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