import json
import numpy
import os

# Opening JSON file
f = open('thirukural_git.json')

# returns JSON object as
# a dictionary
data = json.load(f)

en_translations=[]
kurals=[]
# Iterating through the json
# list
for kural in data['kurals']:
    en_translations.append((kural['meaning']['en'].lower()))
    kurals.append(kural['kural'])



# Closing file
f.close()
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
# model.tokenizer.add_special_tokens({'pad_token':'[thiyaga]'})
#Encoding:

sen_embeddings = model.encode(en_translations)

# sen_embeddings= numpy.memmap('trainedmodel',mode="r",dtype=numpy.float32,shape=(1330,768))
# sen_embeddings.tofile('trainedmodel')

def find_similarities(input:str):
    input_embeddings = model.encode([input.lower()])
    from sklearn.metrics.pairwise import cosine_similarity
    #let's calculate cosine similarity for sentence 0:
    similarity_matrix=cosine_similarity(
        [input_embeddings[0]],
        sen_embeddings[1:]
    )

    indices=[numpy.argpartition(similarity_matrix[0],-3)[-3:]]
    response=''
    for index in indices[0]:
        print(similarity_matrix[0][index])
        response+=en_translations[index+1]
        print(en_translations[index+1])
        response += "\n"+"\n".join(kurals[index+1])+"\n"
        print("\n".join(kurals[index+1]))
    return response

# while True:
#     text=input('Ask valluvar: ')
#     if( text == 'exit'):
#         break
#     find_similarities(text)