# coding: utf-8

# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------

"""
FILE: sample_analyze_read.py

DESCRIPTION:
    This sample demonstrates how to extract document information using "prebuilt-read"
    to analyze a given file.

USAGE:
    python sample_analyze_read.py

    Set the environment variables with your own values before running the sample:
    1) DOCUMENTINTELLIGENCE_ENDPOINT - the endpoint to your Document Intelligence resource.
    2) DOCUMENTINTELLIGENCE_API_KEY - your Document Intelligence API key.
"""

import os


def get_words(page, line):
    result = []
    for word in page.words:
        if _in_span(word, line.spans):
            result.append(word)
    return result


def _in_span(word, spans):
    for span in spans:
        if word.span.offset >= span.offset and (word.span.offset + word.span.length) <= (span.offset + span.length):
            return True
    return False


def format_bounding_region(bounding_regions):
    if not bounding_regions:
        return "N/A"
    return ", ".join(f"Page #{region.page_number}: {format_polygon(region.polygon)}" for region in bounding_regions)


def format_polygon(polygon):
    if not polygon:
        return "N/A"
    return ", ".join([f"[{polygon[i]}, {polygon[i + 1]}]" for i in range(0, len(polygon), 2)])


def analyze_read():
    path_to_sample_documents = os.path.abspath(
        os.path.join(
            os.path.abspath(__file__),
            "..",
            "./sample_forms/forms/Form_1.jpg",
        )
    )

    from azure.core.credentials import AzureKeyCredential
    from azure.ai.documentintelligence import DocumentIntelligenceClient
    from azure.ai.documentintelligence.models import DocumentAnalysisFeature, AnalyzeResult

    endpoint = os.environ["DOCUMENTINTELLIGENCE_ENDPOINT"]
    key = os.environ["DOCUMENTINTELLIGENCE_API_KEY"]

    document_intelligence_client = DocumentIntelligenceClient(endpoint=endpoint, credential=AzureKeyCredential(key))
    with open(path_to_sample_documents, "rb") as f:
        poller = document_intelligence_client.begin_analyze_document(
            "prebuilt-read",
            body=f,
            features=[DocumentAnalysisFeature.STYLE_FONT],
        )
    result: AnalyzeResult = poller.result()

    print("----Languages detected in the document----")
    if result.languages is not None:
        for language in result.languages:
            print(f"Language code: '{language.locale}' with confidence {language.confidence}")

    print("----Styles detected in the document----")
    if result.styles:
        for style in result.styles:
            if style.is_handwritten:
                print("Found the following handwritten content: ")
                print(",".join([result.content[span.offset : span.offset + span.length] for span in style.spans]))
            if style.font_style:
                print(f"The document contains '{style.font_style}' font style, applied to the following text: ")
                print(",".join([result.content[span.offset : span.offset + span.length] for span in style.spans]))

    for page in result.pages:
        print(f"----Analyzing document from page #{page.page_number}----")
        print(f"Page has width: {page.width} and height: {page.height}, measured with unit: {page.unit}")

        if page.lines:
            for line_idx, line in enumerate(page.lines):
                words = get_words(page, line)
                print(
                    f"...Line # {line_idx} has {len(words)} words and text '{line.content}' within "
                    f"bounding polygon '{format_polygon(line.polygon)}'"
                )

        if page.words:
            for word in page.words:
                print(f"......Word '{word.content}' has a confidence of {word.confidence}")

        if page.selection_marks:
            for selection_mark in page.selection_marks:
                print(
                    f"...Selection mark is '{selection_mark.state}' within bounding polygon "
                    f"'{format_polygon(selection_mark.polygon)}' and has a confidence of {selection_mark.confidence}"
                )

    if result.paragraphs:
        print(f"----Detected #{len(result.paragraphs)} paragraphs in the document----")
        # Sort all paragraphs by span's offset to read in the right order.
        result.paragraphs.sort(key=lambda p: (p.spans.sort(key=lambda s: s.offset), p.spans[0].offset))
        print("-----Print sorted paragraphs-----")
        for paragraph in result.paragraphs:
            print(
                f"Found paragraph with role: '{paragraph.role}' within "
                f"{format_bounding_region(paragraph.bounding_regions)} bounding regions"
            )
            print(f"...with content: '{paragraph.content}'")
            print(f"...with offset: {paragraph.spans[0].offset} and length: {paragraph.spans[0].length}")

    print("----------------------------------------")


if __name__ == "__main__":
    from azure.core.exceptions import HttpResponseError
    from dotenv import find_dotenv, load_dotenv

    try:
        load_dotenv(find_dotenv())
        analyze_read()
    except HttpResponseError as error:
        # Examples of how to check an HttpResponseError
        # Check by error code:
        if error.error is not None:
            if error.error.code == "InvalidImage":
                print(f"Received an invalid image error: {error.error}")
            if error.error.code == "InvalidRequest":
                print(f"Received an invalid request error: {error.error}")
            # Raise the error again after printing it
            raise
        # If the inner error is None and then it is possible to check the message to get more information:
        if "Invalid request".casefold() in error.message.casefold():
            print(f"Uh-oh! Seems there was an invalid request: {error}")
        # Raise the error again
        raise