Merge pull request #179 from Cyberjusticelab/65/remove-util-pythonpat…

…h-function

 [#65] Fixes all lints, and removes unused util functions

src/backend_service/app.py

@@ -3,9 +3,6 @@
 from flask import Flask, request, abort, make_response, jsonify
 from flask_cors import CORS
 
-import util
-util.load_src_dir_to_sys_path()
-
 from controllers import conversationController, legalController
 from postgresql_db import database
 

src/backend_service/app_test.py

@@ -4,9 +4,6 @@
 
 from werkzeug.datastructures import FileStorage
 
-import util
-
-util.load_src_dir_to_sys_path()
 from services import fileService
 from services.staticStrings import StaticStrings
 

src/backend_service/controllers/conversationController.py

@@ -1,10 +1,6 @@
 import json
 
 from flask import jsonify, abort, make_response
-
-import util
-
-util.load_src_dir_to_sys_path()
 from postgresql_db.models import *
 from services import nlpService, fileService
 from services.staticStrings import *

src/ml_service/feature_extraction/main.py

@@ -8,7 +8,7 @@
 from outputs.output import Log
 
 '''
-Improvement: 
+Improvement:
 1 - Commands can be imporoved for this class. Now a little basic
 2 - Perhaps clear previous output directories of the models etc.
 '''

src/ml_service/outputs/output.py

@@ -66,7 +66,7 @@ def save_text_file(self, filename, text, protocol='a'):
         __rel_path = filename
         file_path = os.path.join(self.__output_dir, __rel_path)
         file = open(file_path, protocol)
-        if not type(text) == list:
+        if not isinstance(text, list):
             text = [text]
         for lines in text:
             file.writelines(lines)

src/ml_service/word_vectors/related_word_fetcher.py

@@ -7,7 +7,7 @@
 from outputs.output import Log
 
 """
-This python module is used to fill in missing keys from the 
+This python module is used to fill in missing keys from the
 word vector model. This script attempts to find the closest
 similar word to the one given. The end goal will enable every
 word to be vectorized.
@@ -18,7 +18,7 @@
 
 """
 Cache is a dictionary which stores keys<words not found>
-and maps them to values<words found> 
+and maps them to values<words found>
 """
 
 cache = {}

src/nlp_service/app.py

@@ -3,10 +3,8 @@
 from flask import Flask
 from flask import request
 
-import util
 from controllers import nlpController
 
-util.load_src_dir_to_sys_path()
 from postgresql_db import database
 
 # Flask Setup
@@ -18,7 +16,7 @@
 """
   These functions establishes HTTP routes for the core functionality of the src/nlp_service/controllers/nlpController.py
   The core functionality of classify_claim_category and classify_fact_value are explained further in the file mentioned above
-  
+
 """
 
 

src/nlp_service/controllers/nlpController.py

@@ -1,8 +1,5 @@
 from flask import jsonify, abort, make_response
 
-import util
-
-util.load_src_dir_to_sys_path()
 from postgresql_db.models import *
 from rasa.rasa_classifier import RasaClassifier
 from services import mlService

src/nlp_service/rasa/rasa_classifier.py

@@ -26,7 +26,7 @@ def __init__(self):
     """
           Training method that trains the data sets from facts and problem categories separately
               force_train=False will ensure the saved models are used
-              initialize_interpreters=True so the interpreters get initialized due to the models already being present 
+              initialize_interpreters=True so the interpreters get initialized due to the models already being present
           result: creates models on trained data
     """
 
@@ -63,7 +63,7 @@ def classify_fact(self, fact_name, message):
         Training the interpreters one by one
         training_data_dir: Directory where data is held on our project
         interpreter_dict: dictionary of interpreters that will hold the interpreters
-        force_train: set ti either true or false so it knows if it should train new models (if empty dir) or use the old ones already present 
+        force_train: set ti either true or false so it knows if it should train new models (if empty dir) or use the old ones already present
         initialized_interpreters: won't attempt to initialize interpreters if there are no models
         :returns dict of the fact with intent and entities
     """