from .cleaning import remove_citations, split_data, split_text, chunk_data
import pandas as pd
import numpy as np
import json

with open("utils/id2label.json", "r") as j:
    id2label = json.loads(j.read())

with open("utils/label2id.json", "r") as j:
    label2id = json.loads(j.read())


def average_text(text, model):
    # result = classifier(df_train[(df_train.case_name==case) & (df_train.category=='per_curiam')]['clean_text'].to_list())
    result = model(text)
    pred = {}
    for c in result:
        for d in c:
            if d["label"] not in pred:
                pred[d["label"]] = [round(d["score"], 2)]
            else:
                pred[d["label"]].append(round(d["score"], 2))
    sumary = {k: round(sum(v) / len(v), 2) for k, v in pred.items()}
    result = [{dct["label"]: round(dct["score"], 2) for dct in lst} for lst in result]
    return dict(sorted(sumary.items(), key=lambda x: x[1], reverse=True)), result


# def find_case_by_name(df, name):
#     return display(
#         HTML(
#             df[df["case_name"].str.contains(name)]
#             .iloc[:, :-1]
#             .to_html(render_links=True, escape=False)
#         )
#     )


# def head_df(df):
#     return display(
#         HTML(df.iloc[:, :-1].head().to_html(render_links=True, escape=False))
#     )