124 lines
4.8 KiB
Python
124 lines
4.8 KiB
Python
import json
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import MeCab
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import re
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import requests
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request_url = "http://127.0.0.1:19633"
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request_timeout = 10
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# Initialize the Tagger
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#tagger = MeCab.Tagger()
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# Generate Hiragana and Katakana characters
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hiragana = tuple(chr(i) for i in range(12353, 12436)) # \u3041 - \u3094
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katakana = tuple(chr(i) for i in range(12450, 12532)) # \u30A2 - \u30F4
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punctuation = tuple(chr(i) for i in range(65281, 65382))
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def elide(text: str) -> str:
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elide_max_length = 100
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if len(text) > elide_max_length:
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return text[:100] + "..."
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return text
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def anki_fields_term(term : str) -> dict:
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params = {
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"text": term,
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"type": "term",
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"markers": ["audio", "cloze-body-kana", "conjugation", "expression", "furigana", "furigana-plain", "glossary", "glossary-brief", "glossary-no-dictionary", "glossary-first", "glossary-first-brief", "glossary-first-no-dictionary", "part-of-speech", "phonetic-transcriptions", "pitch-accents", "pitch-accent-graphs", "pitch-accent-graphs-jj", "pitch-accent-positions", "pitch-accent-categories", "reading", "tags", "clipboard-image", "clipboard-text", "cloze-body", "cloze-prefix", "cloze-suffix", "dictionary", "dictionary-alias", "document-title", "frequencies", "frequency-harmonic-rank", "frequency-harmonic-occurrence", "frequency-average-rank", "frequency-average-occurrence", "screenshot", "search-query", "popup-selection-text", "sentence", "sentence-furigana", "sentence-furigana-plain", "url"],
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"maxEntries": 1,
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"includeMedia": False,
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}
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response = requests.post(request_url + "/ankiFields", json = params, timeout = request_timeout)
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return json.loads(response.text)
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def term_entries(term :str) -> dict:
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print("Requesting termEntries:")
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params = {
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"term": term,
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"maxEntries": 1,
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"includeMedia": False,
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}
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response = requests.post(request_url + "/termEntries", json = params, timeout = request_timeout)
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return json.loads(response.text)
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# perhaps set up a filter parameter.
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def flatten_string(node):
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if isinstance(node,str):
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yield node
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elif isinstance(node, list):
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for item in node:
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yield from flatten_string(item)
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elif isinstance(node,dict):
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if isinstance(node.get("content"), str):
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yield node["content"]
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else:
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for v in node.values():
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yield from flatten_string(v)
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def extract_text_by_type(node, target_type):
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if isinstance(node,dict):
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data = node.get("data")
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if isinstance(data,dict) and data.get("content") == target_type:
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yield from flatten_string(node.get("content"))
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#yield from ",".join(node.get("content"))
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for value in node.values():
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yield from extract_text_by_type(value, target_type)
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elif isinstance(node,list):
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for item in node:
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yield from extract_text_by_type(item,target_type)
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# Combine both into one tuple
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all_kana = hiragana + katakana + punctuation + (' ', 'ー', '・')
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KANJI_RE = re.compile(r"[\u4E00-\u9FFF]")
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with open("dump-vocab.json", "r", encoding="utf-16") as f:
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vocab = json.load(f)
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def get_glossary(word :str) -> list:
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glossary = list()
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dict_items = term_entries(word)
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entry = next(iter(dict_items.get("dictionaryEntries",[])),None)
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if entry:
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definition = next(iter(entry.get("definitions",[])),None)
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if definition:
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def_entry = next(iter(definition.get("entries", [])),None)
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if def_entry:
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glossary.extend( list(extract_text_by_type(def_entry, "glossary")) )
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return glossary[:2]
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for item in vocab:
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#print(word[0])
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word = item[0]
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print("Requesting termEntries:")
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# params = {
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# "term": word,
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# }
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# response = requests.post(request_url + "/termEntries", json = params, timeout = request_timeout)
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# print(response)
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# print(elide(response.text))
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# print(response.json()) # Dumps json
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# for entry in dict_items.get("dictionaryEntries", []):
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# for definition in entry.get("definitions", []):
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# first = next(iter(definition.get("entries", [])),None)
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# if first:
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# glossary = list(extract_text_by_type(first, "glossary"))
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# if len(glossary) > 0:
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# break
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# for def_entry in definition.get("entries", []):
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# glossary.extend( list(extract_text_by_type(def_entry, "glossary")) )
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# for content in def_entry.get("content", []):
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# if content.get('data') is not None:
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# data = content['data']
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# print(data['content']) # type of content
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#anki_card = anki_fields_term(word)
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item.append("; ".join(get_glossary(word)))
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print()
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with open("dump-vocab.json", "w", encoding="utf-16") as outfile:
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json.dump(vocab, outfile, ensure_ascii=False, indent=2)
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