ner.py

"""This module contains the CAMeL Tools Named Entity Recognition component.
"""

from helpers.helper import prepare_output
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
# from transformers import BertForTokenClassification, BertTokenizer
from helpers.helper import en_to_ar_camel
from camel_tools.ner import NERecognizer
import os 
import subprocess



if not os.path.exists('model/'):
        subprocess.call( 'mkdir model/', shell=True)
        print('============= MAKING  Model DIR ===============')


if not os.path.exists('model/camel'):
        subprocess.call( 'mkdir model/camel', shell=True)
        print('============= MAKING  Camel DIR ===============')

if not os.path.exists('model/ours'):
        subprocess.call( 'mkdir model/ours', shell=True)
        print('============= MAKING  Ours DIR ===============')



# FOR Gcloud 
'''
model_path = os.path.dirname(os.path.abspath(__file__))+'/model/camel'
    os.environ["CAMELTOOLS_DATA"] = model_path
    copy_path =  model_path+'/data'

if not os.path.exists('/root/.camel_tools/'):
        subprocess.call( 'mkdir /root/.camel_tools/', shell=True)
        print('============= MAKING DIR ===============')



if not os.path.exists('/root/.camel_tools/data/'):
    # subprocess.call(  'sudo cp -r '+  copy_path + '/ ' + '/root/.camel_tools/'    , shell=True)

    ## This is the working one 
    subprocess.call(  'cp -r '+  copy_path + '/ ' + '/root/.camel_tools/'    , shell=True)


    print(subprocess.call('echo $CAMELTOOLS_DATA' , shell=True))
    print(model_path)
    print(copy_path)
    print( os.listdir(model_path))
    print( os.listdir('/root/.camel_tools/'))
    print( os.listdir('/root/'))

''' 
ner = NERecognizer.pretrained()

def test_camel(s):
    # Predict the labels of a single sentence.
    # The sentence must be pretokenized by whitespace and punctuation.
    sentence = s.split()

    labels = ner.predict_sentence(sentence)
    res = ''
    # Print the list of token-label pairs
    for token, label in zip(sentence, labels):
        if(label == 'O'):
            continue
        # print("{}\t{}".format(label, token))
        s = f"{en_to_ar_camel[label]}     {label}      {token}"
        res = res + s + '\n'
    return res , labels , sentence