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| from chatterbot import ChatBot | |
| import spacy | |
| spacy.cli.download("en_core_web_sm") | |
| spacy.cli.download("en") | |
| nlp = spacy.load('en_core_web_sm') | |
| chatbot = ChatBot( | |
| 'CoronaBot', | |
| storage_adapter='chatterbot.storage.SQLStorageAdapter', | |
| logic_adapters=[ | |
| 'chatterbot.logic.MathematicalEvaluation', | |
| 'chatterbot.logic.TimeLogicAdapter', | |
| 'chatterbot.logic.BestMatch', | |
| { | |
| 'import_path': 'chatterbot.logic.BestMatch', | |
| 'default_response': 'I am sorry, but I do not understand. I am still learning.', | |
| 'maximum_similarity_threshold': 0.90 | |
| } | |
| ], | |
| database_uri='sqlite:///database.sqlite3' | |
| ) | |
| # Training With Own Questions | |
| from chatterbot.trainers import ListTrainer | |
| trainer = ListTrainer(chatbot) | |
| training_data_quesans = open('training_data/ques_ans.txt').read().splitlines() | |
| training_data_personal = open('training_data/personal_ques.txt').read().splitlines() | |
| training_data = training_data_quesans + training_data_personal | |
| trainer.train(training_data) | |
| # Training With Corpus | |
| from chatterbot.trainers import ChatterBotCorpusTrainer | |
| trainer_corpus = ChatterBotCorpusTrainer(chatbot) | |
| trainer_corpus.train( | |
| 'chatterbot.corpus.english' | |
| ) | |