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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JW_q5khKnpb7",
"outputId": "56c6c160-d1bc-43e1-c25b-677f149e230b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.1)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.14.0)\n",
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]
}
],
"source": [
"!pip install transformers\n",
"!pip install huggingface_hub\n",
"!python -c \"from huggingface_hub.hf_api import HfFolder; HfFolder.save_token(<YOUR_TOKEN_HERE>)\""
]
},
{
"cell_type": "code",
"source": [
"!pip install sentencepiece\n",
"nltk.download('punkt')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "guoGKlR4n1tY",
"outputId": "2653425f-2888-4318-cb4f-03a38e805077"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"[nltk_data] Downloading package punkt to /root/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"!pip install gradio"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yBUmeZpGo9OX",
"outputId": "82e89bb1-c748-4314-899b-4c85bafafa5f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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]
}
]
},
{
"cell_type": "code",
"source": [
"import requests\n",
"from transformers import pipeline\n",
"import nltk\n",
"from nltk import sent_tokenize\n",
"from transformers import MBartForConditionalGeneration, MBart50TokenizerFast\n",
"from transformers import pipeline"
],
"metadata": {
"id": "laNjJ5sHn7EP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Use a pipeline as a high-level helper\n",
"from transformers import pipeline\n",
"\n",
"pipe = pipeline(\"text2text-generation\", model=\"SnypzZz/Llama2-13b-Language-translate\", use_fast = False)"
],
"metadata": {
"id": "GyqW7CypoCsX"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"tokenizer = MBart50TokenizerFast.from_pretrained(\"SnypzZz/Llama2-13b-Language-translate\", src_lang=\"en_XX\")\n",
"model = None\n",
"model_loaded = False"
],
"metadata": {
"id": "XrBJH1-ToJW3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"api_token_header = \"\"\n",
"with open('/content/secrets.py', 'r') as f:\n",
" api_token_header = f.read()"
],
"metadata": {
"id": "yvoISQtlyyF6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def load_model():\n",
" global model, model_loaded\n",
" model = MBartForConditionalGeneration.from_pretrained(\"SnypzZz/Llama2-13b-Language-translate\")\n",
" model_loaded =True\n",
" return model"
],
"metadata": {
"id": "Z2HyW64woSps"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def translation(text,dest_lang,dest_lang_code, src_lang_code):\n",
"\n",
" if(dest_lang_code == src_lang_code):\n",
" return \"Please select different languages to translate between.\"\n",
"\n",
" # headers = {\"Authorization\": f\"Bearer {secrets_sih.api_token_header}\"}\n",
" headers = {\"Authorization\": f\"Bearer {api_token_header}\"}\n",
"\n",
" # Bengali Done\n",
" if(dest_lang == \"Bengali\" and src_lang_code == \"en_XX\"):\n",
" API_URL = \"https://api-inference.huggingface.co/models/csebuetnlp/banglat5_nmt_en_bn\"\n",
" def query(payload):\n",
" response = requests.post(API_URL, headers=headers, json=payload)\n",
" return response.json()\n",
" output = query({\n",
" \"inputs\": text,\n",
" })\n",
" print(output)\n",
" return output[0]['translation_text']\n",
" else:\n",
" global model\n",
" if model:\n",
" pass\n",
" else:\n",
" model = load_model()\n",
" loaded_model = model\n",
" tokenizer = MBart50TokenizerFast.from_pretrained(\"SnypzZz/Llama2-13b-Language-translate\", src_lang=src_lang_code)\n",
" #model_inputs = tokenizer(text, return_tensors=\"pt\")\n",
" loaded_model_inputs = tokenizer(text, return_tensors=\"pt\")\n",
"\n",
" # translate\n",
" generated_tokens = loaded_model.generate(\n",
" **loaded_model_inputs,\n",
" forced_bos_token_id=tokenizer.lang_code_to_id[dest_lang_code]\n",
" )\n",
" output = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)\n",
" print(output)\n",
" return output[0]"
],
"metadata": {
"id": "q2H24G_lobxo"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def main_translation(text,dest_lang_code,src_lang_code):\n",
"\n",
" codes = {\"en_XX\":\"English\",\"bn_IN\":\"Bengali\", \"en_GB\":\"English\",\"gu_IN\":\"Gujarati\",\"hi_IN\":\"Hindi\",\"ta_IN\":\"Tamil\",\"te_IN\":\"Telugu\",\"mr_IN\":\"Marathi\"}\n",
" dest_lang = codes[dest_lang_code]\n",
" src_lang = codes[src_lang_code]\n",
"\n",
" sentences = sent_tokenize(text)\n",
" output = \"\"\n",
" for line in sentences:\n",
" output += translation(line,dest_lang,dest_lang_code, src_lang_code)\n",
" return {\"output\":output}"
],
"metadata": {
"id": "Q8-OWT7nodES"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"main_translation(\"Hello World\", \"hi_IN\",\"en_XX\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EHt29-U2wMp7",
"outputId": "c0cd8454-603e-4ed9-919a-cf6801397864"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"['हेलो वर्ल्ड']\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'output': 'हेलो वर्ल्ड'}"
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"source": [
"#User Interface\n",
"\n",
"import gradio as gr\n",
"\n",
"\n",
"def test(text, src, dest):\n",
" ans = main_translation(text,dest,src)\n",
" return ans['output']\n",
"demo = gr.Interface(\n",
" test,\n",
" [\"textbox\",\n",
" gr.Dropdown(\n",
" [(\"English\", \"en_XX\"), (\"Hindi\",\"hi_IN\"), (\"Bengali\",\"bn_IN\"), (\"Gujarati\",\"gu_IN\"), (\"Tamil\",\"ta_IN\"), (\"Telugu\",\"te_IN\"), (\"Marathi\",\"mr_IN\")], label=\"Source\", info=\"Select the Source Language!\"\n",
" ),\n",
" gr.Dropdown(\n",
" [(\"English\", \"en_XX\"), (\"Hindi\",\"hi_IN\"), (\"Bengali\",\"bn_IN\"), (\"Gujarati\",\"gu_IN\"), (\"Tamil\",\"ta_IN\"), (\"Telugu\",\"te_IN\"), (\"Marathi\",\"mr_IN\")], label=\"Destination\", info=\"Select the Destination Language!\"\n",
" ),\n",
" ],\n",
" outputs=[\"textbox\"],\n",
")\n",
"\n",
"demo.launch()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 646
},
"id": "ebFtlmmmpCQO",
"outputId": "a064cd8e-dca3-4eda-d95e-bc25fac28d23"
},
"execution_count": null,
"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://3800d81e3201b8d734.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://3800d81e3201b8d734.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": 57
}
]
}
]
} |