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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "gradio_demos.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyNhpeeMxUCUtvCsAPt4I1BZ",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/nlp-en-es/nlp-de-cero-a-cien/blob/main/6_demos/gradio_demos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "z5L1Z2AcwUPX"
},
"source": [
"!pip install transformers gradio sentencepiece"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "GC1HwQjqQRjS"
},
"source": [
"# Example 1. Normal inference of a model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "AHRKQwWuwrw3"
},
"source": [
"import gradio as gr\n",
"\n",
"from transformers import pipeline\n",
"\n",
"pipe = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-es\")\n",
"\n",
"def predict(text):\n",
" return pipe(text)[0][\"translation_text\"]\n",
" \n",
"title = \"Interactive demo: Helsinki-NLP English to Spanish Translation\"\n",
"\n",
"iface = gr.Interface(\n",
" fn=predict, \n",
" inputs=[gr.inputs.Textbox(label=\"text\", lines=3)],\n",
" outputs='text',\n",
" title=title,\n",
" examples=[[\"Hello! My name is Omar\"], [\"I like this workshop\"]]\n",
")\n",
"\n",
"iface.launch(debug=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "PwTqI-t0QZtM"
},
"source": [
"# Example 2. Using Inference API from Hugging Face"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EDkCXgXBx_MU"
},
"source": [
"import gradio as gr\n",
"\n",
"description = \"BigGAN text-to-image demo.\"\n",
"title = \"BigGAN ImageNet\"\n",
"\n",
"interface = gr.Interface.load(\n",
" \"huggingface/osanseviero/BigGAN-deep-128\", \n",
" description=description,\n",
" title = title,\n",
" examples=[[\"american robin\"], [\"chest\"], [\"soap bubble\"]]\n",
")\n",
"\n",
"interface.launch()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "WffHZhIzURoW"
},
"source": [
"# Example 3. Using series of models"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sS6iAMYKQpkH"
},
"source": [
"import gradio as gr\n",
"from gradio.mix import Series\n",
"\n",
"description = \"Generate your own D&D story!\"\n",
"title = \"Spanish Story Generator using Opus MT and GPT-2\"\n",
"\n",
"translator_es = gr.Interface.load(\"huggingface/Helsinki-NLP/opus-mt-es-en\")\n",
"story_gen = gr.Interface.load(\"huggingface/pranavpsv/gpt2-genre-story-generator\")\n",
"translator_en = gr.Interface.load(\"huggingface/Helsinki-NLP/opus-mt-en-es\")\n",
"\n",
"examples = [[\"La aventura comienza en\"]]\n",
"\n",
"interface = Series(translator_es, story_gen, translator_en, description = description,\n",
" title = title,\n",
" examples=examples, \n",
" inputs = gr.inputs.Textbox(lines = 10)\n",
")\n",
"\n",
"interface.launch()"
],
"execution_count": null,
"outputs": []
}
]
} |