Gradio.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ANYANTUDRE/NLP-Course-Hugging-Face/blob/main/Gradio.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
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      "execution_count": 3,
      "metadata": {
        "colab": {
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        "outputId": "a4a9b514-e673-4d48-c348-296aaa54b8f9"
      },
      "outputs": [
        {
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          "text": [
            "Successfully installed aiofiles-23.2.1 colorama-0.4.6 fastapi-0.110.1 ffmpy-0.3.2 gradio-4.25.0 gradio-client-0.15.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 orjson-3.10.0 pydub-0.25.1 python-multipart-0.0.9 ruff-0.3.5 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.37.2 tomlkit-0.12.0 uvicorn-0.29.0 websockets-11.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio"
      ]
    },
    {
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      "source": [
        "import gradio as gr\n",
        "from transformers import pipeline\n",
        "\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "qyM6HlxFkIob"
      },
      "execution_count": 8,
      "outputs": []
    },
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        "model = pipeline(\"text-generation\")\n",
        "\n",
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        "outputId": "eb779f9b-81ad-4a43-ee1f-063f69892ce5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to openai-community/gpt2 and revision 6c0e608 (https://huggingface.co/openai-community/gpt2).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
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              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27e371d15f144dd28dd77bb671f7ac7e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6d31256b97ad4af4aab9d1e191d54eb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8a8ed8f46054183ae28c64974ecf4c2"
            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbfe76bb0c334d7e92b2f2aa19b3a3fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60118ae1ec74439aa4890f6c7b409cf2"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gr.Interface(fn=predict, inputs=\"text\", outputs=\"text\").launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "WeYtz01ekEn7",
        "outputId": "d93c43f7-8634-438f-dda1-8884db7418c6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://48525ed28bdb2488a0.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://48525ed28bdb2488a0.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import gradio as gr\n",
        "\n",
        "\n",
        "def reverse_audio(audio):\n",
        "    sr, data = audio\n",
        "    reversed_audio = (sr, np.flipud(data))\n",
        "    return reversed_audio\n",
        "\n",
        "\n",
        "mic = gr.Audio(sources=\"microphone\", type=\"numpy\", label=\"Speak here...\")\n",
        "gr.Interface(reverse_audio, mic, \"audio\").launch()"
      ],
      "metadata": {
        "id": "_tsWxsfVkEv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "notes = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n",
        "\n",
        "def generate_tone(note, octave, duration):\n",
        "    sr = 48000\n",
        "    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n",
        "    frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n",
        "    duration = int(duration)\n",
        "    audio = np.linspace(0, duration, duration * sr)\n",
        "    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n",
        "    return (sr, audio)\n",
        "\n",
        "gr.Interface(\n",
        "    fn=generate_tone,\n",
        "    inputs=[ gr.Dropdown(notes, type=\"index\"),\n",
        "             gr.Slider(minimum=4, maximum=6, step=1),\n",
        "             gr.Textbox(type=\"number\", value=1, label=\"Duration in seconds\"),\n",
        "            ],\n",
        "    outputs=\"audio\",\n",
        ").lauch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "iqd_R6UXoiuA",
        "outputId": "e2dd335a-f82e-4878-9443-30515e5161c7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "`type` must be one of \"text\", \"password\", or \"email\".",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-afdf4e0dd8e1>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m     inputs=[ gr.Dropdown(notes, type=\"index\"),\n\u001b[1;32m     15\u001b[0m              \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSlider\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminimum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaximum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m              \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"number\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Duration in seconds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m             ],\n\u001b[1;32m     18\u001b[0m     \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"audio\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gradio/component_meta.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    157\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gradio/components/textbox.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, value, lines, max_lines, placeholder, label, info, every, show_label, container, scale, min_width, interactive, visible, elem_id, autofocus, autoscroll, elem_classes, render, type, text_align, rtl, show_copy_button)\u001b[0m\n\u001b[1;32m     81\u001b[0m         \"\"\"\n\u001b[1;32m     82\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"password\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"email\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'`type` must be one of \"text\", \"password\", or \"email\".'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: `type` must be one of \"text\", \"password\", or \"email\"."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "notes = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n",
        "\n",
        "\n",
        "def generate_tone(note, octave, duration):\n",
        "    sr = 48000\n",
        "    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n",
        "    frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n",
        "    duration = int(duration)\n",
        "    audio = np.linspace(0, duration, duration * sr)\n",
        "    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n",
        "    return (sr, audio)\n",
        "\n",
        "\n",
        "gr.Interface(\n",
        "    generate_tone,\n",
        "    [\n",
        "        gr.Dropdown(notes, type=\"index\"),\n",
        "        gr.Slider(minimum=4, maximum=6, step=1),\n",
        "        gr.Textbox(type=\"text\", value=1, label=\"Duration in seconds\"),\n",
        "    ],\n",
        "    \"audio\",\n",
        ").launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "OreUmWretSiz",
        "outputId": "958393cf-3c7a-4298-ed64-febe0e7a143e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://d5182ab1a68dbcd913.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://d5182ab1a68dbcd913.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bx0f1C11tq5f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = pipeline(\"automatic-speech-recognition\")\n",
        "\n",
        "def transcribe_audio(mic=None, file=None):\n",
        "  if mic is not None:\n",
        "    audio = mic\n",
        "  elif file is not None:\n",
        "    audio = file\n",
        "  else:\n",
        "    return \"You must either provide a mic recording or a file\"\n",
        "\n",
        "  transcription = model(audio)[\"text\"]\n",
        "  return transcription\n",
        "\n",
        "gr.Interface(\n",
        "    fn=transcribe_audio,\n",
        "    inputs= gr.Audio(sources=[\"microphone\", \"upload\"], type=\"filepath\"),\n",
        "    outputs=\"text\",\n",
        ").launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "IeOJrV_tza-K",
        "outputId": "ea5076c1-0c60-43ae-fb40-8c487aae8f52"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to facebook/wav2vec2-base-960h and revision 55bb623 (https://huggingface.co/facebook/wav2vec2-base-960h).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "Some weights of the model checkpoint at facebook/wav2vec2-base-960h were not used when initializing Wav2Vec2ForCTC: ['wav2vec2.encoder.pos_conv_embed.conv.weight_g', 'wav2vec2.encoder.pos_conv_embed.conv.weight_v']\n",
            "- This IS expected if you are initializing Wav2Vec2ForCTC 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 Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-base-960h and are newly initialized: ['wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original1', 'wav2vec2.masked_spec_embed']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://6b98cafc5024601d46.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://6b98cafc5024601d46.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S47TM8ZC1MXr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "title = \"GPT-J-6B\"\n",
        "description = \"Gradio Demo for GPT-J 6B, a transformer model trained using Ben Wang's Mesh Transformer JAX. 'GPT-J' refers to the class of model, while '6B' represents the number of trainable parameters. To use it, simply add your text, or click one of the examples to load them. Read more at the links below.\"\n",
        "article = \"<p style='text-align: center'><a href='https://github.com/kingoflolz/mesh-transformer-jax' target='_blank'>GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model</a></p>\"\n",
        "\n",
        "interf = gr.load(\n",
        "    \"huggingface/EleutherAI/gpt-j-6B\",\n",
        "    inputs=gr.Textbox(lines=5, label=\"Input Text\"),\n",
        "    #title=title,\n",
        "    #description=description,\n",
        "    #article=article,\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHWpDP0VO-HU",
        "outputId": "d08f0a2c-63c7-4e3c-a6f2-4a184de3e2da"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fetching model from: https://huggingface.co/EleutherAI/gpt-j-6B\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "interf.launch(debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "DBOQlMCZO-Kr",
        "outputId": "99f1444f-d204-474a-e13c-b6b3ac5cdf74"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Rerunning server... use `close()` to stop if you need to change `launch()` parameters.\n",
            "----\n",
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://76d23e876a1439bbe5.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://76d23e876a1439bbe5.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "description = \"Story generation with GPT-2\"\n",
        "title = \"Generate your own story\"\n",
        "examples = [[\"Adventurer is approached by a mysterious stranger in the tavern for a new quest.\"]]\n",
        "\n",
        "interface = gr.Interface.load(\"huggingface/pranavpsv/gpt2-genre-story-generator\",\n",
        "\n",
        ")\n",
        "\n",
        "interface.launch()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "QoXOozjfO-OX",
        "outputId": "7d93605e-bf33-462e-8ca1-2181027efe2d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'Dependency' object has no attribute 'launch'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-ef777d78de21>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m )\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0minterface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'Dependency' object has no attribute 'launch'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YPqNHPdlQ5CC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9iPWW42HRmTr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "description = \"BigGAN text-to-image demo.\"\n",
        "title = \"BigGAN ImageNet\"\n",
        "interface = gr.Interface.load(\"huggingface/osanseviero/BigGAN-deep-128\",\n",
        "            description=description,\n",
        "            title = title,\n",
        "            examples=[[\"american robin\"]]\n",
        ")\n",
        "interface.launch()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "rKcvnx1ORmYg",
        "outputId": "4ab5e325-2862-4d6e-bc6e-3661c43e153f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "EventListener._setup.<locals>.event_trigger() got an unexpected keyword argument 'description'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-5bdf810e42b8>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdescription\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"BigGAN text-to-image demo.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtitle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"BigGAN ImageNet\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m interface = gr.Interface.load(\"huggingface/osanseviero/BigGAN-deep-128\", \n\u001b[0m\u001b[1;32m      5\u001b[0m             \u001b[0mdescription\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdescription\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m             \u001b[0mtitle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: EventListener._setup.<locals>.event_trigger() got an unexpected keyword argument 'description'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gr.load(\"huggingface/gpt2\").launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "PLFyisVBRnY8",
        "outputId": "73c833a9-445a-4628-9e26-9427c19e59d1"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'gr' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-95271ce12322>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"huggingface/gpt2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'gr' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "io = gr.Interface.load(\"models/EleutherAI/gpt-neo-2.7B\")\n",
        "io(\"It was the best of times\")  # outputs model completion"
      ],
      "metadata": {
        "id": "UDz8hlbETvCk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uXHWgiHzT3HC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}