{
 "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": []
  }
 ],
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