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import os |
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os.environ["TOKENIZERS_PARALLELISM"] = "false" |
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from PIL import Image, ImageDraw |
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import traceback |
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import gradio as gr |
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from gradio import processing_utils |
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import torch |
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from docquery import pipeline |
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from docquery.document import load_bytes, load_document, ImageDocument |
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from docquery.ocr_reader import get_ocr_reader |
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def ensure_list(x): |
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if isinstance(x, list): |
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return x |
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else: |
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return [x] |
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CHECKPOINTS = { |
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"LayoutLMv1 for Invoices 🧾": "impira/layoutlm-invoices", |
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} |
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PIPELINES = {} |
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def construct_pipeline(task, model): |
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global PIPELINES |
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if model in PIPELINES: |
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return PIPELINES[model] |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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ret = pipeline(task=task, model=CHECKPOINTS[model], device=device) |
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PIPELINES[model] = ret |
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return ret |
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def run_pipeline(model, question, document, top_k): |
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pipeline = construct_pipeline("document-question-answering", model) |
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return pipeline(question=question, **document.context, top_k=top_k) |
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def lift_word_boxes(document, page): |
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return document.context["image"][page][1] |
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def expand_bbox(word_boxes): |
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if len(word_boxes) == 0: |
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return None |
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min_x, min_y, max_x, max_y = zip(*[x[1] for x in word_boxes]) |
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min_x, min_y, max_x, max_y = [min(min_x), min(min_y), max(max_x), max(max_y)] |
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return [min_x, min_y, max_x, max_y] |
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def normalize_bbox(box, width, height, padding=0.005): |
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min_x, min_y, max_x, max_y = [c / 1000 for c in box] |
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if padding != 0: |
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min_x = max(0, min_x - padding) |
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min_y = max(0, min_y - padding) |
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max_x = min(max_x + padding, 1) |
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max_y = min(max_y + padding, 1) |
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return [min_x * width, min_y * height, max_x * width, max_y * height] |
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EXAMPLES = [ |
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[ |
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"acze_tech.png", |
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"Tech Invoice", |
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], |
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[ |
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"acze.png", |
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"Commercial Goods Invoice", |
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], |
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[ |
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"north_sea.png", |
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"Energy Invoice", |
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], |
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] |
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QUESTION_FILES = { |
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"Tech Invoice": "acze_tech.pdf", |
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"Energy Invoice": "north_sea.pdf", |
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} |
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for q in QUESTION_FILES.keys(): |
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assert any(x[1] == q for x in EXAMPLES) |
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FIELDS = { |
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"Vendor Name": ["Vendor Name - Logo?", "Vendor Name - Address?"], |
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"Vendor Address": ["Vendor Address?"], |
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"Customer Name": ["Customer Name?"], |
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"Customer Address": ["Customer Address?"], |
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"Invoice Number": ["Invoice Number?"], |
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"Invoice Date": ["Invoice Date?"], |
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"Due Date": ["Due Date?"], |
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"Subtotal": ["Subtotal?"], |
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"Total Tax": ["Total Tax?"], |
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"Invoice Total": ["Invoice Total?"], |
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"Amount Due": ["Amount Due?"], |
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"Payment Terms": ["Payment Terms?"], |
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"Remit To Name": ["Remit To Name?"], |
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"Remit To Address": ["Remit To Address?"], |
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} |
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def empty_table(fields): |
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return {"value": [[name, None] for name in fields.keys()], "interactive": False} |
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def process_document(document, fields, model, error=None): |
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if document is not None and error is None: |
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preview, json_output, table = process_fields(document, fields, model) |
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return ( |
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document, |
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fields, |
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preview, |
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gr.update(visible=True), |
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gr.update(visible=False, value=None), |
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json_output, |
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table, |
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) |
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else: |
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return ( |
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None, |
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fields, |
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None, |
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gr.update(visible=False), |
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gr.update(visible=True, value=error) if error is not None else None, |
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None, |
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gr.update(**empty_table(fields)), |
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) |
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def process_path(path, fields, model): |
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error = None |
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document = None |
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if path: |
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try: |
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document = load_document(path) |
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except Exception as e: |
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traceback.print_exc() |
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error = str(e) |
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return process_document(document, fields, model, error) |
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def process_upload(file, fields, model): |
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return process_path(file.name if file else None, fields, model) |
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colors = ["#64A087", "green", "black"] |
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def annotate_page(prediction, pages, document): |
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if prediction is not None and "word_ids" in prediction: |
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image = pages[prediction["page"]] |
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draw = ImageDraw.Draw(image, "RGBA") |
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word_boxes = lift_word_boxes(document, prediction["page"]) |
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x1, y1, x2, y2 = normalize_bbox( |
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expand_bbox([word_boxes[i] for i in prediction["word_ids"]]), |
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image.width, |
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image.height, |
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) |
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draw.rectangle(((x1, y1), (x2, y2)), fill=(0, 255, 0, int(0.4 * 255))) |
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def process_question( |
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question, document, img_gallery, model, fields, output, output_table |
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): |
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field_name = question |
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if field_name is not None: |
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fields = {field_name: [question], **fields} |
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if not question or document is None: |
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return None, document, fields, output, gr.update(value=output_table) |
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text_value = None |
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pages = [processing_utils.decode_base64_to_image(p) for p in img_gallery] |
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prediction = run_pipeline(model, question, document, 1) |
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annotate_page(prediction, pages, document) |
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output = {field_name: prediction, **output} |
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table = [[field_name, prediction.get("answer")]] + output_table.values.tolist() |
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return ( |
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None, |
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gr.update(visible=True, value=pages), |
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fields, |
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output, |
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gr.update(value=table, interactive=False), |
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) |
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def process_fields(document, fields, model=list(CHECKPOINTS.keys())[0]): |
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pages = [x.copy().convert("RGB") for x in document.preview] |
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ret = {} |
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table = [] |
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for (field_name, questions) in fields.items(): |
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answers = [ |
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a |
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for q in questions |
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for a in ensure_list(run_pipeline(model, q, document, top_k=1)) |
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if a.get("score", 1) > 0.5 |
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] |
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answers.sort(key=lambda x: -x.get("score", 0) if x else 0) |
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top = answers[0] if len(answers) > 0 else None |
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annotate_page(top, pages, document) |
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ret[field_name] = top |
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table.append([field_name, top.get("answer") if top is not None else None]) |
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return ( |
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gr.update(visible=True, value=pages), |
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gr.update(visible=True, value=ret), |
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table |
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) |
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def load_example_document(img, title, fields, model): |
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document = None |
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if img is not None: |
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if title in QUESTION_FILES: |
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document = load_document(QUESTION_FILES[title]) |
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else: |
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document = ImageDocument(Image.fromarray(img), ocr_reader=get_ocr_reader()) |
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return process_document(document, fields, model) |
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CSS = """ |
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#question input { |
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font-size: 16px; |
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} |
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#url-textbox, #question-textbox { |
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padding: 0 !important; |
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} |
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#short-upload-box .w-full { |
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min-height: 10rem !important; |
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} |
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/* I think something like this can be used to re-shape |
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* the table |
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*/ |
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/* |
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.gr-samples-table tr { |
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display: inline; |
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} |
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.gr-samples-table .p-2 { |
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width: 100px; |
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} |
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*/ |
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#select-a-file { |
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width: 100%; |
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} |
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#file-clear { |
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padding-top: 2px !important; |
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padding-bottom: 2px !important; |
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padding-left: 8px !important; |
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padding-right: 8px !important; |
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margin-top: 10px; |
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} |
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.gradio-container .gr-button-primary { |
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background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%); |
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border: 1px solid #B0DCCC; |
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border-radius: 8px; |
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color: #1B8700; |
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} |
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.gradio-container.dark button#submit-button { |
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background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%); |
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border: 1px solid #B0DCCC; |
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border-radius: 8px; |
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color: #1B8700 |
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} |
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table.gr-samples-table tr td { |
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border: none; |
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outline: none; |
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} |
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table.gr-samples-table tr td:first-of-type { |
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width: 0%; |
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} |
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div#short-upload-box div.absolute { |
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display: none !important; |
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} |
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gradio-app > div > div > div > div.w-full > div, .gradio-app > div > div > div > div.w-full > div { |
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gap: 0px 2%; |
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} |
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gradio-app div div div div.w-full, .gradio-app div div div div.w-full { |
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gap: 0px; |
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} |
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gradio-app h2, .gradio-app h2 { |
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padding-top: 10px; |
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} |
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#answer { |
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overflow-y: scroll; |
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color: white; |
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background: #666; |
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border-color: #666; |
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font-size: 20px; |
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font-weight: bold; |
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} |
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#answer span { |
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color: white; |
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} |
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#answer textarea { |
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color:white; |
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background: #777; |
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border-color: #777; |
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font-size: 18px; |
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} |
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#url-error input { |
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color: red; |
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} |
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#results-table { |
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max-height: 600px; |
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overflow-y: scroll; |
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} |
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""" |
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with gr.Blocks(css=CSS) as demo: |
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gr.Markdown("# Invoice2Table") |
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document = gr.Variable() |
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fields = gr.Variable(value={**FIELDS}) |
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example_question = gr.Textbox(visible=False) |
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example_image = gr.Image(visible=False) |
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with gr.Row(equal_height=True): |
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with gr.Column(): |
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with gr.Row(): |
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gr.Markdown("## Select an invoice", elem_id="select-a-file") |
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img_clear_button = gr.Button( |
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"Clear", variant="secondary", elem_id="file-clear", visible=False |
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) |
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image = gr.Gallery(visible=False) |
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with gr.Row(equal_height=True): |
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with gr.Column(): |
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with gr.Row(): |
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url = gr.Textbox( |
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show_label=False, |
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placeholder="URL", |
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lines=1, |
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max_lines=1, |
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elem_id="url-textbox", |
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) |
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submit = gr.Button("Get") |
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url_error = gr.Textbox( |
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visible=False, |
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elem_id="url-error", |
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max_lines=1, |
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interactive=False, |
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label="Error", |
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) |
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gr.Markdown("— or —") |
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upload = gr.File(label=None, interactive=True, elem_id="short-upload-box") |
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gr.Examples( |
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examples=EXAMPLES, |
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inputs=[example_image, example_question], |
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) |
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with gr.Column() as col: |
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gr.Markdown("## Results") |
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with gr.Tabs(): |
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with gr.TabItem("Table"): |
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output_table = gr.Dataframe( |
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headers=["Field", "Value"], |
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**empty_table(fields.value), |
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elem_id="results-table" |
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) |
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with gr.TabItem("JSON"): |
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output = gr.JSON(label="Output", visible=True) |
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model = gr.Radio( |
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choices=list(CHECKPOINTS.keys()), |
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value=list(CHECKPOINTS.keys())[0], |
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label="Model", |
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visible=False, |
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) |
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gr.Markdown("### Ask a question") |
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with gr.Row(): |
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question = gr.Textbox( |
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label="Question", |
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show_label=False, |
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placeholder="e.g. What is the invoice number?", |
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lines=1, |
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max_lines=1, |
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elem_id="question-textbox", |
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) |
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clear_button = gr.Button("Clear", variant="secondary", visible=False) |
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submit_button = gr.Button( |
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"Add", variant="primary", elem_id="submit-button" |
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) |
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for cb in [img_clear_button, clear_button]: |
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cb.click( |
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lambda _: ( |
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gr.update(visible=False, value=None), |
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None, |
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gr.update(value=None), |
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gr.update(**empty_table(fields.value)), |
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gr.update(visible=False), |
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None, |
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None, |
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None, |
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gr.update(visible=False, value=None), |
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None, |
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), |
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inputs=clear_button, |
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outputs=[ |
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image, |
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document, |
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output, |
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output_table, |
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img_clear_button, |
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example_image, |
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upload, |
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url, |
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url_error, |
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question, |
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], |
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) |
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submit_outputs = [ |
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document, |
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fields, |
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image, |
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img_clear_button, |
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url_error, |
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output, |
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output_table, |
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] |
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upload.change( |
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fn=process_upload, |
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inputs=[upload, fields, model], |
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outputs=submit_outputs, |
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) |
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submit.click( |
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fn=process_path, |
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inputs=[url, fields, model], |
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outputs=submit_outputs, |
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) |
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for action in [question.submit, submit_button.click]: |
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action( |
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fn=process_question, |
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inputs=[question, document, image, model, fields, output, output_table], |
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outputs=[question, image, fields, output, output_table], |
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) |
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example_image.change( |
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fn=load_example_document, |
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inputs=[example_image, example_question, fields, model], |
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outputs=submit_outputs, |
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) |
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if __name__ == "__main__": |
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demo.launch(enable_queue=False) |
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