import json
import MeCab
import re

import requests

request_url = "http://127.0.0.1:19633"
request_timeout = 10

# Initialize the Tagger
#tagger = MeCab.Tagger()

# Generate Hiragana and Katakana characters
hiragana = tuple(chr(i) for i in range(12353, 12436)) # \u3041 - \u3094
katakana = tuple(chr(i) for i in range(12450, 12532)) # \u30A2 - \u30F4
punctuation = tuple(chr(i) for i in range(65281, 65382))

def elide(text: str) -> str:
    elide_max_length = 100
    if len(text) > elide_max_length:
        return text[:100] + "..."
    return text

def anki_fields_term(term : str) -> dict:
    params = {
        "text": term,
        "type": "term",
        "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"],
        "maxEntries": 1,
        "includeMedia": False,
    }
    response = requests.post(request_url + "/ankiFields", json = params, timeout = request_timeout)
    return json.loads(response.text)

def term_entries(term :str) -> dict:
    print("Requesting termEntries:")
    params = {
        "term": term,
        "maxEntries": 1,
        "includeMedia": False,
    }
    response = requests.post(request_url + "/termEntries", json = params, timeout = request_timeout)
    return json.loads(response.text)

# perhaps set up a filter parameter.
def flatten_string(node):
    if isinstance(node,str):
        yield node
    elif isinstance(node, list):
        for item in node:
            yield from flatten_string(item)
    elif isinstance(node,dict):
        if isinstance(node.get("content"), str):
            yield node["content"]
        else:
            for v in node.values():
                yield from flatten_string(v)

def extract_text_by_type(node, target_type):
    if isinstance(node,dict):
        data = node.get("data")
        if isinstance(data,dict) and data.get("content") == target_type:
            yield from flatten_string(node.get("content"))
            #yield from ",".join(node.get("content"))
        for value in node.values():
            yield from extract_text_by_type(value, target_type)
    elif isinstance(node,list):
        for item in node:
            yield from extract_text_by_type(item,target_type)

# Combine both into one tuple
all_kana = hiragana + katakana + punctuation + ('　', 'ー', '・')

KANJI_RE = re.compile(r"[\u4E00-\u9FFF]")

with open("dump-vocab.json", "r", encoding="utf-16") as f:
    vocab = json.load(f)

for item in vocab:
    #print(word[0])
    word = item[0]
    print("Requesting termEntries:")
    # params = {
    #     "term": word,
    # }
    # response = requests.post(request_url + "/termEntries", json = params, timeout = request_timeout)
    # print(response)
    # print(elide(response.text))
    # print(response.json()) # Dumps json
    dict_items = term_entries(word)
    # for dic in dict_items['dictionaryEntries']:
    #     print(dic)
    ## Recurssive content -> data pipelines
    print(word)
    glossary = list()

    entry = next(iter(dict_items.get("dictionaryEntries",[])),None)
    if entry:
        definition = next(iter(entry.get("definitions",[])),None)
        if definition:
            def_entry = next(iter(definition.get("entries", [])),None)
            if def_entry:
                glossary.extend( list(extract_text_by_type(def_entry, "glossary")) )
    glossary[:2]
#    for entry in dict_items.get("dictionaryEntries", []):
#        for definition in entry.get("definitions", []):
            # first = next(iter(definition.get("entries", [])),None)
            # if first:
            #     glossary = list(extract_text_by_type(first, "glossary"))
            #     if len(glossary) > 0:
            #         break
#            for def_entry in definition.get("entries", []):
#                glossary.extend( list(extract_text_by_type(def_entry, "glossary")) )
                # for content in def_entry.get("content", []):
                #     if content.get('data') is not None:
                #         data = content['data']
                #         print(data['content']) # type of content
            
    #anki_card = anki_fields_term(word)
    item.append("; ".join(glossary))
    print()

with open("dump-vocab.json", "w", encoding="utf-16") as outfile:
    json.dump(vocab, outfile, ensure_ascii=False, indent=2)