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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Setup & Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!apt install -y tesseract-ocr\n",
"pip install pytesseract"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Create Custom Handler for Inference Endpoints\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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 LayoutLMForTokenClassification, LayoutLMv2Processor\n",
"import torch\n",
"from subprocess import run\n",
"\n",
"# install tesseract-ocr and pytesseract\n",
"run(\"apt install -y tesseract-ocr\", shell=True, check=True)\n",
"run(\"pip install pytesseract\", shell=True, check=True)\n",
"\n",
"# helper function to unnormalize bboxes for drawing onto the image\n",
"def unnormalize_box(bbox, width, height):\n",
" return [\n",
" width * (bbox[0] / 1000),\n",
" height * (bbox[1] / 1000),\n",
" width * (bbox[2] / 1000),\n",
" height * (bbox[3] / 1000),\n",
" ]\n",
"\n",
"\n",
"# set device\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"\n",
"class EndpointHandler:\n",
" def __init__(self, path=\"\"):\n",
" # load model and processor from path\n",
" self.model = LayoutLMForTokenClassification.from_pretrained(\"philschmid/layoutlm-funsd\").to(device)\n",
" self.processor = LayoutLMv2Processor.from_pretrained(\"philschmid/layoutlm-funsd\")\n",
"\n",
" def __call__(self, data: Dict[str, bytes]) -> Dict[str, List[Any]]:\n",
" \"\"\"\n",
" Args:\n",
" data (:obj:):\n",
" includes the deserialized image file as PIL.Image\n",
" \"\"\"\n",
" # process input\n",
" image = data.pop(\"inputs\", data)\n",
"\n",
" # process image\n",
" encoding = self.processor(image, return_tensors=\"pt\")\n",
"\n",
" # run prediction\n",
" with torch.inference_mode():\n",
" outputs = self.model(\n",
" input_ids=encoding.input_ids.to(device),\n",
" bbox=encoding.bbox.to(device),\n",
" attention_mask=encoding.attention_mask.to(device),\n",
" token_type_ids=encoding.token_type_ids.to(device),\n",
" )\n",
" predictions = outputs.logits.softmax(-1)\n",
"\n",
" # post process output\n",
" result = []\n",
" for item, inp_ids, bbox in zip(\n",
" predictions.squeeze(0).cpu(), encoding.input_ids.squeeze(0).cpu(), encoding.bbox.squeeze(0).cpu()\n",
" ):\n",
" label = self.model.config.id2label[int(item.argmax().cpu())]\n",
" if label == \"O\":\n",
" continue\n",
" score = item.max().item()\n",
" text = self.processor.tokenizer.decode(inp_ids)\n",
" bbox = unnormalize_box(bbox.tolist(), image.width, image.height)\n",
" result.append({\"label\": label, \"score\": score, \"text\": text, \"bbox\": bbox})\n",
" return {\"predictions\": result}\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": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
}
],
"source": [
"import base64\n",
"from PIL import Image\n",
"from io import BytesIO\n",
"import json\n",
"\n",
"# read image from disk\n",
"image = Image.open(\"invoice_example.png\")\n",
"request = {\"inputs\":image }\n",
"\n",
"# test the handler\n",
"pred = my_handler(request)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image, ImageDraw, ImageFont\n",
"\n",
"\n",
"def draw_result(image,result):\n",
" label2color = {\n",
" \"B-HEADER\": \"blue\",\n",
" \"B-QUESTION\": \"red\",\n",
" \"B-ANSWER\": \"green\",\n",
" \"I-HEADER\": \"blue\",\n",
" \"I-QUESTION\": \"red\",\n",
" \"I-ANSWER\": \"green\",\n",
" }\n",
"\n",
"\n",
" # draw predictions over the image\n",
" draw = ImageDraw.Draw(image)\n",
" font = ImageFont.load_default()\n",
" for res in result:\n",
" draw.rectangle(res[\"bbox\"], outline=\"black\")\n",
" draw.rectangle(res[\"bbox\"], outline=label2color[res[\"label\"]])\n",
" draw.text((res[\"bbox\"][0] + 10, res[\"bbox\"][1] - 10), text=res[\"label\"], fill=label2color[res[\"label\"]], font=font)\n",
" return image\n",
"\n",
"draw_result(image,pred[\"predictions\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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