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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install transformers --upgrade"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Custom Handler for Inference Endpoints\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting handler.py\n"
]
}
],
"source": [
"%%writefile handler.py\n",
"from typing import Dict, List, Any\n",
"from transformers import DonutProcessor, VisionEncoderDecoderModel\n",
"import torch\n",
"\n",
"\n",
"# check for GPU\n",
"device = 0 if torch.cuda.is_available() else -1\n",
"\n",
"\n",
"class EndpointHandler:\n",
" def __init__(self, path=\"\"):\n",
" # load the model\n",
" self.processor = DonutProcessor.from_pretrained(path)\n",
" self.model = VisionEncoderDecoderModel.from_pretrained(path)\n",
" # move model to device\n",
" self.model.to(device)\n",
" self.decoder_input_ids = self.processor.tokenizer(\n",
" \"<s_cord-v2>\", add_special_tokens=False, return_tensors=\"pt\"\n",
" ).input_ids\n",
"\n",
" def __call__(self, data: Any) -> List[List[Dict[str, float]]]:\n",
"\n",
" inputs = data.pop(\"inputs\", data)\n",
"\n",
"\n",
" # preprocess the input\n",
" pixel_values = self.processor(inputs, return_tensors=\"pt\").pixel_values\n",
"\n",
" # forward pass\n",
" outputs = self.model.generate(\n",
" pixel_values.to(device),\n",
" decoder_input_ids=self.decoder_input_ids.to(device),\n",
" max_length=self.model.decoder.config.max_position_embeddings,\n",
" early_stopping=True,\n",
" pad_token_id=self.processor.tokenizer.pad_token_id,\n",
" eos_token_id=self.processor.tokenizer.eos_token_id,\n",
" use_cache=True,\n",
" num_beams=1,\n",
" bad_words_ids=[[self.processor.tokenizer.unk_token_id]],\n",
" return_dict_in_generate=True,\n",
" )\n",
" # process output\n",
" prediction = self.processor.batch_decode(outputs.sequences)[0]\n",
" prediction = self.processor.token2json(prediction)\n",
"\n",
" return prediction\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"test custom pipeline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from handler import EndpointHandler\n",
"\n",
"my_handler = EndpointHandler(\".\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'menu': [{'nm': '0571-1854 BLUS WANITA',\n",
" 'unitprice': '@120.000',\n",
" 'cnt': '1',\n",
" 'price': '120,000'},\n",
" {'nm': '1002-0060 SHOPPING BAG', 'cnt': '1', 'price': '0'}],\n",
" 'total': {'total_price': '120,000',\n",
" 'changeprice': '0',\n",
" 'creditcardprice': '120,000',\n",
" 'menuqty_cnt': '1'}}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from PIL import Image\n",
"\n",
"payload = {\"inputs\": Image.open(\"sample.png\").convert(\"RGB\")}\n",
"\n",
"my_handler(payload)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.13 ('dev': conda)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "f6dd96c16031089903d5a31ec148b80aeb0d39c32affb1a1080393235fbfa2fc"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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