MrDoc/app_doc/search/highlight.py

# coding:utf-8
# @文件: highlight.py
# @创建者：州的先生
# #日期：2020/11/24
# 博客地址：zmister.com
from haystack.utils import Highlighter
from django.utils.html import strip_tags
from app_doc.search.chinese_analyzer import ChineseAnalyzer as StemmingAnalyzer


class MyHighLighter(Highlighter):

    def __init__(self, query, **kwargs):

        # self.query = [token.text for token in sa(query)]
        self.query = query

        if "max_length" in kwargs:
            self.max_length = int(kwargs["max_length"])

        if "html_tag" in kwargs:
            self.html_tag = kwargs["html_tag"]

        if "css_class" in kwargs:
            self.css_class = kwargs["css_class"]

        # self.query_words = set(
        #     [word.lower() for word in self.query.split() if not word.startswith("-")]
        # )
        sa = StemmingAnalyzer()
        self.query_words = set(
            [token.text.lower() for token in sa(query) if token.text.replace(" ",'') != '']
        )
        # print(self.query_words)

    def highlight(self, text_block):
        self.text_block = strip_tags(text_block)
        highlight_locations = self.find_highlightable_words()
        start_offset, end_offset = self.find_window(highlight_locations)
        if len(text_block) < self.max_length:
            start_offset = 0
        return self.render_html(highlight_locations, start_offset, end_offset)

    def find_window(self, highlight_locations):
        best_start = 0
        best_end = self.max_length

        # First, make sure we have words.
        if not len(highlight_locations):
            return (best_start, best_end)

        words_found = []

        # Next, make sure we found any words at all.
        for word, offset_list in highlight_locations.items():
            if len(offset_list):
                # Add all of the locations to the list.
                words_found.extend(offset_list)

        if not len(words_found):
            return (best_start, best_end)

        # 只有一个搜索词被发现
        if len(words_found) == 1:
            # return (words_found[0], words_found[0] + self.max_length)  #
            # new added
            best_start = words_found[0]
            best_end = words_found[0] + self.max_length

            if best_end > len(self.text_block):
                current_length = len(self.text_block) - best_start
                move_forward_steps = self.max_length - current_length
                best_start = best_start - move_forward_steps
                if best_start < 0:
                    best_start = 0
            return (best_start, best_end)

        # Sort the list so it's in ascending order.
        words_found = sorted(words_found)

        # We now have a denormalized list of all positions were a word was
        # found. We'll iterate through and find the densest window we can by
        # counting the number of found offsets (-1 to fit in the window).
        highest_density = 0

        if words_found[:-1][0] > self.max_length:
            best_start = words_found[:-1][0]
            best_end = best_start + self.max_length

        for count, start in enumerate(words_found[:-1]):
            current_density = 1

            for end in words_found[count + 1:]:
                if end - start < self.max_length:
                    current_density += 1
                else:
                    current_density = 0

                # Only replace if we have a bigger (not equal density) so we
                # give deference to windows earlier in the document.
                if current_density > highest_density:
                    # print(start)
                    if start == 0:
                        best_start = start
                    else:
                        best_start = start
                    best_end = start + self.max_length
                    highest_density = current_density
        if best_start < 10:
            best_start = 0
            best_end = 200
        else:
            best_start -= 10
            best_end -= 10

        return (best_start, best_end)

    def render_html(self, highlight_locations=None, start_offset=None, end_offset=None):
        # Start by chopping the block down to the proper window.
        text = self.text_block[start_offset:end_offset]

        # Invert highlight_locations to a location -> term list
        term_list = []

        for term, locations in highlight_locations.items():
            term_list += [(loc - start_offset, term) for loc in locations]

        loc_to_term = sorted(term_list)

        # Prepare the highlight template
        if self.css_class:
            hl_start = '<%s class="%s">' % (self.html_tag, self.css_class)
        else:
            hl_start = "<%s>" % (self.html_tag)

        hl_end = "</%s>" % self.html_tag

        # Copy the part from the start of the string to the first match,
        # and there replace the match with a highlighted version.
        highlighted_chunk = ""
        matched_so_far = 0
        prev = 0
        prev_str = ""

        for cur, cur_str in loc_to_term:
            # This can be in a different case than cur_str
            actual_term = text[cur : cur + len(cur_str)]

            # Handle incorrect highlight_locations by first checking for the term
            if actual_term.lower() == cur_str:
                if cur < prev + len(prev_str):
                    continue

                highlighted_chunk += (
                    text[prev + len(prev_str) : cur] + hl_start + actual_term + hl_end
                )
                prev = cur
                prev_str = cur_str

                # Keep track of how far we've copied so far, for the last step
                matched_so_far = cur + len(actual_term)

        # Don't forget the chunk after the last term
        highlighted_chunk += text[matched_so_far:]

        if start_offset > 0:
            highlighted_chunk = "%s" % highlighted_chunk

        if end_offset < len(self.text_block):
            highlighted_chunk = "%s..." % highlighted_chunk

        return highlighted_chunk