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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.29.2)\n",
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            "Requirement already satisfied: evaluate in /usr/local/lib/python3.10/dist-packages (0.4.0)\n",
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            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
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            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "pip install transformers datasets evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415,
          "referenced_widgets": [
            "8edc67e952f34a8f945e2db51f9425ee",
            "080f20bc389f485eba6f710ad28a0360",
            "73aac851a8ae485dbcda59638e1113f6",
            "4b6cd0b9424c4577957a3e10a890fa91",
            "1fc62562be594225adf880f49e467fe4",
            "33ba82aa3ee748d29445c1da004e01b3",
            "44e15dc93ae64a6d972442e22c0cd2de",
            "a880e3a9e7954c1f9d2f0ee8d85703e4",
            "ddd514ab790a4c5fa032822cb58f6609",
            "5ff4185688f646dc806f779490d32b37",
            "bedbd9c752d54e439583a6b276e9cb42",
            "40222f6a62854865a8a24e3ed82708c9",
            "562f64d325534570a7f2aee188b73363",
            "b19d32184744436ab999a0668bd79ef6",
            "d0e7330628eb4f659478e335558d6345",
            "d7b27461e8074b51a5b92d819a953124",
            "7c25bcf4491a4333b99f58970ee52e16"
          ]
        },
        "id": "84mMJtzcHk3L",
        "outputId": "3fef96d3-6f52-4dca-89ae-055247f333d1"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8edc67e952f34a8f945e2db51f9425ee"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "emotions_df = load_dataset(\"FastJobs/Visual_Emotional_Analysis\", split=\"train[:800]\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jePsbV2DHlCk",
        "outputId": "72fc3cd5-37c9-4f6a-f5d4-34327cd4cf03"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset imagefolder (/root/.cache/huggingface/datasets/FastJobs___imagefolder/FastJobs--Visual_Emotional_Analysis-bbb0f5e70847fc91/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(emotions_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PB3_wpmzJFsh",
        "outputId": "ae86b6e2-51d6-4c93-eb0e-acd7e428db53"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "800"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_df = emotions_df.train_test_split(test_size=0.2)"
      ],
      "metadata": {
        "id": "5Nw4bJfTHlLT"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# size of the train dataset\n",
        "len(emotions_df['train'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ppzENe5zRkxQ",
        "outputId": "06730bbc-80e6-4cac-f525-bc78eeadf233"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "640"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# size of the test dataset\n",
        "len(emotions_df['test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wXnineZgRnLw",
        "outputId": "544690f7-d579-4ef4-aa0a-2671b8ecbcb2"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "160"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create 2 dictionary \n",
        "# dic1: maps the label name to an integer\n",
        "# dic2: maps the label id(integer) to a label name\n",
        "labels = emotions_df[\"train\"].features[\"label\"].names\n",
        "label2id, id2label = dict(), dict()\n",
        "for i, label in enumerate(labels):\n",
        "    label2id[label] = str(i)\n",
        "    id2label[str(i)] = label\n"
      ],
      "metadata": {
        "id": "DoqhAfVTR4Qs"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label2id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aRTpaEITR5UN",
        "outputId": "06001bae-a14d-47e2-8c1b-721e4e94748e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'anger': '0',\n",
              " 'contempt': '1',\n",
              " 'disgust': '2',\n",
              " 'fear': '3',\n",
              " 'happy': '4',\n",
              " 'neutral': '5',\n",
              " 'sad': '6',\n",
              " 'surprise': '7'}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id2label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1gyQ1ZZR5XV",
        "outputId": "191228fa-1b9e-4ac3-f9e3-a5028d37ff57"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0': 'anger',\n",
              " '1': 'contempt',\n",
              " '2': 'disgust',\n",
              " '3': 'fear',\n",
              " '4': 'happy',\n",
              " '5': 'neutral',\n",
              " '6': 'sad',\n",
              " '7': 'surprise'}"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoImageProcessor\n",
        "\n",
        "checkpoint = \"google/vit-base-patch16-224-in21k\"\n",
        "image_processor = AutoImageProcessor.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "id": "QmVyt4p1R5a6"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y3wZZw6QR5eU"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "npIDtsLgTROr"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from PIL import Image\n",
        "\n",
        "# convert image to a tensor\n",
        "def convert_to_tf_tensor(image: Image):\n",
        "    np_image = np.array(image)\n",
        "    tf_image = tf.convert_to_tensor(np_image)\n",
        "    # `expand_dims()` is used to add a batch dimension since\n",
        "    # the TF augmentation layers operates on batched inputs.\n",
        "    return tf.expand_dims(tf_image, 0)\n",
        "\n",
        "\n",
        "def preprocess_train(example_batch):\n",
        "    \"\"\"Apply train_transforms across a batch.\"\"\"\n",
        "    images = [\n",
        "        train_data_augmentation(convert_to_tf_tensor(image.convert(\"RGB\"))) for image in example_batch[\"image\"]\n",
        "    ]\n",
        "    example_batch[\"pixel_values\"] = [tf.transpose(tf.squeeze(image)) for image in images]\n",
        "    return example_batch\n",
        "\n",
        "\n",
        "def preprocess_val(example_batch):\n",
        "    \"\"\"Apply val_transforms across a batch.\"\"\"\n",
        "    images = [\n",
        "        val_data_augmentation(convert_to_tf_tensor(image.convert(\"RGB\"))) for image in example_batch[\"image\"]\n",
        "    ]\n",
        "    example_batch[\"pixel_values\"] = [tf.transpose(tf.squeeze(image)) for image in images]\n",
        "    return example_batch"
      ],
      "metadata": {
        "id": "kWOEaQ8FRHzA"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ma5JiYWITRRl"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# apply transform to the training and testing dataset\n",
        "\n",
        "emotions_df[\"train\"].set_transform(preprocess_train)\n",
        "emotions_df[\"test\"].set_transform(preprocess_val)"
      ],
      "metadata": {
        "id": "UpWd7hc7RH3I"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create a batch of examples using DefaultDataCollator\n",
        "from transformers import DefaultDataCollator\n",
        "\n",
        "data_collator = DefaultDataCollator(return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "0zQOcaYBTRVA"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zPYyzlsNTRfg"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate accuracy\n",
        "import evaluate\n",
        "\n",
        "accuracy = evaluate.load(\"accuracy\")"
      ],
      "metadata": {
        "id": "fVaQvixOVVsz"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return accuracy.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "nEj80b7FTseY"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vn9G6JZ0Tsh4"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "TRAIN"
      ],
      "metadata": {
        "id": "LvDvoIFzUEk-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import create_optimizer\n",
        "\n",
        "batch_size = 16\n",
        "num_epochs = 20\n",
        "num_train_steps = len(emotions_df[\"train\"]) * num_epochs\n",
        "learning_rate = 3e-4\n",
        "weight_decay_rate = 0.01\n",
        "\n",
        "optimizer, lr_schedule = create_optimizer(\n",
        "    init_lr=learning_rate,\n",
        "    num_train_steps=num_train_steps,\n",
        "    weight_decay_rate=weight_decay_rate,\n",
        "    num_warmup_steps=0,\n",
        ")"
      ],
      "metadata": {
        "id": "xGYIJKUHTskw"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M3ZPsb9LTsoL"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModelForImageClassification\n",
        "\n",
        "model = TFAutoModelForImageClassification.from_pretrained(\n",
        "    checkpoint,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KABXS5tkfFM8",
        "outputId": "4e5cbbc5-4334-4e5d-f96e-84bed9153db3"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at google/vit-base-patch16-224-in21k were not used when initializing TFViTForImageClassification: ['vit/pooler/dense/kernel:0', 'vit/pooler/dense/bias:0']\n",
            "- This IS expected if you are initializing TFViTForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFViTForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lZMOoitIUexL"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# avoiding overfitting \n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "size = (image_processor.size[\"height\"], image_processor.size[\"width\"])\n",
        "\n",
        "# Transformations for the training set\n",
        "# data augmentation to make the model more robust and to avoid overfitting\n",
        "train_data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        layers.RandomCrop(size[0], size[1]),\n",
        "        layers.Rescaling(scale=1.0 / 127.5, offset=-1),\n",
        "        layers.RandomFlip(\"horizontal\"),\n",
        "        layers.RandomRotation(factor=0.02),\n",
        "        layers.RandomZoom(height_factor=0.2, width_factor=0.2),\n",
        "    ],\n",
        "    name=\"train_data_augmentation\",\n",
        ")\n",
        "\n",
        "# Transformations for the validation set\n",
        "val_data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        layers.CenterCrop(size[0], size[1]),\n",
        "        layers.Rescaling(scale=1.0 / 127.5, offset=-1),\n",
        "    ],\n",
        "    name=\"val_data_augmentation\",\n",
        ")"
      ],
      "metadata": {
        "id": "EtXaPaGCVE7e"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4eKP4h7rVFNr"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# converting our train dataset to tensor dataset (tf.data.Dataset)\n",
        "tf_train_dataset = emotions_df[\"train\"].to_tf_dataset(\n",
        "    columns=\"pixel_values\", label_cols=\"label\", shuffle=True, batch_size=batch_size, collate_fn=data_collator\n",
        ")\n",
        "\n",
        "# converting our test dataset to tensor dataset (tf.data.Dataset)\n",
        "tf_eval_dataset = emotions_df[\"test\"].to_tf_dataset(\n",
        "    columns=\"pixel_values\", label_cols=\"label\", shuffle=True, batch_size=batch_size, collate_fn=data_collator\n",
        ")"
      ],
      "metadata": {
        "id": "3j70Yv9dRH6t"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "\n",
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "model.compile(optimizer=optimizer, loss=loss)"
      ],
      "metadata": {
        "id": "iiDCBtU5UjqM"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback\n",
        "\n",
        "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)\n",
        "push_to_hub_callback = PushToHubCallback(\n",
        "    output_dir=\"emotions_classifier\",\n",
        "    tokenizer=image_processor,\n",
        "    save_strategy=\"no\",\n",
        ")\n",
        "callbacks = [metric_callback, push_to_hub_callback]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kt3kPnOkUjuG",
        "outputId": "ceb5b9e9-768b-46ea-8df1-45022649b6f4"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/emotions_classifier is already a clone of https://huggingface.co/CynthiaCR/emotions_classifier. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/emotions_classifier is already a clone of https://huggingface.co/CynthiaCR/emotions_classifier. Make sure you pull the latest changes with `repo.git_pull()`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920,
          "referenced_widgets": [
            "a965aa730e164b249ff040457c944d94",
            "cdefc2d658bc4bd3ba8b3b4d0affd263",
            "f41f320a42c54f90996b54af83689aff",
            "833a251db60140a598b9b4d28583271b",
            "b157a9c50b834c24acc1445c803c670c",
            "64deb634ee7249ad8ed9bd93c1979459",
            "b41049a008604c1ab7bdb4646092628d",
            "6d971fa517aa4c1295522279f29f0258",
            "4f316fbfcf3644cd84a3f146265c9ba2",
            "06067b563aa54cc386e4954a9702042f",
            "e2dca77a089e43caa918174707165b56"
          ]
        },
        "id": "k1m8BvqxVt-t",
        "outputId": "2594dd93-d133-4ae0-c4c9-50460962540e"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "40/40 [==============================] - 69s 1s/step - loss: 2.0363 - val_loss: 2.0960 - accuracy: 0.1000\n",
            "Epoch 2/20\n",
            "40/40 [==============================] - 47s 1s/step - loss: 2.0822 - val_loss: 2.1254 - accuracy: 0.0813\n",
            "Epoch 3/20\n",
            "40/40 [==============================] - 47s 1s/step - loss: 1.9916 - val_loss: 1.9392 - accuracy: 0.2062\n",
            "Epoch 4/20\n",
            "40/40 [==============================] - 47s 1s/step - loss: 1.9223 - val_loss: 1.8385 - accuracy: 0.1688\n",
            "Epoch 5/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.8213 - val_loss: 1.7294 - accuracy: 0.2313\n",
            "Epoch 6/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.6940 - val_loss: 1.6953 - accuracy: 0.2625\n",
            "Epoch 7/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.7153 - val_loss: 1.6009 - accuracy: 0.3187\n",
            "Epoch 8/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.5788 - val_loss: 1.6385 - accuracy: 0.2750\n",
            "Epoch 9/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.5359 - val_loss: 1.5635 - accuracy: 0.3438\n",
            "Epoch 10/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.4768 - val_loss: 1.6180 - accuracy: 0.3250\n",
            "Epoch 11/20\n",
            "40/40 [==============================] - 50s 1s/step - loss: 1.4746 - val_loss: 1.6063 - accuracy: 0.3125\n",
            "Epoch 12/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.5163 - val_loss: 1.5641 - accuracy: 0.3625\n",
            "Epoch 13/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.4692 - val_loss: 1.5722 - accuracy: 0.3063\n",
            "Epoch 14/20\n",
            "40/40 [==============================] - 50s 1s/step - loss: 1.4468 - val_loss: 1.7363 - accuracy: 0.3500\n",
            "Epoch 15/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.7116 - val_loss: 1.7531 - accuracy: 0.2687\n",
            "Epoch 16/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.5334 - val_loss: 1.5908 - accuracy: 0.2562\n",
            "Epoch 17/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.4988 - val_loss: 1.5169 - accuracy: 0.3312\n",
            "Epoch 18/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.4605 - val_loss: 1.5041 - accuracy: 0.2812\n",
            "Epoch 19/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.3545 - val_loss: 1.4824 - accuracy: 0.3187\n",
            "Epoch 20/20\n",
            "40/40 [==============================] - 48s 1s/step - loss: 1.3846 - val_loss: 1.6122 - accuracy: 0.2687\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file tf_model.h5:   0%|          | 1.00/328M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a965aa730e164b249ff040457c944d94"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/CynthiaCR/emotions_classifier\n",
            "   6794f2e..9552b39  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/CynthiaCR/emotions_classifier\n",
            "   6794f2e..9552b39  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f131a6aae60>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction"
      ],
      "metadata": {
        "id": "29ZPkramjcir"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C_9-dH4cVuEB"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds = load_dataset(\"FastJobs/Visual_Emotional_Analysis\", split=\"train[:10]\")\n",
        "ds"
      ],
      "metadata": {
        "id": "JUFTj8TYiBwS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c22db2f0-0443-4aa9-9346-739967bb43b8"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset imagefolder (/root/.cache/huggingface/datasets/FastJobs___imagefolder/FastJobs--Visual_Emotional_Analysis-bbb0f5e70847fc91/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['image', 'label'],\n",
              "    num_rows: 10\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image = ds[\"image\"][0]"
      ],
      "metadata": {
        "id": "WBXiMJ48qBKl"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"image-classification\", model=\"CynthiaCR/emotions_classifier\")\n",
        "classifier(image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
          "referenced_widgets": [
            "75599d75d500474db4e9ca8801af0ac6",
            "5d8c864d082047b2a3b180985ae0b698",
            "502dd70810054869bb01aae69ab2431d",
            "53ff5a46800543d7a2e6ed532e0085eb",
            "8a3110bdb7844528a867a3f2d0daf028",
            "dbceb3e2a8b144bd8a899e7778b9fb67",
            "dc6982a803d34c2b9366323f44cf0ef7",
            "e4fa00ecdf7e4878bd07e3e81d22f7b0",
            "708979e23d1f4833b31b5c8f8c208cb4",
            "d0432428e4524f318bff7ccf2f91c6b4",
            "25a5031e71144bf4bec62694c16f39e2"
          ]
        },
        "id": "QkaFEJg2Ujxl",
        "outputId": "5ed6475b-9d54-48b8-b175-a16e39341a1d"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/344M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75599d75d500474db4e9ca8801af0ac6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFViTForImageClassification.\n",
            "\n",
            "All the layers of TFViTForImageClassification were initialized from the model checkpoint at CynthiaCR/emotions_classifier.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTForImageClassification for predictions without further training.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.32123512029647827, 'label': 'fear'},\n",
              " {'score': 0.31210750341415405, 'label': 'sad'},\n",
              " {'score': 0.1644315868616104, 'label': 'anger'},\n",
              " {'score': 0.10217338800430298, 'label': 'disgust'},\n",
              " {'score': 0.04358164221048355, 'label': 'contempt'}]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoImageProcessor\n",
        "\n",
        "image_processor = AutoImageProcessor.from_pretrained(\"CynthiaCR/emotions_classifier\")\n",
        "inputs = image_processor(image, return_tensors=\"tf\")"
      ],
      "metadata": {
        "id": "0vbEKCwX0ybE"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sqhjJ_9jkdgl"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModelForImageClassification\n",
        "\n",
        "model = TFAutoModelForImageClassification.from_pretrained(\"CynthiaCR/emotions_classifier\")\n",
        "logits = model(**inputs).logits"
      ],
      "metadata": {
        "id": "iCMCASWf0yew",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f38799c-8478-4b6f-fba3-0beaa95ed8f5"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFViTForImageClassification.\n",
            "\n",
            "All the layers of TFViTForImageClassification were initialized from the model checkpoint at CynthiaCR/emotions_classifier.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTForImageClassification for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7iSRY48Gkdti"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
        "model.config.id2label[predicted_class_id]"
      ],
      "metadata": {
        "id": "pJUFDX_e0yh9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "c09c9df5-41d8-418e-a44c-0cab27fe28e1"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'fear'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    }
  ]
}