import pyarabic.araby as araby
import pandas as pd
import numpy as np
import re
from datasets import load_dataset
from datasets import Features
from datasets import Value
from datasets import Dataset
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import os

Secret_token = os.getenv('HF_token')

dataset = load_dataset("FDSRashid/embed_matn", token = Secret_token)
books = load_dataset('FDSRashid/Hadith_info', data_files='Books.csv', token=Secret_token)['train'].to_pandas()
df = dataset["train"].to_pandas()


dataset = load_dataset("FDSRashid/hadith_info", data_files = 'All_Matns.csv',token = Secret_token, features = features)
matn_info = dataset['train'].to_pandas()
matn_info = matn_info.drop(97550)
matn_info = matn_info.drop(307206)
matn_info['taraf_ID'] = matn_info['taraf_ID'].replace('KeyAbsent', -1)
matn_info['taraf_ID'] = matn_info['taraf_ID'].astype(int)

matn_info['Book ID'] = matn_info['bookid_hadithid'].apply(lambda x: int(x.split('_')[0]))
matn_info['Hadith Number'] = matn_info['bookid_hadithid'].apply(lambda x: int(x.split('_')[1]))
matn_info = matn_info.join(books, on='Book ID')
cols_to_use = df.columns.difference(matn_info.columns)

joined_df = matn_info.merge(df[cols_to_use], left_index=True, right_on='__index_level_0__')
df = joined_df.copy()

model = SentenceTransformer('FDSRashid/QulBERT', token=Secret_token)


def find_most_similar_matn(text, n):
    embed_text = model.encode(araby.strip_diacritics(text))