Redmine #1835: preloaded classla models and used xml for api response

package/structure_assignment/api.py

@@ -2,7 +2,7 @@ import os
 
 import lxml.etree as lxml
 
-from flask import Flask, jsonify, make_response, request
+from flask import Flask, Response
 from flask_httpauth import HTTPBasicAuth
 
 import structure_assignment.pipeline as pipeline
@@ -11,6 +11,7 @@ app = Flask(__name__)
 api_prefix = os.environ['API_PREFIX']
 resource_directory = os.environ['API_RESOURCE_DIR']
 tmp_directory = os.environ['API_TMP']
+pipeline.initialise(temp_dir=tmp_directory, resource_dir=resource_directory)
 
 @app.route(api_prefix + '/test/<string:string>', methods=['GET'])
 def test(string):
@@ -21,7 +22,6 @@ def test(string):
         string_file.write(string + '\n')
 
     try:
-        pipeline.initialise(temp_dir=tmp_directory, resource_dir=resource_directory)
         pipeline.import_string_file(string_file_name)
         pipeline.do_tokenise()
         pipeline.do_tweak_conllu()
@@ -37,4 +37,4 @@ def test(string):
         ok = False
 
     results = {'ok':ok, 'message':message}
-    return jsonify(results)
+    return Response(message, mimetype='text/xml')

package/structure_assignment/pipeline.py

@@ -24,16 +24,19 @@ XML_ID_PREFIX = 's'
 
 resource_directory = None
 tmp_directory = None
+nlp = None
 
 def __get_tmp_file_name(file_name):
     return tmp_directory + '/' + file_name
 
 def initialise(**argument_map):
-    global tmp_directory, resource_directory
+    global tmp_directory, resource_directory, nlp
     tmp_directory = argument_map['temp_dir']
     resource_directory = argument_map['resource_dir']
     shutil.rmtree(tmp_directory, True)
     os.makedirs(tmp_directory, exist_ok=True)
+    NLP_CONFIG_MAP['models_dir'] = resource_directory + '/classla'
+    nlp = classla.Pipeline('sl', **NLP_CONFIG_MAP)
 
 def import_string_file(file_name):
     shutil.copyfile(file_name, __get_tmp_file_name(STRING_LIST_FILE_NAME))
@@ -54,8 +57,6 @@ def do_parse():
     doc = Document(text=None)
     conll_file = CoNLLFile(filename=input_file_name)
     doc.conll_file = conll_file
-    NLP_CONFIG_MAP['models_dir'] = resource_directory + '/classla'
-    nlp = classla.Pipeline('sl', **NLP_CONFIG_MAP)
     result = nlp(doc)
     result.conll_file.write_conll(output_file_name)