrors.huaweicloud.com/repository/npm/ansi-styles/-/ansi-styles-4.3.0.tgz",
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+      "license": "MIT",
+      "dependencies": {
+        "color-convert": "^2.0.1"
+      },
+      "engines": {
+        "node": ">=8"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
+      }
+    },
+    "node_modules/busboy": {
+      "version": "1.6.0",
+      "resolved": "https://mirrors.huaweicloud.com/repository/npm/busboy/-/busboy-1.6.0.tgz",
+      "integrity": "sha512-8SFQbg/0hQ9xy3UNTB0YEnsNBbWfhf7RtnzpL7TkBiTBRfrQ9Fxcnz7VJsleJpyp6rVLvXiuORqjlHi5q+PYuA==",
+      "dependencies": {
+        "streamsearch": "^1.1.0"
+      },
+      "engines": {
+        "node": ">=10.16.0"
+      }
+    },
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