Spaces:
No application file
No application file
File size: 3,946 Bytes
dce5a8c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/parth-pai/Learners_Space_2023_NLP/blob/main/LS_Gradio_Run_Final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yOSyP10ss3nE"
},
"source": [
"##**Mounting Drive**\n",
"First we mount the google drive here to access the required files"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3iod2CiGnm9C",
"outputId": "f5662837-44f7-4d45-dbf6-3f09a8cbb1ab"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive', force_remount=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UftTXc3ctEH8"
},
"source": [
"Install required libraries again here as done in Training code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "34OZ1dPEn_0k"
},
"outputs": [],
"source": [
"!pip install -q gradio transformers sentencepiece"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ssIvx0O_XeW5"
},
"source": [
"##**Displaying using Gradio**\n",
"Now we create an interactive app environment using `gradio`. We can see language translation happening with this good looking and interactive app interface."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aXNwmCQmofdl"
},
"outputs": [],
"source": [
"from transformers import AutoModelForSeq2SeqLM, pipeline, AutoTokenizer\n",
"import gradio as gr"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3xbtLJmntR1e"
},
"source": [
"First input the model, the tokenizer and translation pipeline. Then define the function that gradio will be using and input required arguments into gradio giving title and description. Then launch the interface using `iface.launch()`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dPtYKVNCsa_5"
},
"outputs": [],
"source": [
"model_checkpoint = AutoModelForSeq2SeqLM.from_pretrained('/content/drive/MyDrive/LS_NLP_Sameer')\n",
"tokenizer=AutoTokenizer.from_pretrained('/content/drive/MyDrive/LS_NLP_Sameer')\n",
"translator = pipeline(\"translation\", model=model_checkpoint)\n",
"def translation(text):\n",
" return translator(text)[0]['translation_text']\n",
"iface = gr.Interface(\n",
" fn=translation,\n",
" inputs=gr.inputs.Textbox(label=\"Input English Text\"),\n",
" outputs=gr.outputs.Textbox(label=\"Translated Italian Text\"),\n",
" title=\"English to Italian Translation\",\n",
" description=\"Translate English text to Italian using a fine-tuned model.\",\n",
")\n",
"\n",
"iface.launch()"
]
}
],
"metadata": {
"colab": {
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|