def emotional_mindset_func(query, tok, entities):
if (re.search('happiness', query)):
entities.append('"Status": "Happiness"')
elif (re.search('optimism', query)):
entities.append('"Status": "Optimism"')
elif (re.search('(self(-| )esteem)', query)):
entities.append('"Status": "Self_Esteem"')
elif (re.search('(self(-| )motivation)', query)):
entities.append('"Status": "Self_Motivation"')
elif (re.search('(emotional (assessment|intelligence|profile))|eq',
query)):
entities.append('"Status": "Emotional_Assessment"')
else:
m = load_model(
'D:\\bot\\botapi\\botapi\\models\\Emotional_Assessment\\Emotional_Mindset_Model.h5'
)
score = model_score_LSTM_tokenize(query, tok, m)
if ((score[0][0] > score[0][1]) & (score[0][0] > score[0][2]) &
(score[0][0] > score[0][3]) & (score[0][0] > score[0][4])):
entities.append('"Status": "Happiness"')
elif ((score[0][1] > score[0][0]) & (score[0][1] > score[0][2]) &
(score[0][1] > score[0][3]) & (score[0][1] > score[0][4])):
if (re.search('optimisim|optimistic', query)):
entities.append('"Status": "Optimism"')
else:
entities.append('"Status": "Emotional_Mindset"')
elif ((score[0][2] > score[0][0]) & (score[0][2] > score[0][1]) &
(score[0][2] > score[0][3]) & (score[0][2] > score[0][4])):
entities.append('"Status": "Emotional_Mindset"')
elif ((score[0][3] > score[0][0]) & (score[0][3] > score[0][1]) &
(score[0][3] > score[0][2]) & (score[0][3] > score[0][4])):
entities.append('"Status": "Self_Esteem"')
else:
entities.append('"Status": "Self_Motivation"')
return entities
def persona_model(query, persona_tok):
intent = ''
m = load_model(
'D:\\bot\\botapi\\botapi\\models\\Behavioral_Assesment\\Assessment_concious_core_persona_Model.h5'
)
score = model_score_LSTM_tokenize(query, persona_tok, m)
if ((score[0][0] > score[0][1]) & (score[0][0] > score[0][2]) &
(score[0][0] > score[0][3])):
intent = 'Graphs'
elif ((score[0][1] > score[0][0]) & (score[0][1] > score[0][2]) &
(score[0][1] > score[0][3])):
intent = 'Inquire_Energy'
elif ((score[0][2] > score[0][0]) & (score[0][2] > score[0][3]) &
(score[0][2] > score[0][3])):
intent = 'Inquire_Intesity'
else:
intent = 'Intensity'
return intent
def understanding_self_and_others_func(query, tok, entities):
if (re.search('empathy', query)):
entities.append('"Status": "Empathy"')
elif (re.search('(understanding self (and|&) other)', query)):
entities.append('"Status": "Understanding_Self_And_Others"')
elif (re.search('understanding self', query)):
entities.append('"Status": "Understanding_Self"')
else:
m = load_model(
'D:\\bot\\botapi\\botapi\\models\\Emotional_Assessment\\Understanding_Self_And_Others_Model.h5'
)
score = model_score_LSTM_tokenize(query, tok, m)
if ((score[0][0] > score[0][1]) & (score[0][0] > score[0][2])):
entities.append('"Status": "Empathy"')
elif ((score[0][1] > score[0][0]) & (score[0][1] > score[0][2])):
entities.append('"Status": "Understanding_Self_And_Others"')
else:
entities.append('"Status": "Understanding_Self"')
return entities
def mastering_self_func(query, tok, entities):
if (re.search('stress managment', query)):
entities.append('"Status": "Stress_Managment"')
elif (re.search('impulse control', query)):
entities.append('"Status": "Impulse_Control"')
elif (re.search('emotional self control', query)):
entities.append('"Status": "Emotional_Self_Control"')
elif (re.search('bias managment', query)):
entities.append('"Status": "Bias_Managment"')
elif (re.search('change managment', query)):
entities.append('"Status": "Change_Managment"')
else:
m = load_model(
'D:\\bot\\botapi\\botapi\\models\\Emotional_Assessment\\Assessment_emotional_mastering_self.h5'
)
score = model_score_LSTM_tokenize(query, tok, m)
if ((score[0][0] > score[0][1]) & (score[0][0] > score[0][2]) &
(score[0][0] > score[0][3]) & (score[0][0] > score[0][4]) &
(score[0][0] > score[0][5])):
entities.append('"Status": "Bias_Managment"')
elif ((score[0][1] > score[0][0]) & (score[0][1] > score[0][2]) &
(score[0][1] > score[0][3]) & (score[0][1] > score[0][4]) &
(score[0][1] > score[0][5])):
entities.append('"Status": "Change_Managment"')
elif ((score[0][2] > score[0][0]) & (score[0][2] > score[0][1]) &
(score[0][2] > score[0][3]) & (score[0][2] > score[0][4]) &
(score[0][2] > score[0][5])):
entities.append('"Status": "Emotional_Self_Control"')
elif ((score[0][3] > score[0][0]) & (score[0][3] > score[0][1]) &
(score[0][3] > score[0][2]) & (score[0][3] > score[0][4]) &
(score[0][3] > score[0][5])):
entities.append('"Status": "Impulse_Control"')
elif ((score[0][4] > score[0][0]) & (score[0][4] > score[0][1]) &
(score[0][4] > score[0][2]) & (score[0][4] > score[0][3]) &
(score[0][4] > score[0][5])):
entities.append('"Status": "Mastering_Self"')
else:
entities.append('"Status": "Stress_Managment"')
return entities
def mastering_self_with_others(query, tok, entities):
if (re.search('assertiveness', query)):
entities.append('"Status": "Assertiveness"')
elif (re.search('influence', query)):
entities.append('"Status": "Influence"')
elif (re.search('managing relationship', query)):
entities.append('"Status": "Managing_Relationships"')
elif (re.search('decision confidence', query)):
entities.append('"Status": "Decision_Confidence"')
elif (re.search('emotional expression', query)):
entities.append('"Status": "Emotional_Expression"')
else:
m = load_model(
'D:\\bot\\botapi\\botapi\\models\\Emotional_Assessment\\Mastering_self_with_others.h5'
)
score = model_score_LSTM_tokenize(query, tok, m)
if ((score[0][0] > score[0][1]) & (score[0][0] > score[0][2]) &
(score[0][0] > score[0][3]) & (score[0][0] > score[0][4]) &
(score[0][0] > score[0][5])):
entities.append('"Status": "Assertiveness"')
elif ((score[0][1] > score[0][0]) & (score[0][1] > score[0][2]) &
(score[0][1] > score[0][3]) & (score[0][1] > score[0][4]) &
(score[0][1] > score[0][5])):
entities.append('"Status": "Decision_Confidence"')
elif ((score[0][2] > score[0][0]) & (score[0][2] > score[0][1]) &
(score[0][2] > score[0][3]) & (score[0][2] > score[0][4]) &
(score[0][2] > score[0][5])):
entities.append('"Status": "Emotional_Expression"')
elif ((score[0][3] > score[0][0]) & (score[0][3] > score[0][1]) &
(score[0][3] > score[0][2]) & (score[0][3] > score[0][4]) &
(score[0][3] > score[0][5])):
entities.append('"Status": "Influence"')
elif ((score[0][4] > score[0][0]) & (score[0][4] > score[0][1]) &
(score[0][4] > score[0][2]) & (score[0][4] > score[0][3]) &
(score[0][4] > score[0][5])):
entities.append('"Status": "Mastering_Self_With_Others"')
else:
entities.append('"Status": "Managing_Relationships"')
return entities
def Flex_persona(query, flex_tok):
intent = ''
if (re.search('shift', query)):
if (re.search(' above| up', query)):
intent = '"Shift" : "Above"'
if (re.search(' below| down', query)):
intent = '"Shift" : "Below"'
else:
m = load_model(
'D:\\bot\\botapi\\botapi\\models\\Behavioral_Assesment\\Flex_model.h5'
)
score = model_score_LSTM_tokenize(query, flex_tok, m)
if ((score[0][0] > score[0][1]) & (score[0][0] > score[0][2]) &
(score[0][0] > score[0][3]) & (score[0][0] > score[0][4])):
intent = '"Shift" : "Change_Behavior"'
elif ((score[0][1] > score[0][0]) & (score[0][1] > score[0][2]) &
(score[0][1] > score[0][3]) & (score[0][1] > score[0][4])):
if (re.search(' graph| flex', query)):
intent = 'Graphs'
else:
intent = 'Inquiry'
elif ((score[0][2] > score[0][0]) & (score[0][2] > score[0][3]) &
(score[0][2] > score[0][3]) & (score[0][2] > score[0][4])):
intent = "Inquiry"
elif ((score[0][3] > score[0][0]) & (score[0][3] > score[0][2]) &
(score[0][3] > score[0][1]) & (score[0][3] > score[0][4])):
if (re.search('shift below', query)):
intent = '"Shift" : "Below"'
else:
intent = '"Shift" : "Above"'
else:
if (re.search('shift above', query)):
intent = '"Shift" : "Above"'
else:
intent = '"Shift" : "Below"'
return intent
def capability_assessment_model(query, capability_entity, entity):
top_intent = '"Assessment"'
sub_intent = '"Capability_Assessment"'
Score = 100
report_check = 0
graph_check = 0
if (re.search('verbal interpretation', query)):
entity.append('"Status" : "Verbal_Interpretation"')
entity = main_entity(query, capability_entity[1], entity)
elif (re.search('numerical reasoning', query)):
entity.append('"Status" : "Numerical_Reasoning"')
entity = main_entity(query, capability_entity[1], entity)
elif (re.search('spatial visualization', query)):
entity.append('"Status" : "Spatial_Visualization"')
entity = main_entity(query, capability_entity[1], entity)
elif (re.search('perceptual speed', query)):
entity.append('"Status" : "Perceptual_Speed"')
entity = main_entity(query, capability_entity[1], entity)
elif (re.search('verbal reasoning', query)):
entity.append('"Status" : "Verbal_Reasoning"')
entity = main_entity(query, capability_entity[1], entity)
elif (re.search('capability (assessment|profile)', query)):
intent = overall(query, capability_entity[2])
if (intent == 'Inquiry'):
entity.append('"Status": "Inquiry"')
else:
entity.append('"Status" : "Capability_Assessment"')
if (intent == 'Report'):
report_check = 1
entity.append('"Report":"1"')
entity.append('"Intensity" : "0"')
entity.append('"Percentile" : 0"')
elif (intent == 'Score'):
entity = main_entity(query, capability_entity[1], entity)
elif (intent == 'Graph'):
graph_check = 1
entity.append('"Graph":"1"')
entity.append('"Intensity" : "0"')
entity.append('"Percentile" : 0"')
else:
m = load_model(
'D:\\bot\\botapi\\botapi\\models\\Capability_Assessment\\capability_assessment.h5py'
)
score = model_score_LSTM_tokenize(query, capability_entity[0], m)
if ((score[0][0] > score[0][1]) & (score[0][0] > score[0][2]) &
(score[0][0] > score[0][3]) & (score[0][0] > score[0][4]) &
(score[0][0] > score[0][5])):
entity.append('"Status" : "Verbal_Interpretation"')
entity = main_entity(query, capability_entity[1], entity)
elif ((score[0][1] > score[0][0]) & (score[0][1] > score[0][2]) &
(score[0][1] > score[0][3]) & (score[0][1] > score[0][4]) &
(score[0][1] > score[0][5])):
entity.append('"Status" : "Numerical_Reasoning"')
entity = main_entity(query, capability_entity[1], entity)
#top_intent = 'numerical_reasoning'
elif ((score[0][2] > score[0][0]) & (score[0][2] > score[0][3]) &
(score[0][2] > score[0][3]) & (score[0][2] > score[0][4]) &
(score[0][2] > score[0][5])):
entity.append('"Status" : "Spatial_Visualization"')
entity = main_entity(query, capability_entity[1], entity)
#top_intent = 'spatial Visualization '
elif ((score[0][3] > score[0][0]) & (score[0][3] > score[0][2]) &
(score[0][3] > score[0][1]) & (score[0][3] > score[0][4]) &
(score[0][3] > score[0][5])):
entity.append('"Status" : "Overall"')
intent = overall(query, capability_entity[2])
if (intent == 'Inquiry'):
entity.append('"Status": "Inquiry"')
else:
entity.append('"Status" : "Capability_Assessment"')
if (intent == 'Report'):
report_check = 1
entity.append('"Report":"1"')
entity.append('"Intensity" : "0"')
entity.append('"Percentile" : 0"')
elif (intent == 'Score'):
entity = main_entity(query, capability_entity[1], entity)
elif (intent == 'Graph'):
graph_check = 1
entity.append('"Graph":"1"')
entity.append('"Intensity" : "0"')
entity.append('"Percentile" : 0"')
elif ((score[0][4] > score[0][0]) & (score[0][4] > score[0][2]) &
(score[0][4] > score[0][3]) & (score[0][4] > score[0][1]) &
(score[0][4] > score[0][5])):
entity.append('"Status" : "Perceptual_Speed"')
entity = main_entity(query, capability_entity[1], entity)
#top_intent = 'Perceptual Speed'
else:
entity.append('"Status" : "Verbal_Reasoning"')
entity = main_entity(query, capability_entity[1], entity)
#top_intent = 'verbal_reasoning'
Score = str(Score)
if (report_check == 0):
entity.append('"Report":"0"')
if (graph_check == 0):
entity.append('"Graph":"0"')
return ('{"TopIntent": ' + top_intent + ', "SubIntent": ' + sub_intent +
', "Percentage":' + Score, entity)
from keras.models import load_model
import pandas as pd
from functions import token_stems
from functions import tok_behavior_flex
from functions import model_score_LSTM_tokenize
data = pd.read_excel("D:\\virtual\\Org chart.xlsx")
text = data['Data']
x = []
for a in text:
x.append(token_stems(a))
tok = tok_behavior_flex(x)
m = load_model("C:\\Users\\shiza.abid\\Org_chart.h5py")
#FOR QUERY
query = "send me my organization chart"
score = model_score_LSTM_tokenize(query, tok, m)
if (score[0][0] > score[0][1]):
print("Org chart")
else:
print("JD")
print(score)