#-*- 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("[![Stargazers repo roster for @PaddlePaddle/PaddleNLP](https://reporoster.com/stars/PaddlePaddle/PaddleNLP)](https://github.com/PaddlePaddle/PaddleNLP)")
    gr.HTML(read_content("footer.html"))


if __name__ == "__main__":
    demo.launch(enable_queue=False)