voila.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/baichlseder/voila/blob/main/voila.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cfu0NuaAzdb7"
      },
      "source": [
        "!pip install fastai --upgrade -q"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBGXhIHazjTm"
      },
      "source": [
        "from fastai.vision.all import *\n",
        "import ipywidgets as widgets\n",
        "import requests"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEJA8_r3jY3X"
      },
      "source": [
        "# https://drive.google.com/file/d/1-OujMPsJ5fPGSaCkoHQiJKiemcMyzOyK/view?usp=sharing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNoJZA9Dzphi"
      },
      "source": [
        "def download_file_from_google_drive(id, destination):\n",
        "    URL = \"https://docs.google.com/uc?export=download\"\n",
        "    session = requests.Session()\n",
        "    response = session.get(URL, params = { 'id' : id }, stream = True)\n",
        "    token = get_confirm_token(response)\n",
        "    if token:\n",
        "      params = { 'id' : id, 'confirm' : token }\n",
        "      response = session.get(URL, params = params, stream = True)\n",
        "    save_response_content(response, destination)\n",
        "    \n",
        "def get_confirm_token(response):\n",
        "    for key, value in response.cookies.items():\n",
        "        if key.startswith('download_warning'):\n",
        "            return value\n",
        "\n",
        "    return None\n",
        "\n",
        "def save_response_content(response, destination):\n",
        "    CHUNK_SIZE = 32768\n",
        "\n",
        "    with open(destination, \"wb\") as f:\n",
        "        for chunk in response.iter_content(CHUNK_SIZE):\n",
        "            if chunk: # filter out keep-alive new chunks\n",
        "                f.write(chunk)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkyPY-2I01Ut"
      },
      "source": [
        "file_id = '1-OujMPsJ5fPGSaCkoHQiJKiemcMyzOyK'\n",
        "destination = './handwritting_classifier.pkl'\n",
        "download_file_from_google_drive(file_id, destination)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RRzC7Ry4XmA"
      },
      "source": [
        "## Classifier zu Nummern\n",
        "Es werden alle Zahlen von 0 bis 9 erkannt:\n",
        "  * 0\n",
        "  * 1\n",
        "  * 2\n",
        "  * 3\n",
        "  * 4\n",
        "  * 5\n",
        "  * 6\n",
        "  * 7\n",
        "  * 8\n",
        "  * 9"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lU9qu_Cb183l"
      },
      "source": [
        "path = Path()\n",
        "inference = load_learner(path/'handwritting_classifier.pkl', cpu=True)"
      ],
      "execution_count": 29,
      "outputs": []
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          "referenced_widgets": [
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        "id": "1pzaW_l32fpK",
        "outputId": "a876d6d2-b9f9-4c1a-bbe0-9221d98ead22"
      },
      "source": [
        "btn_upload = widgets.FileUpload()\n",
        "lbl_pred = widgets.Label()\n",
        "btn_run = widgets.Button(description='Classify')\n",
        "out_pl = widgets.Output()\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 = inference.predict(img)\n",
        "  lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
        "\n",
        "btn_run.on_click(on_click_classify)\n",
        "widgets.VBox([widgets.Label('Select your number!'),\n",
        "              btn_upload, btn_run, out_pl, lbl_pred])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
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}