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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() | |