import json
from elastic_helper import ElasticHelper 

eh_obj = ElasticHelper()
path = "/home/gpu/data_11/mj_qa_section.zip"
all_sections = eh_obj.iterateJsonFile(path, True)
    
def list_classes():
    sections = ["qs2737382", "qs894826", "qs1043309", "qs894807", "qs887945", "qs1023813", "qs1988520", "qs997880", "qs919411", "qs3012247", "qs1036224", "qs1636663", "qs1704019", "qs1555153", "qs895753", "qs1024032", "qs3024418", "qs925237", "qs999331", "qs1073335", "qs2964496", "qs2652755", "qs2241523", "qs939369", "qs894037", "qs2148972", "qs885382", "qs929738", "qs886389", "qs2580013", "qs898204", "qs2305848"]

    classes_list = ''
    for item in all_sections:
        id = item['id']
        if not id in sections:
            continue
        source = item['source']
        try:
            class_ = source['code-ai']['label']
        except:
            class_ = 'None'
        classes_list += id + " - " + class_ + "\n"

    with open('./data/classes_list.txt', 'w') as file:
        file.write(classes_list.strip())
        
def classification_error_handler():
    with open('./data/all_sections_classes_new_140405.json', 'r', encoding='utf-8') as _file:
        sections = json.load(_file)
        
    with open('./data/errors.txt', 'r', encoding='utf-8') as _file:
        error_sections = _file.read()
    errors = error_sections.splitlines()
    
    counter = 1
    for item, value in sections.items():
        id = item
        if not id in errors:
            continue
        
        errors.remove(id)
        result = f'id: {id} -- best-class: {value["best-class"]["label"]}\n'
        
        with open('./data/errors_classes.txt', 'a', encoding='utf-8') as _file:
            _file.write(result)
    
    print(errors)
        
def find_large_sections_in_classified():
    with open('./data/all_sections_classes_new_140405.json', 'r', encoding='utf-8') as _file:
        sections = json.load(_file)
    
    classified_ids = [item for item in sections]
    classified_ids_set = set(classified_ids)
    large_not_classified = []
    with open('./data/large_sections.json', 'r', encoding='utf-8') as _file:
        large_sections = json.load(_file)
    for item in large_sections:
        if not item in classified_ids:
            large_not_classified.append(item)
    return large_not_classified       
 
def classified_sections():
    with open('./data/all_sections_classes_new_140405.json', 'r', encoding='utf-8') as _file:
        classified_sections = json.load(_file)
    
    print(len(classified_sections))
    
    eh_obj = ElasticHelper()
    path = "/home/gpu/data_11/14040423/mj_qa_section.zip"
    all_sections = eh_obj.iterateJsonFile(path, True)
    # count = 285839
    for index, item in enumerate(all_sections):
        print(index+1)
    print(len(all_sections))
    classified_ids = [item for item in sections]
    classified_ids_set = set(classified_ids)
    large_not_classified = []
    with open('./data/large_sections.json', 'r', encoding='utf-8') as _file:
        large_sections = json.load(_file)
    for item in large_sections:
        if not item in classified_ids:
            large_not_classified.append(item)
    return large_not_classified
       
gold_test_sections = ["qs2737382", "qs894826", "qs1043309", "qs894807", "qs887945", "qs1023813", "qs1988520", "qs997880", "qs919411", "qs3012247", "qs1036224", "qs1636663", "qs1704019", "qs1555153", "qs895753", "qs1024032", "qs3024418", "qs925237", "qs999331", "qs1073335", "qs2964496", "qs2652755", "qs2241523", "qs939369", "qs894037", "qs2148972", "qs885382", "qs929738", "qs886389", "qs2580013", "qs898204", "qs2305848"]

if __name__ == '__main__':
    
    with open('./data/all_sections_classes_new_140405.json', 'r', encoding='utf-8') as _file:
        sections = json.load(_file)
    
    gold_sections_classes = {}
    for item in sections :
        if item in gold_test_sections:
            gold_sections_classes[item] = sections[item]
    
    classes_list = ''
    for item in all_sections:
        id = item['id']
        if not id in gold_sections_classes:
            continue
        source = item['source']
        try:
            content = source['content']
        except:
            pass
        gold_sections_classes[id]['content'] = content
        # record = gold_sections_classes[item]
        # record['content'] = content
        
    gold_sections_classes_text = ''
    for index, idx in enumerate(gold_sections_classes):
        gold_sections_classes_text += ''.join(f'order: {index+1}\nid: {idx}\ncontent: {gold_sections_classes[idx]["content"]}\nbest-class: {gold_sections_classes[idx]["best-class"]}\nother-classes: {gold_sections_classes[idx]["other-classes"]}\n\n')

    with open('./data/gold_sections_classes.txt', 'w') as file:
        file.write(gold_sections_classes_text.strip())
    
    # region large sections which send to window
    faults = []
    for item in sections:
        itm = sections[item]
        try:
            best = itm['best-class']['score']
        except:
            continue
        if best > 1:
            print(best)
            faults.append((item,best))

    faults_text = ''
    for item in faults:
        faults_text += ''.join(item[0]) + '\n' 
    with open('./data/large_sections.txt', 'a+') as file:
            file.write(faults_text.strip())
    
    # endregion        
            
    # result = classified_sections()      
    # print(len(result))