Duplicate from gradio/translation_main

Co-authored-by: Ali Abdalla <aliabd@users.noreply.huggingface.co>

.gitattributes

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DESCRIPTION.md

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+This translation demo takes in the text, source and target languages, and returns the translation. It uses the Transformers library to set up the model and has a title, description, and example.

README.md

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+---
+title: translation_main
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.35.2
+app_file: run.py
+pinned: false
+duplicated_from: gradio/translation_main
+---

requirements.txt

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+git+https://github.com/huggingface/transformers
+gradio
+torch
+https://gradio-main-build.s3.amazonaws.com/4a0e387e7bbd487de4c5e6e8aee4cf7da0f19dc5/gradio-3.35.2-py3-none-any.whl

run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: translation\n", "### This translation demo takes in the text, source and target languages, and returns the translation. It uses the Transformers library to set up the model and has a title, description, and example.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio git+https://github.com/huggingface/transformers gradio torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline\n", "import torch\n", "\n", "# this model was loaded from https://hf.co/models\n", "model = AutoModelForSeq2SeqLM.from_pretrained(\"facebook/nllb-200-distilled-600M\")\n", "tokenizer = AutoTokenizer.from_pretrained(\"facebook/nllb-200-distilled-600M\")\n", "device = 0 if torch.cuda.is_available() else -1\n", "LANGS = [\"ace_Arab\", \"eng_Latn\", \"fra_Latn\", \"spa_Latn\"]\n", "\n", "def translate(text, src_lang, tgt_lang):\n", "    \"\"\"\n", "    Translate the text from source lang to target lang\n", "    \"\"\"\n", "    translation_pipeline = pipeline(\"translation\", model=model, tokenizer=tokenizer, src_lang=src_lang, tgt_lang=tgt_lang, max_length=400, device=device)\n", "    result = translation_pipeline(text)\n", "    return result[0]['translation_text']\n", "\n", "demo = gr.Interface(\n", "    fn=translate,\n", "    inputs=[\n", "        gr.components.Textbox(label=\"Text\"),\n", "        gr.components.Dropdown(label=\"Source Language\", choices=LANGS),\n", "        gr.components.Dropdown(label=\"Target Language\", choices=LANGS),\n", "    ],\n", "    outputs=[\"text\"],\n", "    examples=[[\"Building a translation demo with Gradio is so easy!\", \"eng_Latn\", \"spa_Latn\"]],\n", "    cache_examples=False,\n", "    title=\"Translation Demo\",\n", "    description=\"This demo is a simplified version of the original [NLLB-Translator](https://huggingface.co/spaces/Narrativaai/NLLB-Translator) space\"\n", ")\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+import torch
+
+# this model was loaded from https://hf.co/models
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+device = 0 if torch.cuda.is_available() else -1
+LANGS = ["ace_Arab", "eng_Latn", "fra_Latn", "spa_Latn"]
+
+def translate(text, src_lang, tgt_lang):
+    """
+    Translate the text from source lang to target lang
+    """
+    translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer, src_lang=src_lang, tgt_lang=tgt_lang, max_length=400, device=device)
+    result = translation_pipeline(text)
+    return result[0]['translation_text']
+
+demo = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.components.Textbox(label="Text"),
+        gr.components.Dropdown(label="Source Language", choices=LANGS),
+        gr.components.Dropdown(label="Target Language", choices=LANGS),
+    ],
+    outputs=["text"],
+    examples=[["Building a translation demo with Gradio is so easy!", "eng_Latn", "spa_Latn"]],
+    cache_examples=False,
+    title="Translation Demo",
+    description="This demo is a simplified version of the original [NLLB-Translator](https://huggingface.co/spaces/Narrativaai/NLLB-Translator) space"
+)
+
+demo.launch()