Update file handling. Now md5 is correct

nlp

@@ -148,9 +148,11 @@ def parse_args():
                         required=True)
     parser.add_argument('-l', '--language',
                         choices=SPACY_MODELS.keys(),
+                        help='Language of the input (2-character ISO 639-1 language codes)',  # noqa
                         required=True)
     parser.add_argument('--check-encoding',
-                        action='store_true')
+                        action='store_true',
+                        help='Check encoding of the input file, UTF-8 is used instead')  # noqa
     parser.add_argument('--log-dir',
                         help='Logging directory')
     parser.add_argument('--mem-mb',

spacy-nlp

@@ -16,29 +16,32 @@ spacy_models = {spacy.info(pipeline)['lang']: pipeline
 
 # Parse the given arguments
 parser = ArgumentParser(description='Create annotations for a given txt file')
-parser.add_argument('input', metavar='Path to txt input file')
-parser.add_argument('output', metavar='Path to JSON output file')
+parser.add_argument('input', help='Path to txt input file')
+parser.add_argument('output', help='Path to JSON output file')
 parser.add_argument('-l', '--language',
                     choices=spacy_models.keys(),
+                    help='Language of the input (2-character ISO 639-1 language codes)',  # noqa
                     required=True)
-parser.add_argument('-c', '--check-encoding', action='store_true')
+parser.add_argument('-c', '--check-encoding',
+                    action='store_true',
+                    help='Check encoding of the input file, UTF-8 is used instead')  # noqa
 args = parser.parse_args()
 
-
-# If requested: Check the encoding of the text contents from the input file
-# Else: Use utf-8
+if args.check_encoding:
     with open(args.input, "rb") as text_file:
         if args.check_encoding:
             encoding = chardet.detect(text_file.read())['encoding']
         else:
             encoding = 'utf-8'
+
+with open(args.input, "rb") as text_file:
     text_md5 = hashlib.md5()
     for chunk in iter(lambda: text_file.read(128 * text_md5.block_size), b''):
         text_md5.update(chunk)
 
 # Load the text contents from the input file
 with open(args.input, encoding=encoding) as text_file:
-    # spaCy NLP is limited to strings with maximum 1 million characters at
+    # spaCy NLP is limited to strings with a maximum of 1 million characters at
     # once. So we split it into suitable chunks.
     text_chunks = textwrap.wrap(
         text_file.read(),

vrt-creator

@@ -94,9 +94,9 @@ def main():
 
     # Parse the given arguments
     parser = ArgumentParser(description='Create a vrt from JSON and txt')
-    parser.add_argument('text', metavar='Path to txt file')
-    parser.add_argument('stand_off_data', metavar='Path to JSON file')
-    parser.add_argument('output', metavar='Path to vrt output file')
+    parser.add_argument('text', help='Path to txt file')
+    parser.add_argument('stand_off_data', help='Path to JSON file')
+    parser.add_argument('output', help='Path to vrt output file')
     args = parser.parse_args()
 
     with open(args.stand_off_data) as stand_of_data_file:

wrapper/nlp

@@ -6,7 +6,7 @@ import os
 import subprocess
 import sys
 
-CONTAINER_IMAGE = 'gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:1.0.0'
+CONTAINER_IMAGE = 'gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:1.0.0b'
 CONTAINER_INPUT_DIR = '/input'
 CONTAINER_OUTPUT_DIR = '/output'
 CONTAINER_LOG_DIR = '/logs'