app.py

import cv2
import gradio as gr
import numpy as np
import torch
from paddleocr import PaddleOCR
from PIL import Image
from transformers import AutoTokenizer, LayoutLMForQuestionAnswering
from transformers.pipelines.document_question_answering import apply_tesseract

model_tag = "impira/layoutlm-document-qa"
MODEL = LayoutLMForQuestionAnswering.from_pretrained(model_tag).eval()
TOKENIZER = AutoTokenizer.from_pretrained(model_tag)
OCR = PaddleOCR(
    use_angle_cls=True,
    lang="en",
    det_limit_side_len=10_000,
    det_db_score_mode="slow",
    enable_mlkdnn=True,
)


PADDLE_OCR_LABEL = "PaddleOCR (en)"
TESSERACT_LABEL = "Tesseract (HF default)"


def predict(image: Image.Image, question: str, ocr_engine: str):
    image_np = np.array(image)

    if ocr_engine == PADDLE_OCR_LABEL:
        ocr_result = OCR.ocr(image_np)[0]
        words = [x[1][0] for x in ocr_result]
        boxes = np.asarray([x[0] for x in ocr_result])  # (n_boxes, 4, 2)

        for box in boxes:
            cv2.polylines(image_np, [box.reshape(-1, 1, 2).astype(int)], True, (0, 255, 255), 3)

        x1 = boxes[:, :, 0].min(1) * 1000 / image.width
        y1 = boxes[:, :, 1].min(1) * 1000 / image.height
        x2 = boxes[:, :, 0].max(1) * 1000 / image.width
        y2 = boxes[:, :, 1].max(1) * 1000 / image.height

        # (n_boxes, 4) in xyxy format
        boxes = np.stack([x1, y1, x2, y2], axis=1).astype(int)

    elif ocr_engine == TESSERACT_LABEL:
        words, boxes = apply_tesseract(image, None, "")

        for x1, y1, x2, y2 in boxes:
            x1 = int(x1 * image.width / 1000)
            y1 = int(y1 * image.height / 1000)
            x2 = int(x2 * image.width / 1000)
            y2 = int(y2 * image.height / 1000)
            cv2.rectangle(image_np, (x1, y1), (x2, y2), (0, 255, 255), 3)

    else:
        raise ValueError(f"Unsupported ocr_engine={ocr_engine}")

    token_ids = TOKENIZER(question)["input_ids"]
    token_boxes = [[0] * 4] * (len(token_ids) - 1) + [[1000] * 4]
    n_question_tokens = len(token_ids)

    token_ids.append(TOKENIZER.sep_token_id)
    token_boxes.append([1000] * 4)

    for word, box in zip(words, boxes):
        new_ids = TOKENIZER(word, add_special_tokens=False)["input_ids"]
        token_ids.extend(new_ids)
        token_boxes.extend([box] * len(new_ids))

    token_ids.append(TOKENIZER.sep_token_id)
    token_boxes.append([1000] * 4)

    with torch.inference_mode():
        outputs = MODEL(
            input_ids=torch.tensor(token_ids).unsqueeze(0),
            bbox=torch.tensor(token_boxes).unsqueeze(0),
        )

    start_scores = outputs.start_logits.squeeze(0).softmax(-1)[n_question_tokens:]
    end_scores = outputs.end_logits.squeeze(0).softmax(-1)[n_question_tokens:]

    span_scores = start_scores.view(-1, 1) * end_scores.view(1, -1)
    span_scores = torch.triu(span_scores)  # don't allow start < end

    score, indices = span_scores.flatten().max(-1)
    start_idx = n_question_tokens + indices // span_scores.shape[1]
    end_idx = n_question_tokens + indices % span_scores.shape[1]

    answer = TOKENIZER.decode(token_ids[start_idx : end_idx + 1])

    return answer, score, image_np


gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(type="pil"),
        "text",
        gr.Radio([PADDLE_OCR_LABEL, TESSERACT_LABEL]),
    ],
    outputs=[
        gr.Textbox(label="Answer"),
        gr.Number(label="Score"),
        gr.Image(label="OCR results"),
    ],
    examples=[
        ["example_01.jpg", "When did the sample take place?", PADDLE_OCR_LABEL],
        ["example_02.jpg", "What is the ID number?", PADDLE_OCR_LABEL],
    ],
).launch(server_name="0.0.0.0", server_port=7860)