{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "l1bKo_b068E-", "outputId": "f5cc991c-349c-4a2a-92de-81df8de3166a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.1/20.1 MB\u001b[0m \u001b[31m35.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m64.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K 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Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "!pip install -q git+https://github.com/huggingface/transformers gradio torch" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "1vema-kT7C54" }, "outputs": [], "source": [ "!mkdir gpt2-models" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a6JjgaVM7SMY", "outputId": "768ec1df-e864-4d5d-89cd-ca4c3435c26e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/content\n", "total 20\n", "drwxr-xr-x 1 root root 4096 Sep 9 03:12 .\n", "drwxr-xr-x 1 root root 4096 Sep 9 03:10 ..\n", "drwxr-xr-x 4 root root 4096 Sep 7 13:23 .config\n", "drwxr-xr-x 2 root root 4096 Sep 9 03:12 gpt2-models\n", "drwxr-xr-x 1 root root 4096 Sep 7 13:24 sample_data\n" ] } ], "source": [ "!pwd && ls -al" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": 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and it has no effect\n", " inputs=gr.inputs.Textbox(lines=5, label=\"Input Text\"),\n", ":21: GradioDeprecationWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n", " outputs=gr.outputs.Textbox(label=\"Generated Text\"),\n" ] }, { "name": "stdout", "output_type": "stream", "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" ] }, { "data": { "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
\n Running on \n https://localhost:${port}${path}\n \n
\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)", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gradio as gr\n", "from transformers import pipeline\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "from transformers.utils.hub import move_cache\n", "\n", "move_cache()\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=\"./gpt2-models/\")\n", "model = AutoModelForCausalLM.from_pretrained(\"gpt2\")\n", "generator = pipeline('text-generation', model=model, tokenizer=tokenizer)\n", "\n", "def generate(text):\n", " result = generator(text, max_length=30, num_return_sequences=1)\n", " return result[0][\"generated_text\"]\n", "\n", "examples = [\n", " [\"The Moon's orbit around Earth has\"],\n", " [\"The smooth Borealis basin in the Northern Hemisphere covers 40%\"],\n", "]\n", "\n", "demo = gr.Interface(\n", " fn=generate,\n", " inputs=gr.inputs.Textbox(lines=5, label=\"Input Text\"),\n", " outputs=gr.outputs.Textbox(label=\"Generated Text\"),\n", " examples=examples\n", ")\n", "\n", "demo.launch()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Vz1e0FL075tK" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "00e271d319ac4d86a361b12f071bc565": { "model_module": "@jupyter-widgets/controls", 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