CNN_approach.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMXjHZQSa47JOTgVieSE4VC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rohandhiman5/Kaggle-challenge-by-cynaptics-club/blob/main/CNN_approach.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": "nOwVlu2Amo1G",
        "outputId": "758d2230-a644-4fdd-ed61-f12b6200186f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading spot-the-synthetic.zip to /content\n",
            " 91% 29.0M/31.8M [00:00<00:00, 65.3MB/s]\n",
            "100% 31.8M/31.8M [00:00<00:00, 57.7MB/s]\n"
          ]
        }
      ],
      "source": [
        "! mkdir ~/.kaggle\n",
        "! cp kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle competitions download spot-the-synthetic"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from zipfile import ZipFile\n",
        "file = 'spot-the-synthetic.zip'\n",
        "with ZipFile(file, 'r') as zip:\n",
        "    zip.extractall()"
      ],
      "metadata": {
        "id": "d7-X2Mi-n1Tb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "MnLbiVHtonqS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from PIL import Image"
      ],
      "metadata": {
        "id": "isGMmaXlp83O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_and_preprocess_images_from_folder(folder, target_size=(224,224)):\n",
        "    images = []\n",
        "    for filename in os.listdir(folder):\n",
        "        img = Image.open(os.path.join(folder, filename))\n",
        "        if img is not None:\n",
        "            img.thumbnail(target_size, Image.ANTIALIAS)\n",
        "            new_img = Image.new(\"RGB\", target_size, (255, 255, 255))\n",
        "            new_img.paste(img, ((target_size[0] - img.size[0]) // 2, (target_size[1] - img.size[1]) // 2))\n",
        "            img_array = np.array(new_img).astype('float32') / 255.0\n",
        "            images.append(img_array)\n",
        "    return np.array(images)\n"
      ],
      "metadata": {
        "id": "ToCSnoVBqJyS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ai_images = load_and_preprocess_images_from_folder('/content/Data/AI_Images')\n",
        "real_images = load_and_preprocess_images_from_folder('/content/Data/Real_Images')\n",
        "test_images = load_and_preprocess_images_from_folder('/content/Data/Test_Images')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3S4YaQ_qdqN",
        "outputId": "fbb320b6-e94a-406e-a574-b7b78e788fd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-aa644e78db35>:6: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use LANCZOS or Resampling.LANCZOS instead.\n",
            "  img.thumbnail(target_size, Image.ANTIALIAS)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ai_images[55]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GeXGtmJz7mam",
        "outputId": "f556750d-b0ee-4362-b42b-f142d2687e3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "\n",
              "       [[0.85490197, 0.85490197, 0.85490197],\n",
              "        [0.8666667 , 0.8666667 , 0.8666667 ],\n",
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              "        [0.90588236, 0.90588236, 0.90588236]],\n",
              "\n",
              "       [[0.77254903, 0.77254903, 0.77254903],\n",
              "        [0.79607844, 0.79607844, 0.79607844],\n",
              "        [0.80784315, 0.80784315, 0.80784315],\n",
              "        ...,\n",
              "        [0.84313726, 0.84313726, 0.84313726],\n",
              "        [0.88235295, 0.88235295, 0.88235295],\n",
              "        [0.8784314 , 0.8784314 , 0.8784314 ]],\n",
              "\n",
              "       [[0.8       , 0.8       , 0.8       ],\n",
              "        [0.7921569 , 0.7921569 , 0.7921569 ],\n",
              "        [0.8117647 , 0.8117647 , 0.8117647 ],\n",
              "        ...,\n",
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              "        [0.8509804 , 0.8509804 , 0.8509804 ]]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ai_labels = np.zeros(len(ai_images))\n",
        "real_labels = np.ones(len(real_images))\n",
        "ai_images.shape\n"
      ],
      "metadata": {
        "id": "-E9CHxfjv378",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e2098eb-5f51-4ca3-b2a2-7392e0a02079"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(655, 224, 224, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "real_images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNA5qbUc8bZY",
        "outputId": "a8463af6-efd8-4474-87eb-70f859b30b2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(600, 224, 224, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.concatenate((ai_images, real_images), axis=0)\n",
        "y_train = np.concatenate((ai_labels, real_labels), axis=0)\n"
      ],
      "metadata": {
        "id": "QGA3GccYv3dN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of X_train:\", X_train.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IeQNzx5tqoL5",
        "outputId": "f076920e-8732-4725-adf9-5656c9efbab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (1255, 224, 224, 3)\n",
            "Shape of y_train: (1255,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:2]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gwvv5fTqrGi",
        "outputId": "325a040a-deb6-49bb-bf09-614a67d8deb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train=y_train.reshape(-1,)"
      ],
      "metadata": {
        "id": "vZoDu0Z1rU5c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:2 ]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkQckAWjFcht",
        "outputId": "19d077c7-729d-4eb4-dffd-243f9dae3664"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[:1]"
      ],
      "metadata": {
        "id": "HYveQPniFhFT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras import layers\n",
        "from tensorflow.keras.models import Sequential"
      ],
      "metadata": {
        "id": "IRiYv3qjTwh8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "  [\n",
        "    layers.experimental.preprocessing.RandomFlip(\"horizontal\",\n",
        "                                                 input_shape=(224,\n",
        "                                                              224,\n",
        "                                                              3)),\n",
        "    layers.experimental.preprocessing.RandomRotation(0.1),\n",
        "    layers.experimental.preprocessing.RandomZoom(0.1),\n",
        "  ]\n",
        ")"
      ],
      "metadata": {
        "id": "AdS6nSGQ6p_e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes = 2\n",
        "\n",
        "model = Sequential([\n",
        "  data_augmentation,\n",
        "  layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
        "  layers.MaxPooling2D(),\n",
        "  layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
        "  layers.MaxPooling2D(),\n",
        "  layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
        "  layers.MaxPooling2D(),\n",
        "  layers.Dropout(0.2),\n",
        "  layers.Flatten(),\n",
        "  layers.Dense(128, activation='relu'),\n",
        "  layers.Dense(2, activation='sigmoid'),\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train, y_train, epochs=30)"
      ],
      "metadata": {
        "id": "CoyrJIaW6p1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e912fd41-8324-4bc8-abae-eef640ebe309"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/backend.py:5727: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
            "  output, from_logits = _get_logits(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 4s 45ms/step - loss: 0.9291 - accuracy: 0.5745\n",
            "Epoch 2/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.6674 - accuracy: 0.5801\n",
            "Epoch 3/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.6395 - accuracy: 0.6104\n",
            "Epoch 4/30\n",
            "40/40 [==============================] - 2s 47ms/step - loss: 0.6422 - accuracy: 0.6414\n",
            "Epoch 5/30\n",
            "40/40 [==============================] - 2s 45ms/step - loss: 0.6309 - accuracy: 0.6303\n",
            "Epoch 6/30\n",
            "40/40 [==============================] - 2s 42ms/step - loss: 0.6107 - accuracy: 0.6598\n",
            "Epoch 7/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.6096 - accuracy: 0.6614\n",
            "Epoch 8/30\n",
            "40/40 [==============================] - 2s 42ms/step - loss: 0.5973 - accuracy: 0.6829\n",
            "Epoch 9/30\n",
            "40/40 [==============================] - 2s 42ms/step - loss: 0.5914 - accuracy: 0.6813\n",
            "Epoch 10/30\n",
            "40/40 [==============================] - 2s 42ms/step - loss: 0.5858 - accuracy: 0.6845\n",
            "Epoch 11/30\n",
            "40/40 [==============================] - 2s 46ms/step - loss: 0.5768 - accuracy: 0.6916\n",
            "Epoch 12/30\n",
            "40/40 [==============================] - 2s 44ms/step - loss: 0.5713 - accuracy: 0.6900\n",
            "Epoch 13/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.5539 - accuracy: 0.6964\n",
            "Epoch 14/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.5484 - accuracy: 0.7155\n",
            "Epoch 15/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.5520 - accuracy: 0.7131\n",
            "Epoch 16/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.5417 - accuracy: 0.7235\n",
            "Epoch 17/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.5302 - accuracy: 0.7267\n",
            "Epoch 18/30\n",
            "40/40 [==============================] - 2s 47ms/step - loss: 0.5344 - accuracy: 0.7235\n",
            "Epoch 19/30\n",
            "40/40 [==============================] - 2s 45ms/step - loss: 0.5255 - accuracy: 0.7291\n",
            "Epoch 20/30\n",
            "40/40 [==============================] - 2s 42ms/step - loss: 0.5282 - accuracy: 0.7434\n",
            "Epoch 21/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.5158 - accuracy: 0.7267\n",
            "Epoch 22/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.5049 - accuracy: 0.7490\n",
            "Epoch 23/30\n",
            "40/40 [==============================] - 2s 43ms/step - loss: 0.4873 - accuracy: 0.7610\n",
            "Epoch 24/30\n",
            "15/40 [==========>...................] - ETA: 1s - loss: 0.5019 - accuracy: 0.7396"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yNE7t1qT6pqT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''cnn = models.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(32,32, 3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(2, activation='softmax')\n",
        "])'''"
      ],
      "metadata": {
        "id": "tCB3jI_hFvHx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''cnn.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])'''"
      ],
      "metadata": {
        "id": "nH6IbsKDF-YW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''cnn.fit(X_train, y_train, epochs=15)'''"
      ],
      "metadata": {
        "id": "5f1-DyT6GCOf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_images = load_and_preprocess_images_from_folder('/content/Data/Test_Images')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XAYoJseKGE21",
        "outputId": "27e778ba-3fab-491d-9358-90ae43a3094e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-5-aa644e78db35>:6: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use LANCZOS or Resampling.LANCZOS instead.\n",
            "  img.thumbnail(target_size, Image.ANTIALIAS)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test=test_images\n",
        "X_test.shape"
      ],
      "metadata": {
        "id": "pf8idah_KgXZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e183e61f-e806-46b0-b55c-b71eda49ff81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 224, 224, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred[:]"
      ],
      "metadata": {
        "id": "MuySfa6BKjP2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "416d956e-1f16-4a97-a6bc-2ca186f810eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 1s 34ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.5108174 , 0.39937377],\n",
              "       [0.5400308 , 0.27735117],\n",
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              "       [0.7908977 , 0.19137125],\n",
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          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_classes = [np.argmax(i) for i in y_pred]\n",
        "y_classes[:]"
      ],
      "metadata": {
        "id": "qi1pNSpzKpCj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a891df4f-4c45-4987-a1b6-8ead9691abbf"
      },
      "execution_count": null,
      "outputs": [
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = ['Real' if label == 1 else 'AI' for label in y_classes]\n",
        "ids = list(y_classes)\n",
        "print(\"ids:\", ids[:5])\n",
        "print(\"labels:\", labels[:5])\n"
      ],
      "metadata": {
        "id": "hjbplIOuOtk6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8896cea8-2814-441d-d700-73813554a168"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ids: [0, 0, 0, 0, 1]\n",
            "labels: ['AI', 'AI', 'AI', 'AI', 'Real']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ids[:])\n",
        "print(labels[:])"
      ],
      "metadata": {
        "id": "zV86pHFaPCPU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60e2911e-4a50-4972-bd35-a4d3b1fc02fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "['AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'Real', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'AI', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'AI', 'AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'AI', 'AI', 'AI', 'AI', 'AI', 'Real', 'AI', 'Real', 'AI', 'AI', 'AI', 'AI', 'AI', 'AI', 'AI', 'AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'Real', 'AI', 'AI', 'AI', 'AI', 'Real', 'AI', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI', 'AI', 'Real', 'AI', 'AI', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'AI', 'AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'AI', 'AI', 'Real', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI', 'AI', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'AI', 'Real', 'Real', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'AI', 'AI', 'AI', 'AI', 'Real', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI', 'Real', 'Real', 'AI', 'Real', 'AI', 'Real', 'Real', 'Real', 'AI', 'AI', 'AI', 'AI', 'AI', 'Real', 'Real', 'Real', 'Real', 'Real', 'Real', 'AI', 'Real', 'Real', 'AI', 'AI', 'Real', 'AI', 'AI', 'AI', 'Real', 'AI']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'id': ids, 'label': labels})"
      ],
      "metadata": {
        "id": "HyLiS_1sQOPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " #Save this DataFrame to CSV file\n",
        "df.to_csv('output3.csv', index=False)\n",
        "print(\"DataFrame saved to 'output.csv'\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9biZFZ1RRmx",
        "outputId": "934c0aa0-7d44-4fc1-b291-688bd8f25c61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame saved to 'output.csv'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T2p86Vy6RX8Y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}