基于腾讯云的苹果AI开发解决方案

计算服务:
  - 云服务器 CVM
  - GPU服务器
  - 云函数 SCF

AI服务:
  - 腾讯云TI平台
  - 腾讯云大模型

存储服务:
  - 对象存储 COS
  - 云数据库 MySQL
  - Redis 缓存

网络服务:
  - API网关
  - 内容分发网络 CDN
  - 私有网络 VPC

# services/model_trainer.py

import os
from tencentcloud.common import credential
from tencentcloud.tiia.v20190529 import tiia_client, models

class TencentCloudModelTrainer:
    def __init__(self, secret_id, secret_key, region="ap-guangzhou"):
        self.cred = credential.Credential(secret_id, secret_key)
        self.region = region

    def train_model(self, dataset_path, output_path):
        """训练端侧模型"""

        # 1. 上传训练数据到COS
        cos_client = self._create_cos_client()
        dataset_url = self._upload_to_cos(cos_client, dataset_path)

        # 2. 创建训练任务
        client = self._create_ti_client()

        req = models.CreateTrainingTaskRequest()
        req.DatasetConfig = {
            "DatasetType": "CosPath",
            "DatasetPath": dataset_url
        }
        req.ModelConfig = {
            "ModelType": "text_classification",
            "ModelArchitecture": "bert_base",
            "MaxSeqLength": 128
        }
        req.OutputConfig = {
            "OutputPath": output_path,
            "OutputFormat": "coreml"
        }

        resp = client.CreateTrainingTask(req)

        return {
            "task_id": resp.TaskId,
            "status": "submitted"
        }

    def optimize_model_for_apple(self, model_path, output_path):
        """优化模型用于苹果设备"""

        # 1. 加载训练好的模型
        import torch
        model = torch.load(model_path)

        # 2. 模型量化
        from coremltools.models import MLModel
        from coremltools.models.neural_network import NeuralNetworkBuilder
        from coremltools.models import datatypes

        # 转换为Core ML格式
        input_features = [("text", datatypes.Array(128))]
        output_features = [("label", datatypes.Array(10))]

        builder = NeuralNetworkBuilder(input_features, output_features)

        # 添加模型层
        # ... 省略具体实现

        # 保存Core ML模型
        mlmodel = MLModel(builder.spec)
        mlmodel.save(output_path)

        return {
            "status": "success",
            "model_path": output_path,
            "model_size": os.path.getsize(output_path)
        }

    def _create_ti_client(self):
        from tencentcloud.tiia.v20190529 import tiia_client as tiia
        return tiia.TiiaClient(self.cred, self.region)

    def _create_cos_client(self):
        from qcloud_cos import CosConfig
        from qcloud_cos import CosS3Client

        config = CosConfig(Region=self.region, SecretId=self.cred.secret_id,
                          SecretKey=self.cred.secret_key)
        return CosS3Client(config)

    def _upload_to_cos(self, cos_client, local_path):
        bucket_name = "your-bucket-name"
        cos_path = f"models/{os.path.basename(local_path)}"

        cos_client.upload_file(
            Bucket=bucket_name,
            LocalFilePath=local_path,
            Key=cos_path
        )

        return f"cos://{bucket_name}/{cos_path}"

# services/inference_service.py

from flask import Flask, request, jsonify
from tencentcloud.common import credential
from tencentcloud.aai.v20221229 import aai_client, models

app = Flask(__name__)

class TencentCloudInferenceService:
    def __init__(self, secret_id, secret_key):
        self.secret_id = secret_id
        self.secret_key = secret_key
        self.cred = credential.Credential(secret_id, secret_key)
        self.client = self._create_client()

    def _create_client(self):
        from tencentcloud.aai.v20221229 import aai_client as aai
        return aai.AaiClient(self.cred, "ap-guangzhou")

    def chat_completion(self, messages, model="glm-4"):
        """聊天补全"""

        req = models.ChatCompletionRequest()
        req.Messages = [{"Role": msg["role"], "Content": msg["content"]} for msg in messages]
        req.Model = model
        req.Stream = False

        resp = self.client.ChatCompletion(req)

        return {
            "content": resp.Choices[0].Message.Content,
            "usage": {
                "prompt_tokens": resp.Usage.PromptTokens,
                "completion_tokens": resp.Usage.CompletionTokens,
                "total_tokens": resp.Usage.TotalTokens
            }
        }

# Flask路由
inference_service = None

@app.before_first_request
def initialize_service():
    global inference_service
    secret_id = os.getenv("TENCENT_SECRET_ID")
    secret_key = os.getenv("TENCENT_SECRET_KEY")
    inference_service = TencentCloudInferenceService(secret_id, secret_key)

@app.route("/api/v1/chat", methods=["POST"])
def chat():
    data = request.json

    messages = data.get("messages", [])
    model = data.get("model", "glm-4")

    try:
        result = inference_service.chat_completion(messages, model)
        return jsonify({
            "success": True,
            "data": result
        })
    except Exception as e:
        return jsonify({
            "success": False,
            "error": str(e)
        }), 500

@app.route("/health", methods=["GET"])
def health():
    return jsonify({"status": "healthy"})

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8080)

# functions/inference_function.py

import json
import os
from tencentcloud.common import credential
from tencentcloud.aai.v20221229 import aai_client, models

def main_handler(event, context):
    """云函数入口"""

    # 解析输入参数
    body = json.loads(event['body'])
    messages = body.get('messages', [])
    model = body.get('model', 'glm-4')

    # 初始化客户端
    secret_id = os.getenv('TENCENT_SECRET_ID')
    secret_key = os.getenv('TENCENT_SECRET_KEY')

    cred = credential.Credential(secret_id, secret_key)
    client = aai_client.AaiClient(cred, "ap-guangzhou")

    # 调用AI接口
    req = models.ChatCompletionRequest()
    req.Messages = [{"Role": msg["role"], "Content": msg["content"]} for msg in messages]
    req.Model = model
    req.Stream = False

    resp = client.ChatCompletion(req)

    # 返回结果
    return {
        'statusCode': 200,
        'body': json.dumps({
            'content': resp.Choices[0].Message.Content,
            'usage': {
                'prompt_tokens': resp.Usage.PromptTokens,
                'completion_tokens': resp.Usage.CompletionTokens,
                'total_tokens': resp.Usage.TotalTokens
            }
        })
    }

# api-gateway-config.yaml

service:
  name: apple-ai-service
  description: Apple AI Integration Service
  protocol: https
  environment: release

apis:
  - name: chat-api
    description: Chat completion API
    method: POST
    path: /api/v1/chat
    timeout: 30

    # 关联云函数
    integration:
      type: scf
      function_name: inference_function
      function_namespace: default

    # 限流配置
    rate_limit:
      value: 100
      unit: second

    # 鉴权配置
    auth:
      type: secret_id
      secret_id: ${API_SECRET_ID}

    # CORS配置
    cors:
      allow_origins: "*"
      allow_methods: ["POST", "OPTIONS"]
      allow_headers: ["Content-Type", "Authorization"]

# utils/cost_calculator.py

class TencentCloudCostCalculator:
    """腾讯云成本计算器"""

    PRICING = {
        'cvm': 0.5,              # 元/小时
        'gpu': 8.0,              # 元/小时
        'scf': 0.0000167,        # 元/GUs
        'cos': 0.005,            # 元/GB/月
        'api_gateway': 0.005,    # 元/千次调用
        'redis': 0.05,           # 元/小时
        'mysql': 0.3,            # 元/小时
    }

    def calculate_monthly_cost(self, usage):
        """计算月度成本"""

        costs = {}

        # CVM/GPU成本
        cvm_hours = usage.get('cvm_hours', 0)
        gpu_hours = usage.get('gpu_hours', 0)
        costs['compute'] = (cvm_hours * self.PRICING['cvm'] +
                           gpu_hours * self.PRICING['gpu']) * 24 * 30

        # 云函数成本
        gpus = usage.get('scf_gus', 0)
        costs['scf'] = gpus * self.PRICING['scf']

        # 存储成本
        storage_gb = usage.get('storage_gb', 0)
        costs['storage'] = storage_gb * self.PRICING['cos']

        # API网关成本
        api_calls = usage.get('api_calls', 0)
        costs['api_gateway'] = (api_calls / 1000) * self.PRICING['api_gateway']

        # Redis成本
        redis_hours = usage.get('redis_hours', 0)
        costs['redis'] = redis_hours * self.PRICING['redis'] * 24 * 30

        # MySQL成本
        mysql_hours = usage.get('mysql_hours', 0)
        costs['mysql'] = mysql_hours * self.PRICING['mysql'] * 24 * 30

        costs['total'] = sum(costs.values())

        return costs

// Network/AppleAIService.swift

import Foundation

class AppleAIService {
    private let baseURL: String
    private let apiKey: String
    private let session: URLSession

    init(baseURL: String, apiKey: String) {
        self.baseURL = baseURL
        self.apiKey = apiKey

        let config = URLSessionConfiguration.default
        config.timeoutIntervalForRequest = 30
        self.session = URLSession(configuration: config)
    }

    func chat(messages: [[String: String]], model: String = "glm-4") async throws -> ChatResponse {
        let url = URL(string: "\(baseURL)/api/v1/chat")!

        var request = URLRequest(url: url)
        request.httpMethod = "POST"
        request.setValue("application/json", forHTTPHeaderField: "Content-Type")
        request.setValue(apiKey, forHTTPHeaderField: "X-API-Key")

        let body: [String: Any] = [
            "messages": messages,
            "model": model
        ]

        request.httpValue = try JSONSerialization.data(withJSONObject: body)

        let (data, response) = try await session.data(for: request)

        guard let httpResponse = response as? HTTPURLResponse,
              httpResponse.statusCode == 200 else {
            throw AIServiceError.requestFailed
        }

        let result = try JSONDecoder().decode(APIResponse<ChatResponse>.self, from: data)

        return result.data
    }
}

struct APIResponse<T: Codable>: Codable {
    let success: Bool
    let data: T
    let error: String?
}

struct ChatResponse: Codable {
    let content: String
    let usage: Usage
}

struct Usage: Codable {
    let prompt_tokens: Int
    let completion_tokens: Int
    let total_tokens: Int
}

enum AIServiceError: Error {
    case requestFailed
    case invalidResponse
    case networkError(Error)
}

// Services/HybridAIService.swift

import Foundation
import CoreML

class HybridAIService: ObservableObject {
    private let localModel: MLModel?
    private let cloudService: AppleAIService

    @Published var currentMode: AI_mode = .local

    init(cloudBaseURL: String, cloudAPIKey: String) {
        // 加载本地模型
        do {
            let config = MLModelConfiguration()
            config.computeUnits = .all
            self.localModel = try MyLocalAIModel(configuration: config)
        } catch {
            self.localModel = nil
        }

        // 初始化云端服务
        self.cloudService = AppleAIService(baseURL: cloudBaseURL, apiKey: cloudAPIKey)
    }

    func process(input: String) async throws -> String {
        // 1. 尝试本地处理
        if let localResult = try? await processLocally(input: input) {
            currentMode = .local
            return localResult
        }

        // 2. 本地失败，使用云端
        currentMode = .cloud
        return try await processRemotely(input: input)
    }

    private func processLocally(input: String) async throws -> String {
        guard let model = localModel else {
            throw AIServiceError.modelNotAvailable
        }

        let modelInput = MyLocalAIModelInput(text: input)
        let output = try model.prediction(from: modelInput)

        return output.featureValue(for: "output")?.stringValue ?? ""
    }

    private func processRemotely(input: String) async throws -> String {
        let messages = [
            ["role": "user", "content": input]
        ]

        let response = try await cloudService.chat(messages: messages)

        return response.content
    }
}

enum AI_mode {
    case local
    case cloud
}

# Dockerfile

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

COPY . .

EXPOSE 8080

CMD ["gunicorn", "--bind", "0.0.0.0:8080", "--workers", "4", "app:app"]

# docker-compose.yml

version: '3'

services:
  app:
    build: .
    ports:
      - "8080:8080"
    environment:
      - TENCENT_SECRET_ID=${TENCENT_SECRET_ID}
      - TENCENT_SECRET_KEY=${TENCENT_SECRET_KEY}
      - COS_BUCKET=${COS_BUCKET}
    depends_on:
      - redis
      - mysql

  redis:
    image: redis:alpine
    ports:
      - "6379:6379"

  mysql:
    image: mysql:8.0
    environment:
      - MYSQL_ROOT_PASSWORD=${MYSQL_PASSWORD}
    ports:
      - "3306:3306"