barr2/using_dataset_hugginface.py

# -*- coding: utf-8 -*-
"""using_dataset_hugginface.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1soGxkZu4antYbYG23GioJ6zoSt_GhSNT
"""

"""**Hugginface loggin for push on Hub**"""
###
#
#  Used bibliografy:
#    https://huggingface.co/learn/nlp-course/chapter5/5
#
###

import os
import time
import math
from huggingface_hub import login
from datasets import load_dataset, concatenate_datasets
from functools import reduce
from pathlib import Path
import pandas as pd
import pathlib
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

HF_TOKEN = ''
DATASET_TO_LOAD = 'bigbio/distemist'
DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm'
DATASET_SOURCE_ID = '11'
BASE_DIR = "BARR2" + os.sep + "txt"

#Loggin to Huggin Face
login(token = HF_TOKEN)

dataset_CODING = load_dataset(DATASET_TO_LOAD)
royalListOfCode = {}
issues_path = 'dataset'
tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium")

#Read current path
path = Path(__file__).parent.absolute()
MAIN_FILE_ADRESS = str(path) + os.sep + BASE_DIR
#print ( os.listdir(str(path) + os.sep + BASE_DIR))

files = [ str(path) + os.sep + BASE_DIR + os.sep +  f for f in os.listdir(MAIN_FILE_ADRESS) if os.path.isfile(str(path) + os.sep + BASE_DIR + os.sep + f) and  pathlib.Path(MAIN_FILE_ADRESS + os.sep + f).suffix == ".txt" ]

#print (files)
for iFile in files:
    with open( iFile,encoding='utf8') as file:
      linesInFile = file.readlines()
      text = reduce(lambda a, b: a + " "+ b, linesInFile, "")

#print (dataset_CODING)

# with open( str(path) + os.sep + 'ICD-O-3_valid-codes.txt',encoding='utf8') as file:
#   """
#      # Build a dictionary with ICD-O-3 associated with 
#      # healtcare problems
#   """
#   linesInFile = file.readlines()
#   for iLine in linesInFile:
#     listOfData = iLine.split('\t')

#     code = listOfData[0]
#     description = reduce(lambda a, b: a + " "+ b, listOfData[1:2], "")
#     royalListOfCode[code.strip()] = description.strip()


# def getCodeDescription(labels_of_type, royalListOfCode):
#   """
#     Search description associated with some code 
#     in royalListOfCode

#   """
#   classification = [] 

#   for iValue in labels_of_type:
#     if iValue in  royalListOfCode.keys():
#       classification.append(royalListOfCode[iValue])
#   return classification


#     # raw_text: Texto asociado al documento, pregunta, caso clínico u otro tipo de información.

#     # topic: (puede ser healthcare_treatment, healthcare_diagnosis, tema, respuesta a pregunta, o estar vacío p.ej en el texto abierto)

#     # speciality: (especialidad médica a la que se relaciona el raw_text p.ej: cardiología, cirugía, otros)

#     # raw_text_type: (puede ser caso clínico, open_text, question)

#     # topic_type: (puede ser medical_topic, medical_diagnostic,answer,natural_medicine_topic, other, o vacio)

#     # source: Identificador de la fuente asociada al documento que aparece en el README y descripción del dataset.

#     # country: Identificador del país de procedencia de la fuente (p.ej.; ch, es) usando el estándar ISO 3166-1 alfa-2 (Códigos de país de dos letras.).
cantemistDstDict = {
  'raw_text': '',
  'topic': '',
  'speciallity': '',
  'raw_text_type': 'clinic_case',
  'topic_type': '',
  'source': DATASET_SOURCE_ID,
  'country': 'es',
  'document_id': ''
}

totalOfTokens = 0
corpusToLoad = []
countCopySeveralDocument = 0
counteOriginalDocument = 0

#print (dataset_CODING['train'][5]['entities'])

for iFile in files:
    with open( iFile,encoding='utf8') as file:
      linesInFile = file.readlines()
      text = reduce(lambda a, b: a + " "+ b, linesInFile, "")       
            #print ("Element in dataset")
            
            #Find topic or diagnosti clasification about the text
      counteOriginalDocument += 1

      listOfTokens = tokenizer.tokenize(text)
      currentSizeOfTokens = len(listOfTokens)
      totalOfTokens += currentSizeOfTokens
      newCorpusRow = cantemistDstDict.copy()

            
      newCorpusRow['raw_text'] = text
      newCorpusRow['document_id'] = str(counteOriginalDocument)
      corpusToLoad.append(newCorpusRow)
        

df = pd.DataFrame.from_records(corpusToLoad)

if os.path.exists(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl"):
  os.remove(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")

df.to_json(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", orient="records", lines=True)
print(
        f"Downloaded all the issues for {DATASET_TO_LOAD}! Dataset stored at {issues_path}/spanish_medical_llms.jsonl"
)

print(' On dataset there are as document ', counteOriginalDocument)
print(' On dataset there are as copy document ', countCopySeveralDocument)
print(' On dataset there are as size of Tokens ', totalOfTokens)
file = Path(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")  # or Path('./doc.txt')
size = file.stat().st_size
print ('File size on Kilobytes (kB)', size >> 10)  # 5242880 kilobytes (kB)
print ('File size on Megabytes  (MB)', size >> 20 ) # 5120 megabytes (MB)
print ('File size on Gigabytes (GB)', size >> 30 ) # 5 gigabytes (GB)

#Once the issues are downloaded we can load them locally using our 
local_spanish_dataset = load_dataset("json", data_files=f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", split="train")


##Update local dataset with cloud dataset
try:  
  spanish_dataset = load_dataset(DATASET_TO_UPDATE, split="train")
  print("=== Before ====")
  print(spanish_dataset)
  spanish_dataset = concatenate_datasets([spanish_dataset, local_spanish_dataset])
except Exception:
  spanish_dataset = local_spanish_dataset

spanish_dataset.push_to_hub(DATASET_TO_UPDATE)

print("=== After ====")
print(spanish_dataset)

# Augmenting the dataset

#Importan if exist element on DATASET_TO_UPDATE we must to update element 
# in list, and review if the are repeted elements