Updated utils.py to support latest ChatbotAI

utils.py

@@ -5,8 +5,8 @@
 
 # In[1]:
 
-
-from chatbot import Chat,reflections,multiFunctionCall
+#from chatbot import Chat,reflections,multiFunctionCall
+from chatbot import Chat,register_call
 import wikipedia
 import os
 
@@ -15,7 +15,7 @@
 
 # In[2]:
 
-
+@register_call("whoIs")
 def whoIs(query,sessionID="general"):
     try:
         return wikipedia.summary(query)
@@ -38,7 +38,7 @@ def whoIs(query,sessionID="general"):
 import cv2
 from keras.models import load_model
 import numpy as np
-import playsound
+# import playsound
 # parameters for loading data and images
 detection_model_path = 'Emotion/haarcascade_files/haarcascade_frontalface_default.xml'
 emotion_model_path = 'Emotion/models/_mini_XCEPTION.102-0.66.hdf5'
@@ -50,6 +50,7 @@ def whoIs(query,sessionID="general"):
 EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised",
  "neutral"]
 
+@register_call("emo")
 def emo(query,sessionID="general"):
     cv2.namedWindow('your_face')
     camera = cv2.VideoCapture(0)
@@ -92,87 +93,7 @@ def emo(query,sessionID="general"):
         return "I cannot see your face."
 
 
-# # Face Identification Connection
-
-# In[4]:
-
-import cv2
-import matplotlib.pyplot as plt
-from IPython import display
-
-import face_recognition
-import glob
-users = glob.glob("Users\*.jpg")
-# Load a sample picture and learn how to recognize it.
-known_face_encodings = []
-known_face_names = []
-
-for user in users:
-    user_image = face_recognition.load_image_file(user)
-    known_face_encodings.append(face_recognition.face_encodings(user_image)[0])
-    known_face_names.append(user.split("\\")[1].split(".")[0])
-print(known_face_names)
-
-
-# In[5]:
-
-
-import face_recognition
-
-def identifyu(query=0,sessionID="general"):
-    video_capture = cv2.VideoCapture(0)
-
-    # Load a sample picture and learn how to recognize it.
-
-
-    # while True:
-        # Grab a single frame of video
-    ret, frame = video_capture.read()
-
-    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
-    rgb_frame = frame[:, :, ::-1]
-
-    # Find all the faces and face enqcodings in the frame of video
-    face_locations = face_recognition.face_locations(rgb_frame)
-    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
-    name = "Unknown"
-    # Loop through each face in this frame of video
-    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
-        # See if the face is a match for the known face(s)
-        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
-
-        name = "Unknown"
-
-        # If a match was found in known_face_encodings, just use the first one.
-        if True in matches:
-            first_match_index = matches.index(True)
-            name = known_face_names[first_match_index]
-
-        # Draw a box around the face
-        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
-
-        # Draw a label with a name below the face
-        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
-        font = cv2.FONT_HERSHEY_DUPLEX
-        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
-    #webcam_preview = plt.imshow(frame)
-
-#     # Hit 'q' on the keyboard to quit!
-#     if cv2.waitKey(1) & 0xFF == ord('q'):
-#         break
-#     break
-
-    # Release handle to the webcam
-    video_capture.release()
-    cv2.destroyAllWindows()
-    return name
-
-
-# # User Can make bot learn
-
-# In[50]:
-
-
+@register_call("learnq")
 def learnq(query,sessionID="general"):
     print(query)
     try:
@@ -184,6 +105,8 @@ def learnq(query,sessionID="general"):
     except:
         pass
     return "write the question again"
+
+@register_call("learna")
 def learna(query,sessionID="general"):
     print(query)
     try:
@@ -226,7 +149,7 @@ def decryp(filename):
 
 # In[40]:
 
-
+@register_call("whathappen")
 def whathappen(query,sessionID="general"):
     aa = query
     nam = identifyu()
@@ -242,4 +165,3 @@ def sas(sentence):
         return "sad"
 
 from datetime import datetime
-