Implemented changes to use abstract feedback
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@@ -141,6 +141,8 @@ namespace Mediapipe.Unity.Tutorial
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/// </summary>
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private Tensor inputTensor;
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public List<Listener> listeners = new List<Listener>();
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/// <summary>
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/// Google Mediapipe setup & run
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/// </summary>
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@@ -207,7 +209,6 @@ namespace Mediapipe.Unity.Tutorial
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keypointManager = new KeypointManager(modelInfoFile);
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// check if model exists at path
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//var model = ModelLoader.Load(Resources.Load<NNModel>("Models/Fingerspelling/model_A-L"));
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worker = modelList.GetCurrentModel().CreateWorker();
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@@ -215,15 +216,13 @@ namespace Mediapipe.Unity.Tutorial
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StartCoroutine(SignRecognitionCoroutine());
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StartCoroutine(MediapipeCoroutine());
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}
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public void ChangeModel(ModelIndex index)
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/// <summary>
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/// Called at the start of course/Minigame, will set the model before the start of SIgnPredictor is called.
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/// </summary>
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/// <param name="index">The index of the model to be used</param>
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public void SetModel(ModelIndex index)
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{
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this.modelList.SetCurrentModel(index);
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// If a worker already existed, we throw it out
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worker?.Dispose();
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// Add a new worker for the new model
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worker = modelList.GetCurrentModel().CreateWorker();
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}
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/// <summary>
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@@ -325,6 +324,10 @@ namespace Mediapipe.Unity.Tutorial
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learnableProbabilities.Add(((char)(i + 65)).ToString(), softmaxedOutput2[i]);
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}
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//Debug.Log($"prob = [{learnableProbabilities.Aggregate(" ", (t, kv) => $"{t}{kv.Key}:{kv.Value} ")}]");
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foreach(Listener listener in listeners)
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{
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listener.ProcessIncomingCall();
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}
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}
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else
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{
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