|
|
# -*- coding: iso-8859-1 -*-
|
|
|
import random
|
|
|
import json
|
|
|
import pickle
|
|
|
import numpy as np
|
|
|
|
|
|
import nltk
|
|
|
from nltk.stem import WordNetLemmatizer
|
|
|
|
|
|
from tensorflow.keras.models import load_model
|
|
|
|
|
|
# Vorlage Code von NeuralNines YT Channel (https://www.youtube.com/watch?v=1lwddP0KUEg&t=1022s)
|
|
|
|
|
|
|
|
|
class Osiris_Sprache:
|
|
|
"""Osiris Sprach Tools
|
|
|
|
|
|
Enth<74>lt alle Funktionen zur Handhabung und Verarbeitung von Input Messages
|
|
|
|
|
|
Returns:
|
|
|
None
|
|
|
"""
|
|
|
|
|
|
lematizer: WordNetLemmatizer
|
|
|
intents: json
|
|
|
|
|
|
words: None
|
|
|
classes: None
|
|
|
model: None
|
|
|
|
|
|
def __init__(self):
|
|
|
self.lematizer = WordNetLemmatizer()
|
|
|
self.intents = json.loads(open('intents.json').read())
|
|
|
|
|
|
self.words = pickle.load(open('words.pkl', 'rb'))
|
|
|
self.classes = pickle.load(open('classes.pkl', 'rb'))
|
|
|
self.model = load_model('osiris_sprachmodel.h5')
|
|
|
|
|
|
def clean_up_sentence(self, sentence):
|
|
|
sentence_words = nltk.word_tokenize(sentence)
|
|
|
sentence_words = [self.lematizer.lemmatize(
|
|
|
word) for word in sentence_words]
|
|
|
return sentence_words
|
|
|
|
|
|
def bag_of_words(self, sentence):
|
|
|
sentence_words = self.clean_up_sentence(sentence)
|
|
|
bag = [0] * len(self.words)
|
|
|
for w in sentence_words:
|
|
|
for i, word in enumerate(self.words):
|
|
|
if word == w:
|
|
|
bag[i] = 1
|
|
|
return np.array(bag)
|
|
|
|
|
|
def predict_class(self, sentence):
|
|
|
bagOfWords = self.bag_of_words(sentence)
|
|
|
res = self.model.predict(np.array([bagOfWords]))[0]
|
|
|
ERROR_THRESHOLD = 0.25
|
|
|
results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
|
|
|
|
|
|
results.sort(key=lambda x: x[1], reverse=True)
|
|
|
return_list = []
|
|
|
for r in results:
|
|
|
return_list.append(
|
|
|
{'intent': self.classes[r[0]], 'probability': str(r[1])})
|
|
|
return return_list
|
|
|
|
|
|
def get_response(self, intent_list):
|
|
|
tag = intent_list[0]['intent']
|
|
|
list_of_intents = self.intents['intents']
|
|
|
for intent in list_of_intents:
|
|
|
if intent['tag'] == tag:
|
|
|
result = random.choice(i['response'])
|
|
|
break
|
|
|
return result
|
|
|
|
|
|
#print('Du kannst jetzt mit mir Reden!')
|
|
|
|
|
|
# while True:
|
|
|
# message = input("")
|
|
|
# ints = predict_class(message)
|
|
|
# res = get_response(ints, intents)
|
|
|
# print(res)
|