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| #-*- coding: UTF-8 -*- | |
| # Copyright 2022 The Impira Team and the HuggingFace Team. | |
| # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import os | |
| import json | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image | |
| import traceback | |
| import requests | |
| import numpy as np | |
| import gradio as gr | |
| import cv2 | |
| from paddlenlp.utils.doc_parser import DocParser | |
| doc_parser = DocParser() | |
| examples = [ | |
| [ | |
| "business_card.png", | |
| "Name;Title;Web Link;Email;Address", | |
| ], | |
| [ | |
| "license.jpeg", | |
| "Name;DOB;ISS;EXP", | |
| ], | |
| [ | |
| "invoice.jpeg", | |
| "名称;纳税人识别号;开票日期", | |
| ], | |
| [ | |
| "custom.jpeg", | |
| "收发货人;进口口岸;进口日期;运输方式;征免性质;境内目的地;运输工具名称;包装种类;件数;合同协议号" | |
| ], | |
| [ | |
| "resume.png", | |
| "职位;年龄;学校|时间;学校|专业", | |
| ], | |
| ] | |
| example_files = { | |
| "Name;Title;Web Link;Email;Address": "business_card.png", | |
| "Name;DOB;ISS;EXP": "license.jpeg", | |
| "职位;年龄;学校|时间;学校|专业": "resume.png", | |
| "收发货人;进口口岸;进口日期;运输方式;征免性质;境内目的地;运输工具名称;包装种类;件数;合同协议号": "custom.jpeg", | |
| "名称;纳税人识别号;开票日期": "invoice.jpeg", | |
| } | |
| lang_map = { | |
| "resume.png": "ch-no", | |
| "custom.jpeg": "ch-no", | |
| "business_card.png": "en-no", | |
| "invoice.jpeg": "ch-no", | |
| "license.jpeg": "en-no", | |
| } | |
| def dbc2sbc(s): | |
| rs = "" | |
| for char in s: | |
| code = ord(char) | |
| if code == 0x3000: | |
| code = 0x0020 | |
| else: | |
| code -= 0xfee0 | |
| if not (0x0021 <= code and code <= 0x7e): | |
| rs += char | |
| continue | |
| rs += chr(code) | |
| return rs | |
| def process_path(path): | |
| error = None | |
| if path: | |
| try: | |
| images_list = [doc_parser.read_image(path)] | |
| return ( | |
| path, | |
| gr.update(visible=True, value=images_list), | |
| gr.update(visible=True), | |
| gr.update(visible=False, value=None), | |
| gr.update(visible=False, value=None), | |
| None, | |
| ) | |
| except Exception as e: | |
| traceback.print_exc() | |
| error = str(e) | |
| return ( | |
| None, | |
| gr.update(visible=False, value=None), | |
| gr.update(visible=False), | |
| gr.update(visible=False, value=None), | |
| gr.update(visible=False, value=None), | |
| gr.update(visible=True, value=error) if error is not None else None, | |
| 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), | |
| gr.update(visible=False, value=None), | |
| gr.update(visible=False, value=None), | |
| None, | |
| ) | |
| def BGR2RGB(img): | |
| pilimg = img.copy() | |
| pilimg[:, :, 0] = img[:, :, 2] | |
| pilimg[:, :, 2] = img[:, :, 0] | |
| return pilimg | |
| def np2base64(image_np): | |
| image_np = BGR2RGB(image_np) | |
| image = cv2.imencode('.jpg', image_np)[1] | |
| base64_str = str(base64.b64encode(image))[2:-1] | |
| return base64_str | |
| def process_doc(document, schema, ocr_lang, layout_analysis): | |
| if not schema: | |
| schema = '时间;组织机构;人物' | |
| if document is None: | |
| return None, None | |
| option = ocr_lang + "-" + layout_analysis | |
| schema = dbc2sbc(schema) | |
| access_token = os.environ['token'] | |
| url = f"https://aip.baidubce.com/rpc/2.0/nlp-itec/poc/ie?access_token={access_token}" | |
| base64_str = np2base64(doc_parser.read_image(document)) | |
| r = requests.post(url, json={"doc": base64_str, "schema": schema, "option": option}) | |
| response = r.json() | |
| print(response) | |
| predictions = response['result'] | |
| img_show = doc_parser.write_image_with_results( | |
| base64_str, | |
| result=predictions, | |
| max_size=2000, | |
| return_image=True) | |
| img_list = [img_show] | |
| return ( | |
| gr.update(visible=True, value=img_list), | |
| gr.update(visible=True, value=predictions), | |
| ) | |
| def load_example_document(img, schema, ocr_lang, layout_analysis): | |
| if img is not None: | |
| document = example_files[schema] | |
| ocr_lang, layout_analysis = lang_map[document].split("-") | |
| preview, answer = process_doc(document, schema, ocr_lang, layout_analysis) | |
| return document, schema, preview, gr.update(visible=True), answer | |
| else: | |
| return None, None, None, gr.update(visible=False), None | |
| def read_content(file_path: str) -> str: | |
| """read the content of target file | |
| """ | |
| with open(file_path, 'r', encoding='utf-8') as f: | |
| content = f.read() | |
| return content | |
| CSS = """ | |
| #prompt input { | |
| font-size: 16px; | |
| } | |
| #url-textbox { | |
| padding: 0 !important; | |
| } | |
| #short-upload-box .w-full { | |
| min-height: 10rem !important; | |
| } | |
| /* I think something like this can be used to re-shape | |
| * the table | |
| */ | |
| /* | |
| .gr-samples-table tr { | |
| display: inline; | |
| } | |
| .gr-samples-table .p-2 { | |
| width: 100px; | |
| } | |
| */ | |
| #select-a-file { | |
| width: 100%; | |
| } | |
| #file-clear { | |
| padding-top: 2px !important; | |
| padding-bottom: 2px !important; | |
| padding-left: 8px !important; | |
| padding-right: 8px !important; | |
| margin-top: 10px; | |
| } | |
| .gradio-container .gr-button-primary { | |
| background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%); | |
| border: 1px solid #B0DCCC; | |
| border-radius: 8px; | |
| color: #1B8700; | |
| } | |
| .gradio-container.dark button#submit-button { | |
| background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%); | |
| border: 1px solid #B0DCCC; | |
| border-radius: 8px; | |
| color: #1B8700 | |
| } | |
| table.gr-samples-table tr td { | |
| border: none; | |
| outline: none; | |
| } | |
| table.gr-samples-table tr td:first-of-type { | |
| width: 0%; | |
| } | |
| div#short-upload-box div.absolute { | |
| display: none !important; | |
| } | |
| gradio-app > div > div > div > div.w-full > div, .gradio-app > div > div > div > div.w-full > div { | |
| gap: 0px 2%; | |
| } | |
| gradio-app div div div div.w-full, .gradio-app div div div div.w-full { | |
| gap: 0px; | |
| } | |
| gradio-app h2, .gradio-app h2 { | |
| padding-top: 10px; | |
| } | |
| #answer { | |
| overflow-y: scroll; | |
| color: white; | |
| background: #666; | |
| border-color: #666; | |
| font-size: 20px; | |
| font-weight: bold; | |
| } | |
| #answer span { | |
| color: white; | |
| } | |
| #answer textarea { | |
| color:white; | |
| background: #777; | |
| border-color: #777; | |
| font-size: 18px; | |
| } | |
| #url-error input { | |
| color: red; | |
| } | |
| """ | |
| with gr.Blocks(css=CSS) as demo: | |
| gr.HTML(read_content("header.html")) | |
| gr.Markdown( | |
| "**UIE-X 🧾 🎓** is a universal information extraction engine which supports both document and text inputs. It is powered by BAIDU and released on PaddleNLP. " | |
| "Our extraction target(schema) can be set in natural language without limitation, and it also supports most extraction tasks. " | |
| "The model performs well on zero-shot and few-shot settings. Moreover, on PaddleNLP, we provide a comprehensive and easy-to-use fine-tuning customization workflow." | |
| "For more details, please visit the [GitHub](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/information_extraction)" | |
| ) | |
| document = gr.Variable() | |
| is_text = gr.Variable() | |
| example_schema = gr.Textbox(visible=False) | |
| example_image = gr.Image(visible=False) | |
| with gr.Row(equal_height=True): | |
| with gr.Column(): | |
| with gr.Row(): | |
| gr.Markdown("## 1. 选择文件 / Select a file 📄", elem_id="select-a-file") | |
| img_clear_button = gr.Button( | |
| "Clear", variant="secondary", elem_id="file-clear", visible=False | |
| ) | |
| image = gr.Gallery(visible=False) | |
| with gr.Row(equal_height=True): | |
| with gr.Column(): | |
| with gr.Row(): | |
| url = gr.Textbox( | |
| show_label=False, | |
| placeholder="URL", | |
| lines=1, | |
| max_lines=1, | |
| elem_id="url-textbox", | |
| ) | |
| submit = gr.Button("Get") | |
| url_error = gr.Textbox( | |
| visible=False, | |
| elem_id="url-error", | |
| max_lines=1, | |
| interactive=False, | |
| label="Error", | |
| ) | |
| gr.Markdown("— or —") | |
| upload = gr.File(label=None, interactive=True, elem_id="short-upload-box") | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[example_image, example_schema], | |
| ) | |
| with gr.Column(): | |
| gr.Markdown("## 2. 信息抽取 / Information extraction ℹ️ ") | |
| gr.Markdown("### 👉 设置schema") | |
| gr.Markdown("实体抽取：实体类别之间以';'分割，例如 **人物；组织机构**") | |
| gr.Markdown("关系抽取：需配置主体和关系类别，中间以'|'分割，例如 **人物|出生时间；人物|邮箱**") | |
| gr.Markdown("### 👉 Set a schema") | |
| gr.Markdown("Entity extraction: entity label should be separated by ';', e.g. **Person;Organization**") | |
| gr.Markdown("Relation extraction: set the subject and relation type, separated by '|', e.g. **Person|Date;Person|Email**") | |
| gr.Markdown("### 💪 模型定制 / Model customization") | |
| gr.Markdown("我们建议通过[数据标注+微调](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/information_extraction/document)的流程进一步增强模型在特定场景的效果") | |
| gr.Markdown("We recommend to further improve the extraction performance in specific domain through the process of [data annotation & fine-tuning](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/information_extraction/document)") | |
| schema = gr.Textbox( | |
| label="Schema", | |
| placeholder="e.g. Name|Company;Name|Position;Email;Phone Number", | |
| lines=1, | |
| max_lines=1, | |
| ) | |
| ocr_lang = gr.Radio( | |
| choices=["ch", "en"], | |
| value="en", | |
| label="OCR语言 / OCR Language (Please choose ch for Chinese images.)", | |
| ) | |
| layout_analysis = gr.Radio( | |
| choices=["yes", "no"], | |
| value="no", | |
| label="版面分析 / Layout analysis (Better extraction for multi-line text)", | |
| ) | |
| with gr.Row(): | |
| clear_button = gr.Button("Clear", variant="secondary") | |
| submit_button = gr.Button( | |
| "Submit", variant="primary", elem_id="submit-button" | |
| ) | |
| with gr.Column(): | |
| output = gr.JSON(label="Output", visible=False) | |
| for cb in [img_clear_button, clear_button]: | |
| cb.click( | |
| lambda _: ( | |
| gr.update(visible=False, value=None), | |
| None, | |
| gr.update(visible=False, value=None), | |
| gr.update(visible=False), | |
| None, | |
| None, | |
| None, | |
| gr.update(visible=False, value=None), | |
| None, | |
| ), | |
| inputs=clear_button, | |
| outputs=[ | |
| image, | |
| document, | |
| output, | |
| img_clear_button, | |
| example_image, | |
| upload, | |
| url, | |
| url_error, | |
| schema, | |
| ], | |
| ) | |
| upload.change( | |
| fn=process_upload, | |
| inputs=[upload], | |
| outputs=[document, image, img_clear_button, output, url_error], | |
| ) | |
| submit.click( | |
| fn=process_path, | |
| inputs=[url], | |
| outputs=[document, image, img_clear_button, output, url_error], | |
| ) | |
| schema.submit( | |
| fn=process_doc, | |
| inputs=[document, schema, ocr_lang, layout_analysis], | |
| outputs=[image, output], | |
| ) | |
| submit_button.click( | |
| fn=process_doc, | |
| inputs=[document, schema, ocr_lang, layout_analysis], | |
| outputs=[image, output], | |
| ) | |
| example_image.change( | |
| fn=load_example_document, | |
| inputs=[example_image, example_schema, ocr_lang, layout_analysis], | |
| outputs=[document, schema, image, img_clear_button, output], | |
| ) | |
| gr.Markdown("[](https://github.com/PaddlePaddle/PaddleNLP)") | |
| gr.HTML(read_content("footer.html")) | |
| if __name__ == "__main__": | |
| demo.launch(enable_queue=False) | |