danceClassifier.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vhpvmx/danceClassifier/blob/main/danceClassifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bvPDFuJLGE6"
      },
      "source": [
        "from fastai.vision.all import *\n",
        "from fastai.vision.widgets import *"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBeAI_6vLF2C"
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        "path = Path()\n",
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UAXBDxdLFqi"
      },
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        "btn_upload = widgets.FileUpload()\n",
        "out_pl = widgets.Output()\n",
        "lbl_pred = widgets.Label()\n",
        "btn_run = widgets.Button(description='Classify')\n",
        "\n",
        "def on_click_classify(change):\n",
        "    img = PILImage.create(btn_upload.data[-1])\n",
        "    out_pl.clear_output()\n",
        "    with out_pl: display(img.to_thumb(128,128))\n",
        "    pred,pred_idx,probs = learn_inf.predict(img)\n",
        "    lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
        "\n",
        "btn_run.on_click(on_click_classify)"
      ],
      "execution_count": 17,
      "outputs": []
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        "#hide_output\n",
        "VBox([widgets.Label('Select your dancer!'), \n",
        "      btn_upload, btn_run, out_pl, lbl_pred])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
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}