createOrtValue method

Implementation

@override
Future<Map<String, dynamic>> createOrtValue(String sourceType, dynamic data, List<int> shape) async {
  try {
    // Convert shape to JavaScript array
    final jsShape = jsArrayFrom(shape);

    // Map the source type to onnxruntime-web data type
    final dataType = _mapSourceTypeToOrtType(sourceType);

    // Handle different data types
    JSObject tensor;

    switch (sourceType) {
      case 'float32':
        // Convert data to Float32Array
        final jsData = _convertToTypedArray(data, 'Float32Array');
        tensor = tensorConstructor.callAsConstructorVarArgs([dataType.toJS, jsData, jsShape]);
        break;

      case 'int32':
        // Convert data to Int32Array
        final jsData = _convertToTypedArray(data, 'Int32Array');
        tensor = tensorConstructor.callAsConstructorVarArgs([dataType.toJS, jsData, jsShape]);
        break;

      case 'int64':
        // Note: JavaScript doesn't have Int64Array, so using BigInt64Array
        // This might require special handling depending on browser support
        final jsData = _convertToTypedArray(data, 'BigInt64Array');
        tensor = tensorConstructor.callAsConstructorVarArgs([dataType.toJS, jsData, jsShape]);
        break;

      case 'uint8':
        // Convert data to Uint8Array
        final jsData = _convertToTypedArray(data, 'Uint8Array');
        tensor = tensorConstructor.callAsConstructorVarArgs([dataType.toJS, jsData, jsShape]);
        break;

      case 'bool':
        // For boolean tensors, ONNX Runtime uses a Uint8Array with 0/1 values
        // Make sure we properly handle all possible incoming bool representations
        final boolArray =
            (data as List).map((value) {
              if (value is bool) {
                return value ? 1 : 0;
              } else if (value is num) {
                return value != 0 ? 1 : 0;
              } else {
                return value == true ? 1 : 0;
              }
            }).toList();
        final jsData = _convertToTypedArray(boolArray, 'Uint8Array');
        tensor = tensorConstructor.callAsConstructorVarArgs([dataType.toJS, jsData, jsShape]);
        break;

      case 'string':
        // For string tensors, we use the standard JavaScript Array
        // ONNX Runtime JS API accepts string arrays for string tensors
        final stringArray = jsArrayFrom((data as List<String>).toList());
        tensor = tensorConstructor.callAsConstructorVarArgs([dataType.toJS, stringArray, jsShape]);
        break;

      default:
        throw PlatformException(
          code: "UNSUPPORTED_TYPE",
          message: "Unsupported data type: $sourceType",
          details: null,
        );
    }

    // Generate a unique ID for this tensor
    final valueId = '${DateTime.now().millisecondsSinceEpoch}_${math.Random().nextInt(10000)}';

    // Store the tensor
    _ortValues[valueId] = tensor;

    // Return the tensor information
    return {'valueId': valueId, 'dataType': sourceType, 'shape': shape};
  } catch (e) {
    if (e is PlatformException) {
      rethrow;
    }
    throw PlatformException(code: "TENSOR_CREATION_ERROR", message: "Failed to create OrtValue: $e", details: null);
  }
}