fwf_app.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/nicoelbert/funwithflags/blob/main/fwf_app.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekPcbq7iZzhJ"
      },
      "source": [
        "#Welcome to Fun with Flags\r\n",
        "Sometimes its hard to know which flag you are looking at. Here you can upload an image and I'll tell you which nation this flag belongs to.\r\n",
        "\r\n",
        "The current version only predicts flags for 240 Nations - the more space the flag takes on your image, the better the prediction gets. Have fun!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jGxxAn5lqbv"
      },
      "source": [
        "!pip install -Uqq fastai  # upgrade fastai on colab\r\n",
        "from fastai.vision.all import *\r\n",
        "from fastai.vision.widgets import *\r\n",
        "import os, shutil\r\n",
        "\r\n",
        "from PIL import Image\r\n",
        "import requests\r\n",
        "from io import BytesIO\r\n",
        "from fastai.vision.widgets import *"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nr0wmgbqnWFV"
      },
      "source": [
        "#load Masterdata from csv\r\n",
        "url = 'https://raw.githubusercontent.com/nicoelbert/funwithflags/main/countries.CSV'\r\n",
        "df_countries = pd.read_csv(url)\r\n",
        "\r\n",
        "#load Model\r\n",
        "download_url('https://github.com/nicoelbert/funwithflags/raw/main/Model/fwf.pkl', 'fwf.pkl')\r\n",
        "learn_inf = load_learner('fwf.pkl', cpu=True)\r\n",
        "\r\n",
        "\r\n",
        "dict_countrynames = df_countries.set_index('Code').to_dict()['Country']\r\n",
        "list_countrycodes= df_countries['Code'].tolist()\r\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDTDbSWvtWvX"
      },
      "source": [
        "def getPreText(confidence):\r\n",
        "  #return the right preffix text based on the confidence\r\n",
        "  pretext = \"\"\r\n",
        "  if confidence >= 0.9:\r\n",
        "    pretext = \"This is the flag of \"\r\n",
        "  elif confidence >= 0.8:\r\n",
        "    pretext = \"I am pretty sure, that this is the flag of \"\r\n",
        "  elif confidence >= 0.7:\r\n",
        "    pretext = \"This should be the flag of \"\r\n",
        "  elif confidence >= 0.6:\r\n",
        "    pretext = \"This might be the flag of \"\r\n",
        "  elif confidence < 0.4:\r\n",
        "    pretext = \"I have to guess - eventually the flag of \"\r\n",
        "  return pretext\r\n",
        "\r\n",
        "#######################################################################################\r\n",
        "\r\n",
        "def getConfidencePercentage(confidence):\r\n",
        "  #return right suffix\r\n",
        "  c_percentage = round(confidence * 100,2)\r\n",
        "  return c_percentage\r\n",
        "\r\n",
        "#######################################################################################\r\n",
        "\r\n",
        "def on_click_classify(change):\r\n",
        "  #get image\r\n",
        "  img = PILImage.create(btn_upload.data[-1])\r\n",
        "  out_pl.clear_output()\r\n",
        "  with out_pl:display(img.to_thumb(256,256))\r\n",
        "\r\n",
        "  #make prediction and retrieve information\r\n",
        "  country_id = learn_inf.predict(img)[0]\r\n",
        "  country_tensor_index = learn_inf.predict(img)[1].item()\r\n",
        "  confidence = learn_inf.predict(img)[2].numpy()[country_tensor_index]\r\n",
        "\r\n",
        "  example_pl.clear_output()\r\n",
        "  with example_pl:display(get_example_img(country_id).to_thumb(256,256),)\r\n",
        "\r\n",
        "  #read full country name \r\n",
        "  country_name = dict_countrynames[country_id]\r\n",
        "\r\n",
        "  #get fitting strings\r\n",
        "  if confidence > 0:\r\n",
        "    pretext= getPreText(confidence)\r\n",
        "  else:\r\n",
        "    pretext= \"No prediction possible\"\r\n",
        "  \r\n",
        "\r\n",
        "  #return the Name of the predicted country\r\n",
        "  lbl_pred.value = pretext + country_name\r\n",
        "  \r\n",
        "  #update confidence bar\r\n",
        "  bar_confidence.value = getConfidencePercentage(confidence)\r\n",
        "  bar_confidence_score.value = \"{} %\".format(getConfidencePercentage(confidence))\r\n",
        "\r\n",
        "  #change to the right accordion tab\r\n",
        "  accordion.selected_index = 1\r\n",
        "\r\n",
        "#######################################################################################\r\n",
        "def on_click_show(change):\r\n",
        "  #get image\r\n",
        "  lbl_upload2.value = \"You uploaded the following image: \"\r\n",
        "  img = PILImage.create(btn_upload.data[-1])\r\n",
        "  out_pl.clear_output()\r\n",
        "  with out_pl:display(img.to_thumb(256,256))\r\n",
        "#######################################################################################\r\n",
        "\r\n",
        "def search_images_ddg(key,max_n=1):\r\n",
        "    \"\"\"Search for 'key' with DuckDuckGo and return a unique urls of 'max_n' images\r\n",
        "    (Adopted from https://github.com/deepanprabhu/duckduckgo-images-api and \r\n",
        "    https://github.com/fastai/fastbook/blob/master/utils.py)\r\n",
        "    \"\"\"\r\n",
        "    url        = 'https://duckduckgo.com/'\r\n",
        "    params     = {'q':key}\r\n",
        "    res        = requests.post(url,data=params)\r\n",
        "    searchObj  = re.search(r'vqd=([\\d-]+)\\&',res.text)\r\n",
        "    if not searchObj: print('Token Parsing Failed !'); return\r\n",
        "    requestUrl = url + 'i.js'\r\n",
        "    headers    = {'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:71.0) Gecko/20100101 Firefox/71.0',\r\n",
        "                 'referer': 'https://duckduckgo.com/'}\r\n",
        "    params     = (('l','us-en'),('o','json'),('q',key),('vqd',searchObj.group(1)),('f',',,,'),('p','1'),('v7exp','a'))\r\n",
        "    urls       = []\r\n",
        "    while True:\r\n",
        "        try:\r\n",
        "            res  = requests.get(requestUrl,headers=headers,params=params)\r\n",
        "            data = json.loads(res.text)\r\n",
        "            for obj in data['results']:\r\n",
        "                urls.append(obj['image'])\r\n",
        "                max_n = max_n - 1\r\n",
        "                if max_n < 1: return L(set(urls))     # dedupe\r\n",
        "            if 'next' not in data: return L(set(urls))\r\n",
        "            requestUrl = url + data['next']\r\n",
        "        except:\r\n",
        "            pass\r\n",
        "\r\n",
        "#######################################################################################\r\n",
        "\r\n",
        "def get_example_img(country_id):\r\n",
        "  country_name = dict_countrynames[country_id]\r\n",
        "  results = search_images_ddg(f'flag {country_name} wikipedia')\r\n",
        "  response = requests.get(results[0])\r\n",
        "  img = Image.open(BytesIO(response.content))\r\n",
        "  lbl_example_source.value = 'Example Image Source: {}'.format(results[0])\r\n",
        "  return img"
      ],
      "execution_count": 8,
      "outputs": []
    },
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        },
        "id": "tgW7mX7NmmwO",
        "outputId": "2a875941-1408-430d-97d7-8f98729e8265"
      },
      "source": [
        "\r\n",
        "#create Labels\r\n",
        "lbl_upload1 = widgets.Label('Upload the flag you want to learn about')\r\n",
        "lbl_upload2 = widgets.Label()\r\n",
        "lbl_pred = widgets.Label()\r\n",
        "lbl_empty = widgets.Label(\" \")\r\n",
        "lbl_result1 = VBox([widgets.Label('The image you uploaded: ')])\r\n",
        "lbl_result2 = VBox([lbl_empty, widgets.Label('The flag should look like this: ')])\r\n",
        "\r\n",
        "#create upload button\r\n",
        "btn_upload = widgets.FileUpload()\r\n",
        "btn_upload\r\n",
        "\r\n",
        "#create Button for Classify\r\n",
        "btn_run = widgets.Button(description='Classify')\r\n",
        "btn_run.on_click(on_click_classify)\r\n",
        "\r\n",
        "btn_show = widgets.Button(description='Show Upload')\r\n",
        "btn_show.on_click(on_click_show)\r\n",
        "\r\n",
        "#output upload\r\n",
        "out_pl = widgets.Output()\r\n",
        "out_pl.clear_output()\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "#output example image\r\n",
        "example_pl = widgets.Output()\r\n",
        "example_pl.clear_output()\r\n",
        "lbl_example_source = widgets.Label()\r\n",
        "\r\n",
        "\r\n",
        "#confidence bar\r\n",
        "bar_confidence = widgets.IntProgress(\r\n",
        "    value=0,\r\n",
        "    min=0,\r\n",
        "    max=100,\r\n",
        "    step=1,\r\n",
        "    description='Confidence: ',\r\n",
        "    bar_style='success', # 'success', 'info', 'warning', 'danger' or ''\r\n",
        "    orientation='horizontal'\r\n",
        ")\r\n",
        "\r\n",
        "bar_confidence_score =  widgets.Label()\r\n",
        "\r\n",
        "bar_confidence_full = HBox([bar_confidence,bar_confidence_score])\r\n",
        "\r\n",
        "#collect widgets\r\n",
        "upload_widgets = VBox([lbl_upload1,HBox([btn_upload,btn_show,btn_run]),lbl_upload2,out_pl])\r\n",
        "result_widgets = VBox([lbl_result1,out_pl,lbl_empty, lbl_pred,bar_confidence_full,lbl_result2, example_pl, lbl_example_source])\r\n",
        "\r\n",
        "#create accordion\r\n",
        "accordion = widgets.Accordion(children=[upload_widgets, result_widgets])\r\n",
        "accordion.set_title(0, 'Upload')\r\n",
        "accordion.set_title(1, 'Result')\r\n",
        "accordion\r\n",
        "\r\n",
        "\r\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
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          "data": {
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              "model_id": "78b2706c3ac743c7900d41db16007c8f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Accordion(children=(VBox(children=(Label(value='Upload the flag you want to learn about'), HBox(children=(File…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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        {
          "output_type": "display_data",
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            "text/html": [
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              "<IPython.core.display.HTML object>"
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}