Sentence processing can now be used with the REST API

ia/process.py

@@ -2,11 +2,12 @@ import os
 
 import torch
 from unidecode import unidecode
-
 import get_path_file
 from ia.model import NeuralNet
 from ia.nltk_utils import bag_of_words, tokenize
 
+print("Loading, might take a few seconds...")
+
 path = os.path.dirname(get_path_file.__file__)
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -53,11 +54,6 @@ def get_tag_for_sentence(input_sentence):
     probs = torch.softmax(output, dim=1)
     prob = probs[0][predicted.item()]
     if prob.item() > 0.75 and len(sentence) > 2:
-        return "MATCHING INTENT : " + tag + " (" + str(prob.item()) + ")"
-        # return intents.intents.get_matching_intent_for_tag(tag).get('tag')
+        return tag
     else:
         return 'dont_understand'
-
-
-if __name__ == '__main__':
-    print(get_tag_for_sentence("Hey, est il"))

main.py

@@ -1,6 +1,7 @@
 import flask
 from flask import Flask, request, jsonify, Response
 
+import ia.process
 from utils import config_utils, flask_utils
 
 app = Flask(__name__)
@@ -10,11 +11,15 @@ app = Flask(__name__)
 def process_request():
     data = flask_utils.get_data_in_request(request)
 
-    if 'sentence' not in data:
-        flask.abort(Response('You must provide a \'sentence\' parameter!'))
+    if 'sentence' not in data or not data['sentence']:
+        flask.abort(Response('You must provide a \'sentence\' parameter (not empty aswell)!'))
 
-    print(data)
-    return jsonify(data)
+    sentence = data['sentence']
+    tag_for_request = ia.process.get_tag_for_sentence(sentence)
+
+    print("SENTENCE : " + sentence + " /// TAG : " + tag_for_request)
+
+    return jsonify(tag_for_request)
 
 
 if __name__ == '__main__':