.ipynb_checkpoints/handler-checkpoint.py

from typing import Dict, List, Any
from transformers import LayoutLMForTokenClassification, LayoutLMv2Processor
import torch
from subprocess import run

# install tesseract-ocr and pytesseract
# run("apt install -y tesseract-ocr", shell=True, check=True)
run("pip install pytesseract", shell=True, check=True)

# helper function to unnormalize bboxes for drawing onto the image
def unnormalize_box(bbox, width, height):
    return [
        width * (bbox[0] / 1000),
        height * (bbox[1] / 1000),
        width * (bbox[2] / 1000),
        height * (bbox[3] / 1000),
    ]

def predict(Image, processor, model):
    """Process document and prepare the data for LayoutLM inference

    Args:
        urls (List[str]): Batch of pre-signed document urls
    Returns:
        (List[List[Dict]]): Features extraction
    """

    
#     images = [get_image_from_url(url) for url in urls]
    encoding = processor(
            images = Image, 
            return_tensors="pt", 
            padding="max_length", 
            truncation=True,
        )
    del encoding["image"] # LayoutLM doesn't require the image
    outputs = model(**encoding)
    results = process_outputs(
        outputs, encoding=encoding, 
        images=Image, model=model, 
        processor=processor,
        threshold = 0.75
    )
    return results, encoding
def get_uniqueLabelList(labels):
    uqnieue_labels =[]
    for label in labels[0]:
        try:
            label_short = label.split("-")[1]
            if label_short not in uqnieue_labels:
                uqnieue_labels.append(label_short)
        except:      
            if label not in uqnieue_labels:
                uqnieue_labels.append(label)
            else:
                pass
    return uqnieue_labels

def process_outputs(outputs, encoding, images, model, processor, threshold):
    scores, _ = torch.max(outputs.logits.softmax(axis=-1), dim=-1)
    scores = scores.tolist()
    predictions = outputs.logits.argmax(-1)
    labels = [[model.config.id2label[pred.item()] for pred in prediction] for prediction in predictions]
    results = _process_outputs(
        encoding=encoding,
        tokenizer=processor.tokenizer,
        processor = processor,
        labels=labels,
        scores=scores,
        images=images,
        threshold = threshold
    )
    return results

def _process_outputs(encoding, tokenizer, labels, scores, images, processor, threshold):
    results = []
    
    width, height = images.size
    entities = []
    previous_word_idx = 0
    unique_lables = get_uniqueLabelList(labels)
#     tokens = tokenizer.convert_ids_to_tokens(input_ids)
#     word_ids = encoding.word_ids(batch_index=batch_idx)
#     word = ""
    entite_wordsidx = []
    for idx, label in enumerate(unique_lables):
        score_sum = float(0)
        if label != "O":
            for ix, pred in enumerate(labels[0]):
                if scores[0][ix] > threshold:
                    if label in pred:
                        score_sum += scores[0][ix]
                        entite_wordsidx.append(ix)
#     
                
                try:
                    score_mean = f'{score_sum/len(entite_wordsidx):.2f}'
                except:
                    score_mean = 0.0 
            # entite_wordsidx.append(entite_wordsidx[-1] + 1)
            entities.append(
                {
                    "word": processor.decode(encoding.input_ids[0][entite_wordsidx]),
                    "label": unique_lables[idx],                       
                    "score": score_mean
                    ,
                }
            )  
            
            entite_wordsidx = []
                
      
    results.append(entities)
    return results

def unnormalize_box(bbox, width, height):
    return [
        int(width * (bbox[0] / 1000)),
        int(height * (bbox[1] / 1000)),
        int(width * (bbox[2] / 1000)),
        int(height * (bbox[3] / 1000)),
    ]
def get_image_from_url(Image):
    return Image.open(f).convert("RGB") # LayoutLMv2Processor requires RGB format
# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class EndpointHandler:
    def __init__(self, path=""):
        # load model and processor from path
        self.model = LayoutLMForTokenClassification.from_pretrained(path).to(device)
        self.processor = LayoutLMv2Processor.from_pretrained(path)

    def __call__(self, data: Dict[str, bytes]) -> Dict[str, List[Any]]:
        """
        Args:
            data (:obj:):
                includes the deserialized image file as PIL.Image
        """
        # process input
        image = data.pop("inputs", data)
        print(image.filename)

        result, encod = predict(image, self.processor, self.model)
        return {"predictions": result}