import "dart:io";

import "package:logging/logging.dart";
import "package:path/path.dart";
import "package:path_provider/path_provider.dart";
import "package:photos/core/network/network.dart";

abstract class MLFramework {
  final _logger = Logger("MLFramework");

  /// Returns the path of the Image Model hosted remotely
  String getImageModelRemotePath();

  /// Returns the path of the Text Model hosted remotely
  String getTextModelRemotePath();

  /// Loads the Image Model stored at [path] into the framework
  Future<void> loadImageModel(String path);

  /// Loads the Text Model stored at [path] into the framework
  Future<void> loadTextModel(String path);

  /// Returns the Image Embedding for a file stored at [imagePath]
  Future<List<double>> getImageEmbedding(String imagePath);

  /// Returns the Text Embedding for [text]
  Future<List<double>> getTextEmbedding(String text);

  /// Downloads the models from remote, caches them and loads them into the
  /// framework. Override this method if you would like to control the
  /// initialization. For eg. if you wish to load the model from `/assets`
  /// instead of a CDN.
  Future<void> init() async {
    await _initImageModel();
    await _initTextModel();
  }

  /// Returns the cosine similarity between [imageEmbedding] and [textEmbedding]
  double computeScore(List<double> imageEmbedding, List<double> textEmbedding) {
    assert(
      imageEmbedding.length == textEmbedding.length,
      "The two embeddings should have the same length",
    );
    double score = 0;
    for (int index = 0; index < imageEmbedding.length; index++) {
      score += imageEmbedding[index] * textEmbedding[index];
    }
    return score;
  }

  // ---
  // Private methods
  // ---

  Future<void> _initImageModel() async {
    //final path = await _getLocalImageModelPath();
    const path = "assets/models/clip/clip-image-vit-32-float32.onnx";
    await loadImageModel(path);
    // if (File(path).existsSync()) {
    //   await loadImageModel(path);
    // } else {
    //   final tempFile = File(path + ".temp");
    //   await _downloadFile(getImageModelRemotePath(), tempFile.path);
    //   await tempFile.rename(path);
    //   await loadImageModel(path);
    // }
  }

  Future<void> _initTextModel() async {
    //final path = await _getLocalTextModelPath();
    const path = "assets/models/clip/clip-text-vit-32-float32.onnx";
    await loadTextModel(path);
    // if (File(path).existsSync()) {
    //   await loadTextModel(path);
    // } else {
    //   final tempFile = File(path + ".temp");
    //   await _downloadFile(getTextModelRemotePath(), tempFile.path);
    //   await tempFile.rename(path);
    //   await loadTextModel(path);
    // }
  }

  Future<String> _getLocalImageModelPath() async {
    return (await getTemporaryDirectory()).path +
        "/models/" +
        basename(getImageModelRemotePath());
  }

  Future<String> _getLocalTextModelPath() async {
    return (await getTemporaryDirectory()).path +
        "/models/" +
        basename(getTextModelRemotePath());
  }

  Future<void> _downloadFile(String url, String savePath) async {
    _logger.info("Downloading " + url);
    final existingFile = File(savePath);
    if (await existingFile.exists()) {
      await existingFile.delete();
    }
    await NetworkClient.instance.getDio().download(url, savePath);
  }
}