Flutter数据源分析插件data_source_analyzer的使用

在Flutter开发中，data_source_analyzer 是一个用于分析数据源的强大插件。它可以帮助开发者轻松地调用远程AI模型，并处理请求和响应。本文将通过一个完整的示例来展示如何使用 data_source_analyzer 插件。

使用步骤

1. 初始化插件

在应用启动时，确保初始化 data_source_analyzer 库。这一步是必不可少的，否则插件无法正常工作。

void main() async {
  WidgetsFlutterBinding.ensureInitialized(); // 确保Flutter绑定已初始化
  initDataSourceAnalyzerLib(); // 初始化data_source_analyzer库
  runApp(const MyApp());
}

2. 创建主界面

创建一个 MyApp 类作为应用的根组件，并设置其主题和主页。

class MyApp extends StatelessWidget {
  const MyApp({super.key});

  [@override](/user/override)
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Flutter Demo',
      theme: ThemeData(
        colorScheme: ColorScheme.fromSeed(seedColor: Colors.deepPurple),
        useMaterial3: true,
        appBarTheme: AppBarTheme(
          backgroundColor: Theme.of(context).colorScheme.primary,
          foregroundColor: Theme.of(context).colorScheme.onPrimary,
          surfaceTintColor: Colors.transparent,
          elevation: 0,
        ),
      ),
      home: const HomeScreen(), // 设置主页为HomeScreen
    );
  }
}

3. 创建主页

在 HomeScreen 中，我们使用 DataSourseAnalyzer 来调用不同的AI模型，并通过表单输入参数。

主页结构

class HomeScreen extends StatelessWidget {
  const HomeScreen({super.key});

  [@override](/user/override)
  Widget build(BuildContext context) {
    final dataSourseAnalyzer = DataSourseAnalyzer.defaultInstance(); // 初始化插件实例
    return Scaffold(
      appBar: AppBar(
        title: const Text('Home'),
        centerTitle: true,
      ),
      body: SingleChildScrollView(
        child: SizedBox(
          width: double.infinity,
          child: Column(
            children: [
              // 添加多个AI请求块
              AiRequestBlock(
                title: "Text classification",
                dataSourseAnalyzer: dataSourseAnalyzer,
                type: "text-classification",
                model: "Xenova/distilbert-base-uncased-finetuned-sst-2-english",
                additionalPipelineParams: "{progress_callback: (p) => console.log(JSON.stringify(p)),}",
                request: "Top SELLING! 300% profit !!! only 50\$ for consultation",
                additionalModelParams: "{topk:null,}",
                remoteHost: 'https://bf73-188-190-37-83.ngrok-free.app',
                remotePathTemplate: '/models/{model}',
              ),
              // 其他AI请求块...
            ],
          ),
        ),
      ),
    );
  }
}

4. 创建AI请求块

AiRequestBlock 是一个自定义状态组件，用于封装每个AI请求的表单和逻辑。

请求块结构

class AiRequestBlock extends StatefulWidget {
  const AiRequestBlock({
    super.key,
    required this.title,
    required this.dataSourseAnalyzer,
    required this.type,
    required this.model,
    required this.additionalPipelineParams,
    required this.request,
    required this.additionalModelParams,
    required this.remoteHost,
    required this.remotePathTemplate,
  });

  final String title;
  final DataSourseAnalyzer dataSourseAnalyzer;
  final String type;
  final String model;
  final String additionalPipelineParams;
  final String request;
  final String additionalModelParams;
  final String remoteHost;
  final String remotePathTemplate;

  [@override](/user/override)
  State<AiRequestBlock> createState() => _AiRequestBlockState();
}

请求块逻辑

class _AiRequestBlockState extends State<AiRequestBlock> {
  DataSourseAnalyzer get dataSourseAnalyzer => widget.dataSourseAnalyzer;

  final _formKey = GlobalKey<FormState>();
  final Map<String, dynamic> requestParams = {};
  bool loading = false;

  [@override](/user/override)
  Widget build(BuildContext context) {
    return SizedBox(
      width: double.infinity,
      child: Form(
        key: _formKey,
        child: Column(
          crossAxisAlignment: CrossAxisAlignment.center,
          children: [
            // 标题和表单字段
            Text(widget.title, style: const TextStyle(fontSize: 20)),
            TextFormField(...), // 请求类型
            TextFormField(...), // 模型
            TextFormField(...), // 额外管道参数
            TextFormField(...), // 请求内容
            TextFormField(...), // 额外模型参数
            TextFormField(...), // 远程主机
            TextFormField(...), // 远程路径模板
            ElevatedButton(
              onPressed: () async {
                // 提交表单并生成AI请求
                setState(() { loading = true; });
                if (_formKey.currentState!.validate()) {
                  _formKey.currentState!.save();
                  try {
                    final output = await dataSourseAnalyzer.callAiModel(
                      model: requestParams["model"],
                      type: requestParams["type"],
                      additionalPipelineParams: requestParams["additionalPipelineParams"],
                      request: requestParams["request"],
                      additionalModelParams: requestParams["additionalModelParams"],
                      remoteHostSettings: RemoteHostSettings(
                        remoteHost: requestParams["remoteHost"],
                        remotePathTemplate: requestParams["remotePathTemplate"],
                      ),
                    );
                    showDialog(
                      context: context,
                      builder: (context) => AlertDialog(
                        title: const Text("Output", style: TextStyle(fontSize: 20)),
                        content: SelectableText(output),
                      ),
                    );
                  } catch (err) {
                    showDialog(
                      context: context,
                      builder: (context) => AlertDialog(
                        title: const Text("Error"),
                        content: Text(err.toString()),
                      ),
                    );
                  }
                }
                setState(() { loading = false; });
              },
              child: loading ? const CircularProgressIndicator() : const Text("Generate AI Request"),
            ),
          ],
        ),
      ),
    );
  }
}