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 @functools.lru_cache(1024) 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, document.preview, None except Exception: pass return None, None, None def process_upload(file): if file: return process_path(file.name) else: return None, None, 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 image, 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=True) with gr.Column(): output = gr.JSON(label="Output") clear_button.click( lambda _: (None, None, None, 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()