Spot_the_Synthetic.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMXPgdg+Gc12VUMUWO9NTuF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DTG2005/Adventure-Bot/blob/master/Spot_the_Synthetic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQyEtYxzEE5B",
        "outputId": "541fc145-2db5-4aad-93d3-e5cfc078df65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n",
            "Successfully installed accelerate-0.28.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105\n"
          ]
        }
      ],
      "source": [
        "! pip install transformers accelerate\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "pipe = pipeline(\"image-classification\", \"umm-maybe/AI-image-detector\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvdCW7iME8se",
        "outputId": "86f14a6b-771b-418b-8b22-f4b1d53b19eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration. Please open a PR/issue to update `preprocessor_config.json` to use `image_processor_type` instead of `feature_extractor_type`. This warning will be removed in v4.40.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle\n",
        "! chmod 600  ~/.kaggle/kaggle.json\n",
        "! kaggle competitions download -c spot-the-synthetic\n",
        "! mkdir data\n",
        "! unzip spot-the-synthetic.zip -d data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzjmXqMHFL9c",
        "outputId": "b16f7e81-8193-4958-a040-1566bc532ccc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n",
            "Downloading spot-the-synthetic.zip to /content\n",
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            "mkdir: cannot create directory ‘data’: File exists\n",
            "Archive:  spot-the-synthetic.zip\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def image_classifier(image):\n",
        "    outputs = pipe(image)\n",
        "    results = {}\n",
        "    for result in outputs:\n",
        "        results[result['label']] = result['score']\n",
        "    if results['human']>results['artificial']:\n",
        "        return \"Real\"\n",
        "    else:\n",
        "        return \"AI\""
      ],
      "metadata": {
        "id": "DUEudaTEObwb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image\n",
        "import os\n",
        "\n",
        "df = {}\n",
        "for img in os.walk(\"/content/data/Data/Test_Images\"):\n",
        "    for img2 in img[2]:\n",
        "      image = Image.open(\"/content/data/Data/Test_Images/\"+ img2)\n",
        "      # print(image_classifier(image))\n",
        "      df[img2[:-4]] = image_classifier(image)\n",
        "\n",
        "df = pd.DataFrame(list(df.items()), columns = [\"Id\", \"Label\"])\n",
        "df.to_csv(\"predictions.csv\", index=False)\n"
      ],
      "metadata": {
        "id": "1vpZLiihPvht"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}