gradio_image_classifier_app.ipynb

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      "cell_type": "code",
      "source": [
        "pip install gradio\n"
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        "id": "c4mdY_Dd37Ro"
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      "source": [
        "import gradio as gr\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import json\n",
        "import requests\n",
        "from os.path import dirname, realpath, join"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the model\n",
        "\n",
        "inception_net = tf.keras.applications.InceptionV3()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkSPnaVOYFlo",
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      "execution_count": 4,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5\n",
            "96112376/96112376 [==============================] - 1s 0us/step\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "# Load human-readable labels for ImageNet.\n",
        "response = requests.get(\"https://git.io/JJkYN\")\n",
        "labels = response.text.split(\"\\n\")\n"
      ],
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      "execution_count": 8,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Custom Function\n",
        "\n",
        "def classify_image(inp):\n",
        "  inp = np.expand_dims(inp, 0)\n",
        "  inp = tf.keras.applications.inception_v3.preprocess_input(inp)\n",
        "  prediction = inception_net.predict(inp).flatten()\n",
        "  return {labels[i]: float(prediction[i]) for i in range(1000)}"
      ],
      "metadata": {
        "id": "ioopTTarY225"
      },
      "execution_count": 9,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "image = gr.inputs.Image(shape=(299, 299))\n",
        "label = gr.outputs.Label(num_top_classes=3)\n",
        "\n",
        "gr.Interface(fn=classify_image, inputs=image, outputs=label).launch();"
      ],
      "metadata": {
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        "id": "iIZIvaiNZpBd",
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      "execution_count": 10,
      "outputs": [
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          "text": [
            "/usr/local/lib/python3.8/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
            "  warnings.warn(value)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
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