Merge branch 'fix/text-2-emotion-2' into 'master'

fix: text_2_emotion_2

See merge request !6

src/lhw_nlp/Dockerfile

@@ -11,11 +11,11 @@ RUN if [ "$NO_GPU" = 0 ]; then \
 RUN pip3 install transformers
 RUN pip3 install dialogflow
 RUN pip3 install pygame
-# ADD THESE LATER :)
-RUN pip3 install text2emotion
-# RUN pip3 install tensorflow
-# RUN pip3 install pickle
-# RUN pip3 install scipy
+
+# For text_2_emotion nodes
+RUN pip3 install text2emotion 
+RUN pip3 install tensorflow
+RUN pip3 install scipy
 
 # Install dependencies
 COPY src/lhw_interfaces src/lhw_interfaces

src/lhw_nlp/lhw_nlp/text_2_emotion_2.py

@@ -21,6 +21,7 @@ import time
 import pickle
 import numpy as np
 
+import os
 
 class NodeText2Emotion(Node):
     def __init__(self, model, tokenizer):
@@ -68,36 +69,23 @@ class NodeText2Emotion(Node):
 
     def text_cb(self, text_msg):
         """ text_cb.data has the text data """
-        self.get_logger().info('text callback!')
         if text_msg.recognized_text != " ":
-            self.log.info("Response: " + text_msg.recognized_text)
-
             #--------------CHECK THE CODE BELOW WITH EXPORT SIMON T-----------------------------
-            print(str(text_msg.recognized_text))
+            # print(str(text_msg.recognized_text))
         
             emotion_arr = self.analyse_text(str(text_msg.recognized_text))
             msg_emotion = self.generateEventT2E(
                 source_id=Response.TEXT_2_EMOTION_2,
                 speaker_id='',
-                emotion_dict=emotion_arr
+                emotion_arr=emotion_arr[0]  # SAMI HAVE A LOOK AT THIS /OSCAR :)
             )
             self.broadcast_emotion.publish(msg_emotion)
 
-            #print('Predicted emotion is: {} and prediction took ({:.2f} seconds)'.format(emotion, pred_time))
-
-            # resp_msg = 'Predicted emotion is: {} and prediction took ({:.2f} seconds)'.format(emotion, pred_time)
-            # msg.recognized_text = resp_msg
-            # animated_msg.data = "I sense {} in you".format(emotion)
-            # self.response_pub.publish(msg)
-            # self.animated_pub.publish(animated_msg)
-
-            # msg.response = "TODO: THIS SHOULD BE THE PREDICTED EMOTION"
-            # self.get_logger().info('publishing: "%s"' % msg.response)
-
 
 def main(args=None):
-    model = keras.models.load_model("nlp")
-    with open('tokenizer.pickle', 'rb') as handle:
+    path_to_here = os.getcwd() + '/src/lhw_nlp/lhw_nlp/'
+    model = keras.models.load_model(path_to_here + 'nlp')
+    with open(path_to_here + 'tokenizer.pickle', 'rb') as handle:
         tokenizer = pickle.load(handle)
 
     rclpy.init(args=args)