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- {
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- "cells": [
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- {
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- "cell_type": "markdown",
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- "source": [
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- "https://www.kdnuggets.com/deploying-your-first-machine-learning-model"
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- ],
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- "metadata": {
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- "id": "MP7O1gtliL6n"
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- }
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "try:\n",
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- " import opendatasets as od\n",
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- " import pandas as pd\n",
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- "except:\n",
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- " !pip install opendatasets\n",
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- " import opendatasets as od\n",
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- "from os import path\n",
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- "\n",
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- "url = \"https://www.kaggle.com/datasets/uciml/glass\" ### kaggle dataset url here\n",
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- "data_dir = \"/content/\" ### directory where you want to save data\n",
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- "\n",
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- "# Go to the account tab and under API section, click Create New API Token.\n",
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- "\n",
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- "# A JSON file will be downloaded, open it locally or you can also use any online JSON viewer and upload it there.\n",
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- "\n",
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- "# On opening this file, you will find the username and key in it. Copy the username and password and paste it into the prompted Notebook cell.\n",
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- "# The content of the downloaded file would look like this.\n",
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- "\n",
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- "# {\"username\":<KAGGLE USERNAME>,\"key\":<KAGGLE KEY>}\n",
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- "\n",
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- "\n",
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- "def download_data(url, data_dir):\n",
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- " od.download(url, data_dir)"
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- ],
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- "metadata": {
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- "id": "5ewudtMkfnPL",
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- "outputId": "6abe70ec-7a22-4872-b0e6-623d9e18e1fe",
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- }
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- },
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- "execution_count": 1,
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- "outputs": [
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- {
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- "output_type": "stream",
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- "name": "stdout",
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- "text": [
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- "Collecting opendatasets\n",
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- " Downloading opendatasets-0.1.22-py3-none-any.whl (15 kB)\n",
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- "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from opendatasets) (4.66.1)\n",
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- "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (from opendatasets) (1.5.16)\n",
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- "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from opendatasets) (8.1.7)\n",
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- "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (1.16.0)\n",
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- "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2023.11.17)\n",
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- "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.8.2)\n",
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- "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.31.0)\n",
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- "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (8.0.1)\n",
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- "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (2.0.7)\n",
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- "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle->opendatasets) (6.1.0)\n",
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- "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle->opendatasets) (0.5.1)\n",
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- "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
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- "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.3.2)\n",
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- "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle->opendatasets) (3.6)\n",
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- "Installing collected packages: opendatasets\n",
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- "Successfully installed opendatasets-0.1.22\n"
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- ]
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- }
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- ]
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "download_data(url, data_dir)"
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- ],
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- "metadata": {
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- "id": "y-gTjPFggtAM",
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- "outputId": "02890664-5063-4698-d664-ee458de7b125",
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- }
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- "execution_count": 2,
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- "outputs": [
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- {
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- "output_type": "stream",
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- "name": "stdout",
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- "text": [
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- "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
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- "Your Kaggle username: bartmiller\n",
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- "Your Kaggle Key: ··········\n",
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- "Downloading glass.zip to /content/glass\n"
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- ]
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- },
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- {
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- "output_type": "stream",
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- "name": "stderr",
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- "text": [
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- "100%|██████████| 3.42k/3.42k [00:00<00:00, 2.36MB/s]"
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- ]
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- "outputId": "046caad7-0e5b-4f95-ebb1-ea972539b936"
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- "outputs": [
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- {
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- "output_type": "execute_result",
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- "data": {
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- "text/plain": [
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- " RI Na Mg Al Si K Ca Ba Fe Type\n",
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- "6 1.51743 13.30 3.60 1.14 73.09 0.58 8.17 0.0 0.0 1\n",
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- "138 1.51674 12.79 3.52 1.54 73.36 0.66 7.90 0.0 0.0 2\n",
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- "40 1.51793 12.79 3.50 1.12 73.03 0.64 8.77 0.0 0.0 1"
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- " const docLinkHtml = 'Like what you see? Visit the ' +\n",
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- " + ' to learn more about interactive tables.';\n",
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- " element.innerHTML = '';\n",
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- ]
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- },
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- "metadata": {},
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- "execution_count": 3
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- }
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- ],
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- "source": [
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- "import pandas as pd\n",
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- "glass_df = pd.read_csv(\"/content/glass/glass.csv\")\n",
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- "glass_df = glass_df.sample(frac = 1)\n",
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- "glass_df.head(3)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "from sklearn.model_selection import train_test_split\n",
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- "\n",
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- "X = glass_df.drop(\"Type\",axis=1)\n",
446
- "y = glass_df.Type\n",
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- "\n",
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- "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=125)"
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- ],
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- "metadata": {
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- "id": "7_eWUKS6hV2o"
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- },
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- "execution_count": 4,
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- "outputs": []
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- },
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- {
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- "cell_type": "code",
458
- "source": [
459
- "from sklearn.ensemble import RandomForestClassifier\n",
460
- "from sklearn.preprocessing import StandardScaler\n",
461
- "from sklearn.impute import SimpleImputer\n",
462
- "from sklearn.pipeline import Pipeline\n",
463
- "\n",
464
- "\n",
465
- "pipe = Pipeline(\n",
466
- " steps=[\n",
467
- " (\"imputer\", SimpleImputer()),\n",
468
- " (\"scaler\", StandardScaler()),\n",
469
- " (\"model\", RandomForestClassifier(n_estimators=100, random_state=125)),\n",
470
- " ]\n",
471
- ")\n",
472
- "pipe.fit(X_train, y_train)\n",
473
- "\n",
474
- "pipe.score(X_test, y_test)"
475
- ],
476
- "metadata": {
477
- "colab": {
478
- "base_uri": "https://localhost:8080/"
479
- },
480
- "id": "MTMLGHGuhvAA",
481
- "outputId": "cb35ca9e-8e24-49ec-8c9b-06d195905fd1"
482
- },
483
- "execution_count": 5,
484
- "outputs": [
485
- {
486
- "output_type": "execute_result",
487
- "data": {
488
- "text/plain": [
489
- "0.8"
490
- ]
491
- },
492
- "metadata": {},
493
- "execution_count": 5
494
- }
495
- ]
496
- },
497
- {
498
- "cell_type": "code",
499
- "source": [
500
- "from sklearn.metrics import classification_report\n",
501
- "\n",
502
- "y_pred = pipe.predict(X_test)\n",
503
- "print(classification_report(y_test,y_pred))"
504
- ],
505
- "metadata": {
506
- "colab": {
507
- "base_uri": "https://localhost:8080/"
508
- },
509
- "id": "EREHPUy_h0Zq",
510
- "outputId": "46d7bc64-0ddb-4be1-fbfb-b43c7ded4e35"
511
- },
512
- "execution_count": 6,
513
- "outputs": [
514
- {
515
- "output_type": "stream",
516
- "name": "stdout",
517
- "text": [
518
- " precision recall f1-score support\n",
519
- "\n",
520
- " 1 0.81 0.92 0.86 24\n",
521
- " 2 0.75 0.88 0.81 17\n",
522
- " 3 0.67 0.25 0.36 8\n",
523
- " 5 0.67 0.67 0.67 3\n",
524
- " 6 1.00 1.00 1.00 3\n",
525
- " 7 0.89 0.80 0.84 10\n",
526
- "\n",
527
- " accuracy 0.80 65\n",
528
- " macro avg 0.80 0.75 0.76 65\n",
529
- "weighted avg 0.79 0.80 0.78 65\n",
530
- "\n"
531
- ]
532
- }
533
- ]
534
- },
535
- {
536
- "cell_type": "code",
537
- "source": [
538
- "!pip install skops"
539
- ],
540
- "metadata": {
541
- "colab": {
542
- "base_uri": "https://localhost:8080/"
543
- },
544
- "id": "56jjXsBxiAiB",
545
- "outputId": "27f71a89-8eec-4e8a-b23b-f3f1f7329cbe"
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547
- "execution_count": 8,
548
- "outputs": [
549
- {
550
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551
- "name": "stdout",
552
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553
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555
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572
- "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (2.0.7)\n",
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- "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (2023.11.17)\n",
574
- "Installing collected packages: skops\n",
575
- "Successfully installed skops-0.9.0\n"
576
- ]
577
- }
578
- ]
579
- },
580
- {
581
- "cell_type": "code",
582
- "source": [
583
- "import skops.io as sio\n",
584
- "sio.dump(pipe, \"glass_pipeline.skops\")"
585
- ],
586
- "metadata": {
587
- "id": "wZARmF26h4S9"
588
- },
589
- "execution_count": 9,
590
- "outputs": []
591
- },
592
- {
593
- "cell_type": "code",
594
- "source": [
595
- "sio.load(\"glass_pipeline.skops\", trusted=True)\n"
596
- ],
597
- "metadata": {
598
- "colab": {
599
- "base_uri": "https://localhost:8080/",
600
- "height": 161
601
- },
602
- "id": "DQ1zj-mjiIRL",
603
- "outputId": "00d4ebb0-2f95-45f7-972f-05c257a3af53"
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- },
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- "execution_count": 10,
606
- "outputs": [
607
- {
608
- "output_type": "execute_result",
609
- "data": {
610
- "text/plain": [
611
- "Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),\n",
612
- " ('model', RandomForestClassifier(random_state=125))])"
613
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- "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()), (&#x27;scaler&#x27;, StandardScaler()),\n",
616
- " (&#x27;model&#x27;, RandomForestClassifier(random_state=125))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()), (&#x27;scaler&#x27;, StandardScaler()),\n",
617
- " (&#x27;model&#x27;, RandomForestClassifier(random_state=125))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=125)</pre></div></div></div></div></div></div></div>"
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745
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746
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747
- " Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=88940a0ba2d1088e0d93a684f25374ff2dfc47a1278ffaa94013d7b19a45d13f\n",
748
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749
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750
- "Installing collected packages: pydub, ffmpy, websockets, typing-extensions, tomlkit, shellingham, semantic-version, python-multipart, orjson, h11, colorama, annotated-types, aiofiles, uvicorn, starlette, pydantic-core, httpcore, pydantic, httpx, gradio-client, fastapi, gradio\n",
751
- " Attempting uninstall: typing-extensions\n",
752
- " Found existing installation: typing_extensions 4.5.0\n",
753
- " Uninstalling typing_extensions-4.5.0:\n",
754
- " Successfully uninstalled typing_extensions-4.5.0\n",
755
- " Attempting uninstall: pydantic\n",
756
- " Found existing installation: pydantic 1.10.13\n",
757
- " Uninstalling pydantic-1.10.13:\n",
758
- " Successfully uninstalled pydantic-1.10.13\n",
759
- "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
760
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761
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762
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763
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764
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765
- "\u001b[0mSuccessfully installed aiofiles-23.2.1 annotated-types-0.6.0 colorama-0.4.6 fastapi-0.108.0 ffmpy-0.3.1 gradio-4.12.0 gradio-client-0.8.0 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 orjson-3.9.10 pydantic-2.5.3 pydantic-core-2.14.6 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.32.0.post1 tomlkit-0.12.0 typing-extensions-4.9.0 uvicorn-0.25.0 websockets-11.0.3\n"
766
- ]
767
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769
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770
- {
771
- "cell_type": "code",
772
- "source": [
773
- "!pip install --upgrade typing\n",
774
- "\n"
775
- ],
776
- "metadata": {
777
- "colab": {
778
- "base_uri": "https://localhost:8080/",
779
- "height": 324
780
- },
781
- "id": "hkRt-nm-i7n3",
782
- "outputId": "c5674dce-ad3d-4d5b-c6e5-2c6b32b1f8dd"
783
- },
784
- "execution_count": 16,
785
- "outputs": [
786
- {
787
- "output_type": "stream",
788
- "name": "stdout",
789
- "text": [
790
- "Collecting typing\n",
791
- " Downloading typing-3.7.4.3.tar.gz (78 kB)\n",
792
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794
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795
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797
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798
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799
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800
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801
- ]
802
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803
- {
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805
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807
- "pip_warning": {
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810
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815
- }
816
- ]
817
- },
818
- {
819
- "cell_type": "code",
820
- "source": [
821
- "import gradio as gr\n",
822
- "import skops.io as sio\n",
823
- "\n",
824
- "pipe = sio.load(\"glass_pipeline.skops\", trusted=True)\n",
825
- "\n",
826
- "classes = [\n",
827
- " \"None\",\n",
828
- " \"Building Windows Float Processed\",\n",
829
- " \"Building Windows Non Float Processed\",\n",
830
- " \"Vehicle Windows Float Processed\",\n",
831
- " \"Vehicle Windows Non Float Processed\",\n",
832
- " \"Containers\",\n",
833
- " \"Tableware\",\n",
834
- " \"Headlamps\",\n",
835
- "]\n",
836
- "\n",
837
- "\n",
838
- "def classifier(RI, Na, Mg, Al, Si, K, Ca, Ba, Fe):\n",
839
- " pred_glass = pipe.predict([[RI, Na, Mg, Al, Si, K, Ca, Ba, Fe]])[0]\n",
840
- " label = f\"Predicted Glass label: **{classes[pred_glass]}**\"\n",
841
- " return label\n",
842
- "\n",
843
- "\n",
844
- "inputs = [\n",
845
- " gr.Slider(1.51, 1.54, step=0.01, label=\"Refractive Index\"),\n",
846
- " gr.Slider(10, 17, step=1, label=\"Sodium\"),\n",
847
- " gr.Slider(0, 4.5, step=0.5, label=\"Magnesium\"),\n",
848
- " gr.Slider(0.3, 3.5, step=0.1, label=\"Aluminum\"),\n",
849
- " gr.Slider(69.8, 75.4, step=0.1, label=\"Silicon\"),\n",
850
- " gr.Slider(0, 6.2, step=0.1, label=\"Potassium\"),\n",
851
- " gr.Slider(5.4, 16.19, step=0.1, label=\"Calcium\"),\n",
852
- " gr.Slider(0, 3, step=0.1, label=\"Barium\"),\n",
853
- " gr.Slider(0, 0.5, step=0.1, label=\"Iron\"),\n",
854
- "]\n",
855
- "outputs = [gr.Label(num_top_classes=7)]\n",
856
- "\n",
857
- "title = \"Glass Classification\"\n",
858
- "description = \"Enter the details to correctly identify glass type?\"\n",
859
- "\n",
860
- "gr.Interface(\n",
861
- " fn=classifier,\n",
862
- " inputs=inputs,\n",
863
- " outputs=outputs,\n",
864
- " title=title,\n",
865
- " description=description,\n",
866
- ").launch()"
867
- ],
868
- "metadata": {
869
- "colab": {
870
- "base_uri": "https://localhost:8080/",
871
- "height": 1000
872
- },
873
- "id": "A8KXp_EFiS1U",
874
- "outputId": "c021cdbf-b938-4951-f5e7-8bc0988e9d8a"
875
- },
876
- "execution_count": 1,
877
- "outputs": [
878
- {
879
- "output_type": "stream",
880
- "name": "stderr",
881
- "text": [
882
- "Exception in thread Thread-5 (attachment_entry):\n",
883
- "Traceback (most recent call last):\n",
884
- " File \"/usr/local/lib/python3.10/dist-packages/debugpy/server/api.py\", line 237, in listen\n",
885
- " sock, _ = endpoints_listener.accept()\n",
886
- " File \"/usr/lib/python3.10/socket.py\", line 293, in accept\n",
887
- " fd, addr = self._accept()\n",
888
- "TimeoutError: timed out\n",
889
- "\n",
890
- "During handling of the above exception, another exception occurred:\n",
891
- "\n",
892
- "Traceback (most recent call last):\n",
893
- " File \"/usr/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
894
- " self.run()\n",
895
- " File \"/usr/lib/python3.10/threading.py\", line 953, in run\n",
896
- " self._target(*self._args, **self._kwargs)\n",
897
- " File \"/usr/local/lib/python3.10/dist-packages/google/colab/_debugpy.py\", line 52, in attachment_entry\n",
898
- " debugpy.listen(_dap_port)\n",
899
- " File \"/usr/local/lib/python3.10/dist-packages/debugpy/public_api.py\", line 31, in wrapper\n",
900
- " return wrapped(*args, **kwargs)\n",
901
- " File \"/usr/local/lib/python3.10/dist-packages/debugpy/server/api.py\", line 143, in debug\n",
902
- " log.reraise_exception(\"{0}() failed:\", func.__name__, level=\"info\")\n",
903
- " File \"/usr/local/lib/python3.10/dist-packages/debugpy/server/api.py\", line 141, in debug\n",
904
- " return func(address, settrace_kwargs, **kwargs)\n",
905
- " File \"/usr/local/lib/python3.10/dist-packages/debugpy/server/api.py\", line 251, in listen\n",
906
- " raise RuntimeError(\"timed out waiting for adapter to connect\")\n",
907
- "RuntimeError: timed out waiting for adapter to connect\n"
908
- ]
909
- },
910
- {
911
- "output_type": "stream",
912
- "name": "stdout",
913
- "text": [
914
- "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
915
- "\n",
916
- "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
917
- "Running on public URL: https://efa6ecf31e4b5a440c.gradio.live\n",
918
- "\n",
919
- "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
920
- ]
921
- },
922
- {
923
- "output_type": "display_data",
924
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925
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926
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927
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928
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929
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930
- ]
931
- },
932
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933
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935
- "output_type": "execute_result",
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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