Google vertex AI api problem

[RHIJZY]

I am trying to make an app that uses Google Vertex AI API to import images onto a dataset using a service account. I got images importing onto the dataset perfectly working, but no matter what I do i cannot label them. anyone can help me with that?

import 'dart:convert';
import 'dart:io';
import 'package:flutter/material.dart';
import 'package:flutter/services.dart' show rootBundle;
import 'package:image_picker/image_picker.dart';
import 'package:http/http.dart' as http;
import 'package:googleapis_auth/auth_io.dart';
import 'package:mime/mime.dart';

const String projectId = "Project_ID"; // Your Google Cloud Project ID
const String datasetId = "Dataset_ID"; // Your Vertex AI Dataset ID
const String storageBucket = "Bucket_Name"; // Your GCS Bucket
const String region = "us-central1"; // Your Vertex AI Region
const String serviceAccountPath = "assets/computeservice.json"; // Path to service account JSON

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      home: ImageUploader(),
    );
  }
}

class ImageUploader extends StatefulWidget {
  @override
  _ImageUploaderState createState() => _ImageUploaderState();
}

class _ImageUploaderState extends State<ImageUploader> {
  File? _image;
  String _status = "Select an image to upload";
  bool _isUploading = false;

  Future<void> _pickImage() async {
    final pickedFile = await ImagePicker().pickImage(source: ImageSource.gallery);
    if (pickedFile != null) {
      setState(() {
        _image = File(pickedFile.path);
      });
    }
  }

  Future<AuthClient> _authenticate() async {
    final String jsonString = await rootBundle.loadString(serviceAccountPath);
    final credentials = ServiceAccountCredentials.fromJson(jsonString);
    print("🔑 Authenticated as: ${credentials.email}");
    return await clientViaServiceAccount(credentials, ["https://www.googleapis.com/auth/cloud-platform"]);
  }

  Future<String?> _uploadToGCS(File image, AuthClient client) async {
    final fileName = "uploads/${DateTime.now().millisecondsSinceEpoch}_${image.uri.pathSegments.last}";
    final storageUrl = "https://storage.googleapis.com/upload/storage/v1/b/$storageBucket/o?uploadType=media&name=$fileName";
    final mimeType = lookupMimeType(image.path) ?? 'application/octet-stream';

    final response = await client.post(
      Uri.parse(storageUrl),
      headers: {
        "Content-Type": mimeType,
        "Authorization": "Bearer ${(client as AutoRefreshingAuthClient).credentials.accessToken.data}",
      },
      body: image.readAsBytesSync(),
    );

    if (response.statusCode == 200) {
      print("✅ Image uploaded successfully: $fileName");
      return fileName;
    } else {
      print("🚨 Error uploading image: ${response.body}");
      return null;
    }
  }

  Future<void> _importToVertexAI(String gcsFileName, String accessToken) async {
    const String importSchemaUri =
        "gs://google-cloud-aiplatform/schema/dataset/ioformat/image_classification_single_label_io_format_1.0.0.yaml";

    final String gcsFilePath = "gs://$storageBucket/$gcsFileName";

    final String datasetImportUrl =
        "https://$region-aiplatform.googleapis.com/v1/projects/$projectId-p0/locations/$region/datasets/$datasetId:import";

    final requestBody = jsonEncode({
      "importConfigs": [
        {
          "gcsSource": {
            "uris": [gcsFilePath]
          },
          "importSchemaUri": importSchemaUri,
          "annotationLabels": {
            "label": "your_label_name"
          }
        }
      ]
    });

    print("📤 DEBUG: Import Request Body:\n$requestBody");

    try {
      final response = await http.post(
        Uri.parse(datasetImportUrl),
        headers: {
          "Content-Type": "application/json",
          "Authorization": "Bearer $accessToken",
        },
        body: requestBody,
      );

      print("🚨 DEBUG: Full Response Body:\n${response.body}");

      setState(() {
        if (response.statusCode == 200) {
          _status = "✅ Image successfully imported to Vertex AI!";
        } else {
          _status = "❌ Failed to import: ${response.body}";
        }
      });

    } catch (e) {
      setState(() {
        _status = "🚨 Error during import: ${e.toString()}";
      });
    }
  }



  Future<void> _uploadAndTrain() async {
    if (_image == null) {
      setState(() {
        _status = "⚠ Please select an image first.";
      });
      return;
    }

    setState(() {
      _isUploading = true;
      _status = "🔑 Authenticating...";
    });

    try {
      final client = await _authenticate();
      setState(() {
        _status = "📤 Uploading image to Google Cloud Storage...";
      });

      final gcsFileName = await _uploadToGCS(_image!, client);
      if (gcsFileName == null) throw Exception("Upload failed.");

      setState(() {
        _status = "📥 Importing image to Vertex AI dataset...";
      });

      final accessToken = (client as AutoRefreshingAuthClient).credentials.accessToken.data;
      await _importToVertexAI(gcsFileName, accessToken);
    } catch (e) {
      setState(() {
        _status = "🚨 Error: ${e.toString()}";
      });
    } finally {
      setState(() {
        _isUploading = false;
      });
    }
  }

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(title: Text("Vertex AI Image Uploader")),
      body: Center(
        child: Column(
          mainAxisAlignment: MainAxisAlignment.center,
          children: [
            _image != null ? Image.file(_image!, height: 200) : Icon(Icons.image, size: 100),
            SizedBox(height: 20),
            ElevatedButton(onPressed: _pickImage, child: Text("Pick Image")),
            SizedBox(height: 10),
            ElevatedButton(
              onPressed: _isUploading ? null : _uploadAndTrain,
              child: _isUploading ? CircularProgressIndicator() : Text("Upload to Dataset"),
            ),
            SizedBox(height: 20),
            Text(_status),
          ],
        ),
      ),
    );
  }
}