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import sys
import json
import spacy
import pickle
import random
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as text
from sklearn.preprocessing import LabelBinarizer
def print_my_examples(inputs, results):
result_for_printing = \
[f'input: {inputs[i]:<30} : estimated intent: {results[i]}'
for i in range(len(inputs))]
print(*result_for_printing, sep='\n')
print()
examples = [
'play a song from U2', # this is the same sentence tried earlier
'Will it rain tomorrow',
'I like to hear greatist hits from beastie boys',
'I like to book a table for 3 persons',
'5 stars for machines like me',
'play a boogie wit da hoodie',
"play Bob's favorite song",
"give me a hug",
"hello"
]
greetings = ["Greeting","smalltalk_greetings_hello"]
courtesy_greeting = ["CourtesyGreeting"]
stored_name = "Amari"
examples = ["hello"]
#nlp = spacy.load("en_core_web_sm")
classifier_model = tf.keras.models.load_model('caesarmodel/caesarnl.h5',custom_objects={'KerasLayer':hub.KerasLayer})
# Show the model architecture
results = tf.nn.softmax(classifier_model(tf.constant(examples)))
with open("caesarmodel/labelbinarizer.pkl","rb") as f:
binarizer = pickle.load(f)
intents=binarizer.inverse_transform(results.numpy())
with open("intentdata/responses.json","r") as f:
responses = json.load(f)["responses"]
if intents[0] in greetings:
greetresponse = random.choice(responses["Greeting"]).replace("<HUMAN>",stored_name)
print(greetresponse)
#sentence_intents = dict(zip(examples,intents))
#print(sentence_intents)
#print_my_examples(examples, intents)
# TODO AIM - Implement Chatbot Gossip to Caesar
# 1. Add data to datasets train | valid | test
# a. then clean labels
# 2. Augment data to provide more potential possibilites
# 3. Use BERT to match input with the response
# Command Labels - AddToPlaylist | GetWeather -> API -> user
# Conversation Labes - Greeting | Goodbye -> BERTNN: input:"hello" => response:"hi there, I am caesar" -> user
# TODO AIM - Single names of songs artists like "play a boogie" and it will play a boogie's music.
# 1. Idea one - NER detect the named entities
# 2. Create new Neural Network that detects that. * Have to determine the relationship between the entites
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