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| from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration, T5Tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") | |
| grammar_tokenizer = T5Tokenizer.from_pretrained('deep-learning-analytics/GrammarCorrector') | |
| grammar_model = T5ForConditionalGeneration.from_pretrained('deep-learning-analytics/GrammarCorrector') | |
| import torch | |
| import gradio as gr | |
| def chat(message, history=[]): | |
| new_user_input_ids = tokenizer.encode(message+tokenizer.eos_token, return_tensors='pt') | |
| if len(history) > 0: | |
| last_set_of_ids = history[len(history)-1][2] | |
| bot_input_ids = torch.cat([last_set_of_ids, new_user_input_ids], dim=-1) | |
| else: | |
| bot_input_ids = new_user_input_ids | |
| chat_history_ids = model.generate(bot_input_ids, max_length=5000, pad_token_id=tokenizer.eos_token_id) | |
| response_ids = chat_history_ids[:, bot_input_ids.shape[-1]:][0] | |
| response = tokenizer.decode(response_ids, skip_special_tokens=True) | |
| history.append((message, response, chat_history_ids)) | |
| return history, history, feedback(message) | |
| def feedback(text): | |
| num_return_sequences=1 | |
| batch = grammar_tokenizer([text],truncation=True,padding='max_length',max_length=64, return_tensors="pt") | |
| corrections = grammar_model.generate(**batch,max_length=64,num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5) | |
| corrected_text = grammar_tokenizer.decode(corrections[0], clean_up_tokenization_spaces=True, skip_special_tokens=True) | |
| print("The corrected text is: ", corrected_text) | |
| print("The orig text is: ", text) | |
| if corrected_text.rstrip('.') == text.rstrip('.'): | |
| # if corrected_text == text: | |
| feedback = f'Looks good! Keep up the good work' | |
| else: | |
| feedback = f'\'{corrected_text}\' might be a little better' | |
| return feedback | |
| title = "A chatbot that provides grammar feedback" | |
| description = "A quick proof of concept using Gradio" | |
| article = "<p style='text-align: center'><a href='https://docs.google.com/presentation/d/11fiO91MKZVgNoQJh5pn3Tw8-inHe6XbWYB2r1f701WI/edit?usp=sharing'> A conversational agent for Language learning</a> | <a href='https://github.com/ConorNugent/gradio-chatbot-demo'>Github Repo</a></p>" | |
| examples = [ | |
| ["Have you read the play what I wrote?"], | |
| ["Were do you live?"], | |
| ] | |
| iface = gr.Interface( | |
| chat, | |
| [gr.Textbox(label="Send messages here"), "state"], | |
| [gr.Chatbot(label='Conversation'), "state", gr.Textbox( | |
| label="Feedback", | |
| lines=1 | |
| )], | |
| allow_screenshot=False, | |
| allow_flagging="never", | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples) | |
| iface.launch() | |