{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V8i3gjxY85ps",
        "outputId": "b5286759-f627-4e7d-89fb-91ced8842cad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Collecting h11>=0.8\n",
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            "Collecting uc-micro-py\n",
            "  Downloading uc_micro_py-1.0.1-py3-none-any.whl (6.2 kB)\n",
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4711 sha256=13f8ffda7263e49659b8cfa6e64058f5b17b4ea5d25f283bfe79404460f085ff\n",
            "  Stored in directory: /root/.cache/pip/wheels/ff/5b/59/913b443e7369dc04b61f607a746b6f7d83fb65e2e19fcc958d\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: rfc3986, pydub, ffmpy, websockets, uc-micro-py, sniffio, python-multipart, pycryptodome, orjson, mdurl, h11, aiofiles, uvicorn, markdown-it-py, linkify-it-py, anyio, starlette, mdit-py-plugins, httpcore, httpx, fastapi, gradio\n",
            "Successfully installed aiofiles-23.1.0 anyio-3.6.2 fastapi-0.93.0 ffmpy-0.3.0 gradio-3.20.1 h11-0.14.0 httpcore-0.16.3 httpx-0.23.3 linkify-it-py-2.0.0 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 mdurl-0.1.2 orjson-3.8.7 pycryptodome-3.17 pydub-0.25.1 python-multipart-0.0.6 rfc3986-1.5.0 sniffio-1.3.0 starlette-0.25.0 uc-micro-py-1.0.1 uvicorn-0.20.0 websockets-10.4\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "gr.Interface.load(\n",
        "    'huggingface/nlpconnect/vit-gpt2-image-captioning',\n",
        "    inputs = gr.Image(label=\"Carga una imagen\"),\n",
        "    title = \"Classificator\",\n",
        "    description = \"Obtener descripcion por imagen\"\n",
        ").launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "TFIAntoh9MCp",
        "outputId": "53099e44-244d-41c3-aef8-8a3060baf087"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fetching model from: https://huggingface.co/nlpconnect/vit-gpt2-image-captioning\n",
            "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    }
  ]
}