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| import os | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| import functools | |
| from PIL import Image, ImageDraw | |
| import gradio as gr | |
| import torch | |
| from docquery.pipeline import get_pipeline | |
| from docquery.document import load_bytes, load_document, ImageDocument | |
| def ensure_list(x): | |
| if isinstance(x, list): | |
| return x | |
| else: | |
| return [x] | |
| CHECKPOINTS = { | |
| "LayoutLMv1 🦉": "impira/layoutlm-document-qa", | |
| "Donut 🍩": "naver-clova-ix/donut-base-finetuned-docvqa", | |
| } | |
| PIPELINES = {} | |
| def construct_pipeline(model): | |
| global PIPELINES | |
| if model in PIPELINES: | |
| return PIPELINES[model] | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| ret = get_pipeline(checkpoint=CHECKPOINTS[model], device=device) | |
| PIPELINES[model] = ret | |
| return ret | |
| def run_pipeline(model, question, document, top_k): | |
| pipeline = construct_pipeline(model) | |
| return pipeline(question=question, **document.context, top_k=top_k) | |
| # TODO: Move into docquery | |
| # TODO: Support words past the first page (or window?) | |
| def lift_word_boxes(document): | |
| return document.context["image"][0][1] | |
| def expand_bbox(word_boxes): | |
| if len(word_boxes) == 0: | |
| return None | |
| min_x, min_y, max_x, max_y = zip(*[x[1] for x in word_boxes]) | |
| min_x, min_y, max_x, max_y = [min(min_x), min(min_y), max(max_x), max(max_y)] | |
| return [min_x, min_y, max_x, max_y] | |
| # LayoutLM boxes are normalized to 0, 1000 | |
| def normalize_bbox(box, width, height, padding=0.005): | |
| min_x, min_y, max_x, max_y = [c / 1000 for c in box] | |
| if padding != 0: | |
| min_x = max(0, min_x - padding) | |
| min_y = max(0, min_y - padding) | |
| max_x = min(max_x + padding, 1) | |
| max_y = min(max_y + padding, 1) | |
| return [min_x * width, min_y * height, max_x * width, max_y * height] | |
| examples = [ | |
| [ | |
| "invoice.png", | |
| "What is the invoice number?", | |
| ], | |
| [ | |
| "contract.jpeg", | |
| "What is the purchase amount?", | |
| ], | |
| [ | |
| "statement.png", | |
| "What are net sales for 2020?", | |
| ], | |
| ] | |
| def process_path(path): | |
| if path: | |
| try: | |
| document = load_document(path) | |
| return ( | |
| document, | |
| gr.update(visible=True, value=document.preview), | |
| gr.update(visible=False, value=None), | |
| ) | |
| except Exception: | |
| pass | |
| return ( | |
| None, | |
| gr.update(visible=False, value=None), | |
| gr.update(visible=False, value=None), | |
| ) | |
| def process_upload(file): | |
| if file: | |
| return process_path(file.name) | |
| else: | |
| return ( | |
| None, | |
| gr.update(visible=False, value=None), | |
| gr.update(visible=False, value=None), | |
| ) | |
| colors = ["#64A087", "green", "black"] | |
| def process_question(question, document, model=list(CHECKPOINTS.keys())[0]): | |
| if document is None: | |
| return None, None | |
| predictions = run_pipeline(model, question, document, 3) | |
| image = document.preview.copy() | |
| draw = ImageDraw.Draw(image, "RGBA") | |
| for i, p in enumerate(ensure_list(predictions)): | |
| if i > 0: | |
| # Keep the code around to produce multiple boxes, but only show the top | |
| # prediction for now | |
| break | |
| if "start" in p and "end" in p and p.get("page") == 0: | |
| x1, y1, x2, y2 = normalize_bbox( | |
| expand_bbox(lift_word_boxes(document)[p["start"] : p["end"] + 1]), | |
| image.width, | |
| image.height, | |
| ) | |
| draw.rectangle(((x1, y1), (x2, y2)), fill=(0, 255, 0, int(0.4 * 255))) | |
| return gr.update(visible=True, value=image), gr.update( | |
| visible=True, value=predictions | |
| ) | |
| def load_example_document(img, question, model): | |
| document = ImageDocument(Image.fromarray(img)) | |
| preview, answer = process_question(question, document, model) | |
| return document, question, preview, answer | |
| CSS = """ | |
| #short-upload-box .w-full { | |
| min-height: 10rem !important; | |
| } | |
| #question input { | |
| font-size: 16px; | |
| } | |
| """ | |
| with gr.Blocks(css=CSS) as demo: | |
| gr.Markdown("# DocQuery: Query Documents w/ NLP") | |
| document = gr.Variable() | |
| example_question = gr.Textbox(visible=False) | |
| example_image = gr.Image(visible=False) | |
| gr.Markdown("## 1. Upload a file or select an example") | |
| with gr.Row(equal_height=True): | |
| with gr.Column(): | |
| upload = gr.File( | |
| label="Upload a file", interactive=True, elem_id="short-upload-box" | |
| ) | |
| url = gr.Textbox(label="... or a URL", interactive=True) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[example_image, example_question], | |
| ) | |
| gr.Markdown("## 2. Ask a question") | |
| with gr.Row(equal_height=True): | |
| question = gr.Textbox( | |
| label="Question", | |
| placeholder="e.g. What is the invoice number?", | |
| lines=1, | |
| max_lines=1, | |
| elem_id="question", | |
| ) | |
| model = gr.Radio( | |
| choices=list(CHECKPOINTS.keys()), | |
| value=list(CHECKPOINTS.keys())[0], | |
| label="Model", | |
| ) | |
| with gr.Row(): | |
| clear_button = gr.Button("Clear", variant="secondary") | |
| submit_button = gr.Button("Submit", variant="primary", elem_id="submit-button") | |
| with gr.Row(): | |
| image = gr.Image(visible=False) | |
| with gr.Column(): | |
| output = gr.JSON(label="Output", visible=False) | |
| clear_button.click( | |
| lambda _: ( | |
| gr.update(visible=False, value=None), | |
| None, | |
| None, | |
| gr.update(visible=False, value=None), | |
| ), | |
| inputs=clear_button, | |
| outputs=[image, document, question, output], | |
| ) | |
| upload.change(fn=process_upload, inputs=[upload], outputs=[document, image, output]) | |
| url.change(fn=process_path, inputs=[url], outputs=[document, image, output]) | |
| question.submit( | |
| fn=process_question, | |
| inputs=[question, document, model], | |
| outputs=[image, output], | |
| ) | |
| submit_button.click( | |
| process_question, | |
| inputs=[question, document, model], | |
| outputs=[image, output], | |
| ) | |
| model.change( | |
| process_question, inputs=[question, document, model], outputs=[image, output] | |
| ) | |
| example_image.change( | |
| fn=load_example_document, | |
| inputs=[example_image, example_question, model], | |
| outputs=[document, question, image, output], | |
| ) | |
| gr.Markdown("### More Info") | |
| gr.Markdown( | |
| "DocQuery uses LayoutLMv1 fine-tuned on DocVQA, a document visual question" | |
| " answering dataset, as well as SQuAD, which boosts its English-language comprehension." | |
| " To use it, simply upload an image or PDF, type a question, and click 'submit', or " | |
| " click one of the examples to load them." | |
| ) | |
| gr.Markdown("[Github Repo](https://github.com/impira/docquery)") | |
| if __name__ == "__main__": | |
| demo.launch() | |