runInference method

Implementation

@override
Future<Map<String, dynamic>> runInference(
  String sessionId,
  Map<String, OrtValue> inputs, {
  Map<String, dynamic>? runOptions,
}) async {
  try {
    // Check if the session exists
    if (!_sessions.containsKey(sessionId)) {
      throw PlatformException(code: "INVALID_SESSION", message: "Session not found", details: null);
    }

    final session = _sessions[sessionId]!;

    // Create a JavaScript object for inputs
    final jsInputs = createJSObject();

    // Process inputs - expecting OrtValue references
    for (final entry in inputs.entries) {
      final name = entry.key;
      final ortValue = entry.value;

      // Get the tensor by ID
      final valueId = ortValue.id;
      final tensor = _ortValues[valueId];
      if (tensor == null) {
        throw PlatformException(code: "INVALID_VALUE", message: "OrtValue with ID $valueId not found", details: null);
      }

      // Add to inputs object
      jsInputs.setProperty(name.toJS, tensor);
    }

    // Create run options if provided
    JSObject? jsRunOptions;
    if (runOptions != null && runOptions.isNotEmpty) {
      jsRunOptions = createJSObject();

      // Set run options if needed
      if (runOptions.containsKey('logSeverityLevel')) {
        jsRunOptions.setProperty('logSeverityLevel'.toJS, runOptions['logSeverityLevel']);
      }

      if (runOptions.containsKey('logVerbosityLevel')) {
        jsRunOptions.setProperty('logVerbosityLevel'.toJS, runOptions['logVerbosityLevel']);
      }

      if (runOptions.containsKey('terminate')) {
        jsRunOptions.setProperty('terminate'.toJS, runOptions['terminate']);
      }
    }

    // Run inference
    final runPromise =
        jsRunOptions != null
            ? callMethod(session, 'run', [jsInputs, jsRunOptions])
            : callMethod(session, 'run', [jsInputs]);

    // Wait for the promise to resolve
    final jsOutputs = await promiseToFuture<JSObject>(runPromise);

    // Process outputs - create OrtValue objects for each output
    final outputMap = <String, dynamic>{};
    final outputNames = getOutputNames(session);

    for (final name in outputNames) {
      if (jsOutputs.has(name)) {
        final tensor = jsOutputs.getProperty(name.toJS) as JSObject;

        // Create a new OrtValue and store it
        final valueId = '${DateTime.now().millisecondsSinceEpoch}_${math.Random().nextInt(10000)}';

        _ortValues[valueId] = tensor;

        // Get tensor type
        final type = tensor.getProperty('type'.toJS).toString();

        // Get tensor shape
        final jsShape = tensor.getProperty('dims'.toJS) as JSObject;
        final shapeLength = (jsShape.getProperty('length'.toJS) as JSNumber).toDartInt;
        final shape = <int>[];

        for (var i = 0; i < shapeLength; i++) {
          final val = jsShape.getProperty(i.toString().toJS) as JSNumber;
          shape.add(val.toDartInt);
        }

        // Format parameters like native platforms do
        outputMap[name] = [valueId, _mapOrtTypeToSourceType(type), shape];
      }
    }

    return outputMap;
  } catch (e) {
    if (e is PlatformException) {
      rethrow;
    }
    throw PlatformException(code: "INFERENCE_ERROR", message: "Failed to run inference: $e", details: null);
  }
}