import json
from watson_developer_cloud import NaturalLanguageUnderstandingV1
from watson_developer_cloud.natural_language_understanding_v1 \
  import Features, EntitiesOptions, KeywordsOptions
import time
from datetime import timedelta
import sys
import os
import argparse


#We need to get our API credentials in the code for authentication that we have stored as Environment Variables locally
NLP_USER_WATSON = os.environ.get("NLP_USER_WATSON")
NLP_PASS_WATSON = os.environ.get("NLP_PASS_WATSON")
NLP_VER_WATSON = os.environ.get("NLP_VER_WATSON")


#Following line is used to save all the console output into a text file
sys.stdout = open('nlp_api_output.txt', 'a')

start_time = time.monotonic()


def input_file(text_file_path):
    global text
    if os.path.isfile(text_file_path):
        with open(text_file_path, 'r') as text_file:
            text = text_file.read()
    else:
        print("File doesn't exist in the directory!")


def analyze_text():
  #Initialize NaturalLanguageUnderstanding function using the API credentials
  natural_language_understanding = NaturalLanguageUnderstandingV1(
    username = NLP_USER_WATSON,
    password = NLP_PASS_WATSON,
    version = NLP_VER_WATSON)

  response = natural_language_understanding.analyze(
    text = text,
    features = Features(
      entities = EntitiesOptions(
        emotion = True,
        sentiment = True),
      keywords = KeywordsOptions(
        emotion = True,
        sentiment = True)))

  print(json.dumps(response, indent = 2)) #json output after textual analysis


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description = __doc__,
        formatter_class = argparse.RawDescriptionHelpFormatter)
    parser.add_argument(
        'text_file_path',
        help = 'The complete file path of the text file you want to analyze.')
    args = parser.parse_args()

    input_file(args.text_file_path)
    analyze_text()


end_time = time.monotonic()
print("Execution_Time:", timedelta(seconds = end_time - start_time))
print('\n')