def __init__(self, speaker=None, demo=False):
Thread.__init__(self)
self.go_on = True
self._logger = logging.getLogger('dialogs')
self._demo_mode = demo
if speaker:
self._speaker = speaker
else:
self._speaker = SpeakerIdentifier().get_current_speaker_id()
self._parser = Parser()
self._resolver = Resolver()
self._content_analyser = ContentAnalyser()
self._verbalizer = Verbalizer()
self._nl_input_queue = Queue()
self._sentence_output_queue = Queue()
#true when the Dialog module is within an interaction with a speaker
self.in_interaction = False
#the current set of sentences that dialog is dealing with
self.sentences = deque()
#the current sentence being worked on. Specifically, if active_sentence
#is set, the parser will _complete_ this sentence instead of creating a
#new one.
self.active_sentence = None
#true when, during an interaction, more infos is needed.
self.waiting_for_more_info = False
#contains the last result of a failed resolution (including the object
#that could not be resolved. Cf doc of UnsufficientInputError for details.
self._last_output = None
#contains the last result of a failed resolution of an anaphoric words (it, one) (including the object
#that could not be resolved. Cf doc of UnidentifiedAnaphoraError for details.
self._anaphora_input = None
#the ID of the speaker we are talking with. used to resolve references
#like 'me', 'you', etc.
self.current_speaker = None
#the set of the statements generated by the analysis of the last sentence.
# For testing purposes.
self.last_stmts_set = []
#last utterance outputed to user. This is a tuple containing both the
# Sentence object and the verbalized version.
self.last_sentence = (None, None)
def __init__(self, speaker=None, demo=False):
Thread.__init__(self)
self.go_on = True
self._logger = logging.getLogger("dialogs")
self._demo_mode = demo
if speaker:
self._speaker = speaker
else:
self._speaker = SpeakerIdentifier().get_current_speaker_id()
self._parser = Parser()
self._resolver = Resolver()
self._content_analyser = ContentAnalyser()
self._verbalizer = Verbalizer()
self._nl_input_queue = Queue()
self._sentence_output_queue = Queue()
# true when the Dialog module is within an interaction with a speaker
self.in_interaction = False
# the current set of sentences that dialog is dealing with
self.sentences = deque()
# the current sentence being worked on. Specifically, if active_sentence
# is set, the parser will _complete_ this sentence instead of creating a
# new one.
self.active_sentence = None
# true when, during an interaction, more infos is needed.
self.waiting_for_more_info = False
# contains the last result of a failed resolution (including the object
# that could not be resolved. Cf doc of UnsufficientInputError for details.
self._last_output = None
# contains the last result of a failed resolution of an anaphoric words (it, one) (including the object
# that could not be resolved. Cf doc of UnidentifiedAnaphoraError for details.
self._anaphora_input = None
# the ID of the speaker we are talking with. used to resolve references
# like 'me', 'you', etc.
self.current_speaker = None
# the set of the statements generated by the analysis of the last sentence.
# For testing purposes.
self.last_stmts_set = []
# last utterance outputed to user. This is a tuple containing both the
# Sentence object and the verbalized version.
self.last_sentence = (None, None)
from dialogs.parsing.parser import Parser
parser = Parser()
print """
<html>
<head>
<meta charset="UTF-8">
<title> DIALOGS parser demo</title>
<link rel ="stylesheet" type="text/css" href="styles.css" title="Style"></link>
<link rel="stylesheet" href="javascript/jquery.tooltip.css" />
<script src="javascript/lib/jquery.js" type="text/javascript"></script>
<script src="javscript/lib/jquery.bgiframe.js" type="text/javascript"></script>
<script src="javascript/lib/jquery.dimensions.js" type="text/javascript"></script>
<script src="javascript/jquery.tooltip.js" type="text/javascript"></script>
<script type="text/javascript">
$(function() {
$('.subject').tooltip({
track: true,
delay: 0,
showURL: false,
bodyHandler: function() { return "Subject";},
fade: 250
});
$('.indirect_object').tooltip({
track: true,
delay: 0,
showURL: false,
class Dialog(Thread):
"""The main Dialog class.
The speaker ID can be specified at initializtion by passing it to the
constructor.
"""
# This holds the history of conversation of all the instances of Dialog.
dialog_history = []
def __init__(self, speaker=None, demo=False):
Thread.__init__(self)
self.go_on = True
self._logger = logging.getLogger("dialogs")
self._demo_mode = demo
if speaker:
self._speaker = speaker
else:
self._speaker = SpeakerIdentifier().get_current_speaker_id()
self._parser = Parser()
self._resolver = Resolver()
self._content_analyser = ContentAnalyser()
self._verbalizer = Verbalizer()
self._nl_input_queue = Queue()
self._sentence_output_queue = Queue()
# true when the Dialog module is within an interaction with a speaker
self.in_interaction = False
# the current set of sentences that dialog is dealing with
self.sentences = deque()
# the current sentence being worked on. Specifically, if active_sentence
# is set, the parser will _complete_ this sentence instead of creating a
# new one.
self.active_sentence = None
# true when, during an interaction, more infos is needed.
self.waiting_for_more_info = False
# contains the last result of a failed resolution (including the object
# that could not be resolved. Cf doc of UnsufficientInputError for details.
self._last_output = None
# contains the last result of a failed resolution of an anaphoric words (it, one) (including the object
# that could not be resolved. Cf doc of UnidentifiedAnaphoraError for details.
self._anaphora_input = None
# the ID of the speaker we are talking with. used to resolve references
# like 'me', 'you', etc.
self.current_speaker = None
# the set of the statements generated by the analysis of the last sentence.
# For testing purposes.
self.last_stmts_set = []
# last utterance outputed to user. This is a tuple containing both the
# Sentence object and the verbalized version.
self.last_sentence = (None, None)
def run(self):
while self.go_on:
try:
# block with a (short) timeout to prevent maxing the CPU
input = self._nl_input_queue.get(True, 0.1).strip()
ResourcePool().mark_active(self._speaker)
self._logger.info(colored_print("\n-------[ NL INPUT ]-------\n", "green"))
self._logger.info(colored_print("- " + input + "\n", "blue"))
self.in_interaction = True
try:
self._process(input)
except UnsufficientInputError as uie:
self._logger.info(colored_print("Not enough informations! Going back to human:", "magenta"))
self._last_output = uie.value
# Output towards human
sys.stdout.write(self._verbalizer.verbalize(uie.value["question"]) + "\n")
sys.stdout.flush()
self._logger.info(
colored_print("- " + self._verbalizer.verbalize(uie.value["question"]), "blue") + "\n"
)
# Waiting for more info to solve content
self.waiting_for_more_info = True
except InterruptedInteractionError as iie:
self._logger.info(colored_print("I can not continue. I interrupt the interaction.", "magenta"))
# Output towards human
sys.stdout.write(self._verbalizer.verbalize(iie.value) + "\n")
sys.stdout.flush()
self._logger.info(colored_print("- " + self._verbalizer.verbalize(iie.value), "blue") + "\n")
except UnidentifiedAnaphoraError as uae:
self._logger.info(
colored_print("Not sure of the interpretation... Asking for confirmation to human:", "magenta")
)
self._anaphora_input = uae.value
# waiting for more info to solve anaphora
self.waiting_for_more_info = True
# Output towards human
sys.stdout.write(self._verbalizer.verbalize(uae.value["question"]) + "\n")
sys.stdout.flush()
self._logger.info(
"- " + colored_print(self._verbalizer.verbalize(uae.value["question"]), "blue") + "\n"
)
except GrammaticalError as ge:
self._logger.info(
colored_print(
"Grammatical error! "
+ "I can not correctly parse the sentence. Asking "
+ "the speaker to reformulate",
"magenta",
)
)
# waiting for more info to solve anaphora
self.waiting_for_more_info = True
# Output towards human
sys.stdout.write("I didn't really understand your last sentence. Could you reformulate?\n")
sys.stdout.flush()
except DialogError as de:
self._logger.info(colored_print("Error! " + de.value, "magenta"))
# Output towards human
emotions.confused()
sys.stdout.write("Sorry, I'm confused... Could you repeat in a different way?\n")
sys.stdout.flush()
except KbError as ke:
self._logger.info(colored_print("Error! " + ke.value, "magenta"))
# Output towards human
emotions.confused()
sys.stdout.write("Sorry, I'm confused... My knowledge looks somehow broken...\n")
sys.stdout.flush()
# interrupt the interaction
self.in_interaction = False
except Empty:
pass
try:
output = self._sentence_output_queue.get(block=False)
self._logger.info(colored_print("> Returning output: ", "bold"))
nl_output = self._verbalizer.verbalize(output)
sys.stdout.write(nl_output + "\n")
sys.stdout.flush()
self._logger.info("- " + colored_print(nl_output, "blue") + "\n")
except Empty:
pass
def stop(self):
while not self._nl_input_queue.empty():
pass
self.go_on = False
def input(self, input, speaker=None):
input = input.decode("utf-8").encode("ascii")
if speaker:
self.current_speaker = speaker
else:
self.current_speaker = self._speaker
self._nl_input_queue.put(input)
def test(self, speaker, input, answer=None):
"""This method eases the testing of dialog by returning only when a
sentence is completely processed.
The optional 'answer' argument is used to answer a request for more
details from the resolution code.
The method returns a tuple containing the last produced statements (if
relevant) and the last sentence produced (as a tuple of (Sentence,
verbalized sentence), is relevant).
"""
import time
self.in_interaction = True
self.input(input, speaker)
while self.in_interaction:
if self.waiting_for_more_info:
if answer:
self._logger.debug(colored_print("> Automatically answering: ", "bold"))
self._logger.info(colored_print(answer, "red"))
self.input(answer, speaker)
answer = None
else:
return [], self.last_sentence
time.sleep(0.1)
return self.last_stmts_set, self.last_sentence
def _process(self, nl_input):
# Parsing
self._logger.info(colored_print("\n-------[ PREPROCESSING ]-------\n", "green"))
preprocessed_sentences = self._parser.preprocess(nl_input, None)
for s in preprocessed_sentences:
self._logger.debug(s)
self._logger.info(colored_print("\n-------[ PARSING ]-------\n", "green"))
parsed_sentences = self._parser.parse(preprocessed_sentences, None)
for s in parsed_sentences:
self._logger.debug(str(s))
if self._demo_mode:
wait_for_keypress()
# Unsufficient input or unidentified anaphora Error processing
for s in parsed_sentences:
# Check the sentence is grammatically valid, else go back to the human
if not s.isvalid():
raise GrammaticalError(s)
if s.isaborting():
parsed_sentences.remove(s)
self._last_output = self._anaphora_input = None
self.waiting_for_more_info = False
sys.stdout.write("Alright. Forgotten!\n")
sys.stdout.flush()
self._logger.info("- " + colored_print("Alright. Forgotten!", "blue") + "\n")
break
if s.islearning() and self._last_output:
s.append_sub_sentence(self._last_output["sentence"])
self._last_output = None
self.waiting_for_more_info = False
break
if self.waiting_for_more_info:
self._logger.info(colored_print("Waiting for more information activated\n", "magenta"))
# Processing Unsifficient input Error with sentences remerge
if self._last_output:
self._logger.info(
colored_print("New content provided by human! merging it with the previous sentence.", "magenta")
)
self._last_output["object_with_more_info"] = nominal_group_remerge(
parsed_sentences, self._last_output["status"], self._last_output["object"]
)
# current_sentence that is to replace if it is to answer unsificient input
parsed_sentences = [self._last_output["sentence"]]
# Processing Unidentified Anaphora Error with sentences replacement
if self._anaphora_input:
self._logger.info(colored_print("Ok. Got a confirmation. Processing it.", "magenta"))
self._anaphora_input["object_with_more_info"] = replacement(
parsed_sentences,
self._anaphora_input["object"],
self._anaphora_input["objects_list"][1:],
self._anaphora_input["object_to_confirm"],
)
# current_sentence that is to replace if it is to answer unidentified anaphora
parsed_sentences = [self._anaphora_input["sentence"]]
# No more info needed
self.waiting_for_more_info = False
# end of Unsufficient input or unidentified anaphora Error processing
# Sentences Queue()
[self.sentences.append(stnts) for stnts in parsed_sentences]
for s in range(len(self.sentences)): # sentences is a deque. Cannot do a simple [:] to iterate over a copy
self.active_sentence = self.sentences.popleft()
# Resolution
self._resolver.sentences_store = Dialog.dialog_history
uie_object = self._last_output["object"] if self._last_output else None
uie_object_with_more_info = self._last_output["object_with_more_info"] if uie_object else None
self._last_output = None
uae_object = self._anaphora_input["object"] if self._anaphora_input else None
uae_object_with_more_info = self._anaphora_input["object_with_more_info"] if uae_object else None
uae_object_list = self._anaphora_input["objects_list"] if uae_object else None
self._anaphora_input = None
self._logger.info(colored_print("\n-------[ RESOLVING SENTENCE ]-------\n", "green"))
self.active_sentence = self._resolver.references_resolution(
self.active_sentence, self.current_speaker, uae_object, uae_object_with_more_info, uae_object_list
)
self.active_sentence = self._resolver.noun_phrases_resolution(
self.active_sentence, self.current_speaker, uie_object, uie_object_with_more_info
)
self.active_sentence = self._resolver.verbal_phrases_resolution(self.active_sentence)
self._logger.info(colored_print("\n[ Sentence after resolution ]", "green"))
self._logger.info(str(self.active_sentence))
if self._demo_mode:
wait_for_keypress()
# Content analysis
self._logger.info(colored_print("\n-------[ CONTENT ANALYSIS ]-------\n", "green"))
self.last_stmts_set, self.current_situation_id = self._content_analyser.analyse(
self.active_sentence, self.current_speaker
)
if self._demo_mode:
wait_for_keypress()
# Verbalization
if self._content_analyser.analyse_output():
self._logger.info(colored_print("\n-------[ VERBALIZATION ]-------\n", "green"))
self.last_sentence = (
self._content_analyser.analyse_output(),
self._verbalizer.verbalize(self._content_analyser.analyse_output()),
)
if self.current_situation_id:
self._logger.debug(
colored_print("Answer: <%s>: Sent to knowledge base." % self.last_sentence[1], "magenta")
)
self._logger.debug("Up to the robot controller now!")
ResourcePool().ontology_server.revise(
['%s verbalisesTo "%s"' % (self.current_situation_id, self.last_sentence[1])],
{"method": "add", "models": [ResourcePool().default_model]},
)
else:
self._sentence_output_queue.put(self.last_sentence[0])
if self._demo_mode:
wait_for_keypress()
else:
self._logger.info(colored_print("\nNothing to verbalize!", "magenta"))
self.last_sentence = (None, "")
# Dialog History
self._logger.debug(colored_print("Sentence and answer saved in history.", "magenta"))
Dialog.dialog_history.append(self.active_sentence)
if self.last_sentence[0]:
Dialog.dialog_history.extend(self.last_sentence[0])
# Finalizing the processing
self.active_sentence = None
self._logger.info(colored_print('\n[ NL sentence "' + nl_input + '" processed! ]', "green"))
if self.last_sentence[0] and self.last_sentence[0][0].data_type == END:
# Exit!
self.stop()
self.in_interaction = False
class Dialog(Thread):
"""The main Dialog class.
The speaker ID can be specified at initializtion by passing it to the
constructor.
"""
#This holds the history of conversation of all the instances of Dialog.
dialog_history = []
def __init__(self, speaker=None, demo=False):
Thread.__init__(self)
self.go_on = True
self._logger = logging.getLogger('dialogs')
self._demo_mode = demo
if speaker:
self._speaker = speaker
else:
self._speaker = SpeakerIdentifier().get_current_speaker_id()
self._parser = Parser()
self._resolver = Resolver()
self._content_analyser = ContentAnalyser()
self._verbalizer = Verbalizer()
self._nl_input_queue = Queue()
self._sentence_output_queue = Queue()
#true when the Dialog module is within an interaction with a speaker
self.in_interaction = False
#the current set of sentences that dialog is dealing with
self.sentences = deque()
#the current sentence being worked on. Specifically, if active_sentence
#is set, the parser will _complete_ this sentence instead of creating a
#new one.
self.active_sentence = None
#true when, during an interaction, more infos is needed.
self.waiting_for_more_info = False
#contains the last result of a failed resolution (including the object
#that could not be resolved. Cf doc of UnsufficientInputError for details.
self._last_output = None
#contains the last result of a failed resolution of an anaphoric words (it, one) (including the object
#that could not be resolved. Cf doc of UnidentifiedAnaphoraError for details.
self._anaphora_input = None
#the ID of the speaker we are talking with. used to resolve references
#like 'me', 'you', etc.
self.current_speaker = None
#the set of the statements generated by the analysis of the last sentence.
# For testing purposes.
self.last_stmts_set = []
#last utterance outputed to user. This is a tuple containing both the
# Sentence object and the verbalized version.
self.last_sentence = (None, None)
def run(self):
while self.go_on:
try:
# block with a (short) timeout to prevent maxing the CPU
input = self._nl_input_queue.get(True, 0.1).strip()
ResourcePool().mark_active(self._speaker)
self._logger.info(
colored_print("\n-------[ NL INPUT ]-------\n",
'green'))
self._logger.info(colored_print("- " + input + "\n", 'blue'))
self.in_interaction = True
try:
self._process(input)
except UnsufficientInputError as uie:
self._logger.info(
colored_print(
"Not enough informations! Going back to human:",
'magenta'))
self._last_output = uie.value
#Output towards human
sys.stdout.write(
self._verbalizer.verbalize(uie.value['question']) +
"\n")
sys.stdout.flush()
self._logger.info(
colored_print(
"- " + self._verbalizer.verbalize(
uie.value['question']), 'blue') + "\n")
#Waiting for more info to solve content
self.waiting_for_more_info = True
except InterruptedInteractionError as iie:
self._logger.info(
colored_print(
"I can not continue. I interrupt the interaction.",
'magenta'))
#Output towards human
sys.stdout.write(
self._verbalizer.verbalize(iie.value) + "\n")
sys.stdout.flush()
self._logger.info(
colored_print(
"- " +
self._verbalizer.verbalize(iie.value), 'blue') +
"\n")
except UnidentifiedAnaphoraError as uae:
self._logger.info(
colored_print(
"Not sure of the interpretation... Asking for confirmation to human:",
'magenta'))
self._anaphora_input = uae.value
#waiting for more info to solve anaphora
self.waiting_for_more_info = True
#Output towards human
sys.stdout.write(
self._verbalizer.verbalize(uae.value['question']) +
"\n")
sys.stdout.flush()
self._logger.info("- " + colored_print(
self._verbalizer.verbalize(uae.value['question']),
'blue') + "\n")
except GrammaticalError as ge:
self._logger.info(colored_print("Grammatical error! " + \
"I can not correctly parse the sentence. Asking " + \
"the speaker to reformulate", 'magenta'))
#waiting for more info to solve anaphora
self.waiting_for_more_info = True
#Output towards human
sys.stdout.write(
"I didn't really understand your last sentence. Could you reformulate?\n"
)
sys.stdout.flush()
except DialogError as de:
self._logger.info(
colored_print("Error! " + de.value, 'magenta'))
#Output towards human
emotions.confused()
sys.stdout.write(
"Sorry, I'm confused... Could you repeat in a different way?\n"
)
sys.stdout.flush()
except KbError as ke:
self._logger.info(
colored_print("Error! " + ke.value, 'magenta'))
#Output towards human
emotions.confused()
sys.stdout.write(
"Sorry, I'm confused... My knowledge looks somehow broken...\n"
)
sys.stdout.flush()
# interrupt the interaction
self.in_interaction = False
except Empty:
pass
try:
output = self._sentence_output_queue.get(block=False)
self._logger.info(colored_print("> Returning output: ",
'bold'))
nl_output = self._verbalizer.verbalize(output)
sys.stdout.write(nl_output + "\n")
sys.stdout.flush()
self._logger.info("- " + colored_print(nl_output, 'blue') +
"\n")
except Empty:
pass
def stop(self):
while not self._nl_input_queue.empty():
pass
self.go_on = False
def input(self, input, speaker=None):
input = input.decode('utf-8').encode('ascii')
if speaker:
self.current_speaker = speaker
else:
self.current_speaker = self._speaker
self._nl_input_queue.put(input)
def test(self, speaker, input, answer=None):
"""This method eases the testing of dialog by returning only when a
sentence is completely processed.
The optional 'answer' argument is used to answer a request for more
details from the resolution code.
The method returns a tuple containing the last produced statements (if
relevant) and the last sentence produced (as a tuple of (Sentence,
verbalized sentence), is relevant).
"""
import time
self.in_interaction = True
self.input(input, speaker)
while self.in_interaction:
if self.waiting_for_more_info:
if answer:
self._logger.debug(
colored_print("> Automatically answering: ", 'bold'))
self._logger.info(colored_print(answer, 'red'))
self.input(answer, speaker)
answer = None
else:
return [], self.last_sentence
time.sleep(0.1)
return self.last_stmts_set, self.last_sentence
def _process(self, nl_input):
#Parsing
self._logger.info(
colored_print("\n-------[ PREPROCESSING ]-------\n",
'green'))
preprocessed_sentences = self._parser.preprocess(nl_input, None)
for s in preprocessed_sentences:
self._logger.debug(s)
self._logger.info(
colored_print("\n-------[ PARSING ]-------\n",
'green'))
parsed_sentences = self._parser.parse(preprocessed_sentences, None)
for s in parsed_sentences:
self._logger.debug(str(s))
if self._demo_mode:
wait_for_keypress()
#Unsufficient input or unidentified anaphora Error processing
for s in parsed_sentences:
#Check the sentence is grammatically valid, else go back to the human
if not s.isvalid():
raise GrammaticalError(s)
if s.isaborting():
parsed_sentences.remove(s)
self._last_output = self._anaphora_input = None
self.waiting_for_more_info = False
sys.stdout.write("Alright. Forgotten!\n")
sys.stdout.flush()
self._logger.info(
"- " + colored_print("Alright. Forgotten!", 'blue') + "\n")
break
if s.islearning() and self._last_output:
s.append_sub_sentence(self._last_output['sentence'])
self._last_output = None
self.waiting_for_more_info = False
break
if self.waiting_for_more_info:
self._logger.info(
colored_print("Waiting for more information activated\n",
'magenta'))
#Processing Unsifficient input Error with sentences remerge
if self._last_output:
self._logger.info(
colored_print(
"New content provided by human! merging it with the previous sentence.",
'magenta'))
self._last_output[
'object_with_more_info'] = nominal_group_remerge(
parsed_sentences, self._last_output['status'],
self._last_output['object'])
#current_sentence that is to replace if it is to answer unsificient input
parsed_sentences = [self._last_output['sentence']]
#Processing Unidentified Anaphora Error with sentences replacement
if self._anaphora_input:
self._logger.info(
colored_print("Ok. Got a confirmation. Processing it.",
'magenta'))
self._anaphora_input['object_with_more_info'] = replacement(
parsed_sentences, self._anaphora_input['object'],
self._anaphora_input['objects_list'][1:],
self._anaphora_input['object_to_confirm'])
#current_sentence that is to replace if it is to answer unidentified anaphora
parsed_sentences = [self._anaphora_input['sentence']]
#No more info needed
self.waiting_for_more_info = False
#end of Unsufficient input or unidentified anaphora Error processing
#Sentences Queue()
[self.sentences.append(stnts) for stnts in parsed_sentences]
for s in range(
len(self.sentences)
): #sentences is a deque. Cannot do a simple [:] to iterate over a copy
self.active_sentence = self.sentences.popleft()
#Resolution
self._resolver.sentences_store = Dialog.dialog_history
uie_object = self._last_output[
'object'] if self._last_output else None
uie_object_with_more_info = self._last_output[
'object_with_more_info'] if uie_object else None
self._last_output = None
uae_object = self._anaphora_input[
'object'] if self._anaphora_input else None
uae_object_with_more_info = self._anaphora_input[
'object_with_more_info'] if uae_object else None
uae_object_list = self._anaphora_input[
'objects_list'] if uae_object else None
self._anaphora_input = None
self._logger.info(
colored_print("\n-------[ RESOLVING SENTENCE ]-------\n",
'green'))
self.active_sentence = self._resolver.references_resolution(
self.active_sentence, self.current_speaker, uae_object,
uae_object_with_more_info, uae_object_list)
self.active_sentence = self._resolver.noun_phrases_resolution(
self.active_sentence, self.current_speaker, uie_object,
uie_object_with_more_info)
self.active_sentence = self._resolver.verbal_phrases_resolution(
self.active_sentence)
self._logger.info(
colored_print("\n[ Sentence after resolution ]", 'green'))
self._logger.info(str(self.active_sentence))
if self._demo_mode:
wait_for_keypress()
#Content analysis
self._logger.info(
colored_print("\n-------[ CONTENT ANALYSIS ]-------\n",
'green'))
self.last_stmts_set, self.current_situation_id = self._content_analyser.analyse(
self.active_sentence, self.current_speaker)
if self._demo_mode:
wait_for_keypress()
#Verbalization
if self._content_analyser.analyse_output():
self._logger.info(
colored_print("\n-------[ VERBALIZATION ]-------\n",
'green'))
self.last_sentence = (
self._content_analyser.analyse_output(),
self._verbalizer.verbalize(
self._content_analyser.analyse_output()))
if self.current_situation_id:
self._logger.debug(
colored_print(
"Answer: <%s>: Sent to knowledge base." %
self.last_sentence[1], 'magenta'))
self._logger.debug("Up to the robot controller now!")
ResourcePool().ontology_server.revise(
[
'%s verbalisesTo "%s"' %
(self.current_situation_id, self.last_sentence[1])
], {
"method": "add",
"models": [ResourcePool().default_model]
})
else:
self._sentence_output_queue.put(self.last_sentence[0])
if self._demo_mode:
wait_for_keypress()
else:
self._logger.info(
colored_print("\nNothing to verbalize!", 'magenta'))
self.last_sentence = (None, "")
#Dialog History
self._logger.debug(
colored_print("Sentence and answer saved in history.",
'magenta'))
Dialog.dialog_history.append(self.active_sentence)
if self.last_sentence[0]:
Dialog.dialog_history.extend(self.last_sentence[0])
#Finalizing the processing
self.active_sentence = None
self._logger.info(
colored_print("\n[ NL sentence \"" + nl_input + "\" processed! ]",
'green'))
if self.last_sentence[0] and self.last_sentence[0][0].data_type == END:
# Exit!
self.stop()
self.in_interaction = False
from dialogs.parsing.parser import Parser
parser = Parser()
print """
<html>
<head>
<meta charset="UTF-8">
<title> DIALOGS parser demo</title>
<link rel ="stylesheet" type="text/css" href="styles.css" title="Style"></link>
<link rel="stylesheet" href="javascript/jquery.tooltip.css" />
<script src="javascript/lib/jquery.js" type="text/javascript"></script>
<script src="javscript/lib/jquery.bgiframe.js" type="text/javascript"></script>
<script src="javascript/lib/jquery.dimensions.js" type="text/javascript"></script>
<script src="javascript/jquery.tooltip.js" type="text/javascript"></script>
<script type="text/javascript">
$(function() {
$('.subject').tooltip({
track: true,
delay: 0,
showURL: false,
bodyHandler: function() { return "Subject";},
fade: 250
});
$('.indirect_object').tooltip({
track: true,
delay: 0,
showURL: false,
