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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): | |
history = history or [] | |
if message.startswith("How many"): | |
response = random.randint(1, 10) | |
elif message.startswith("How"): | |
response = random.choice(["Great", "Good", "Okay", "Bad"]) | |
elif message.startswith("Where"): | |
response = random.choice(["Here", "There", "Somewhere"]) | |
else: | |
response = "I don't know" | |
history.append((message, response)) | |
return history, feedback(message) | |
def feedback(text): | |
# tokenized_phrases = grammar_tokenizer([text], return_tensors='pt', padding=True) | |
# corrections = grammar_model.generate(**tokenized_phrases) | |
# corrections = grammar_tokenizer.batch_decode(corrections, skip_special_tokens=True) | |
batch = grammar_tokenizer([input_text],truncation=True,padding='max_length',max_length=64, return_tensors="pt").to(torch_device) | |
corrections= grammar_model.generate(**batch,max_length=64,num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5) | |
print("The corrections are: ", corrections) | |
if len(corrections) == 0: | |
feedback = f'Looks good! Keep up the good work' | |
else: | |
suggestion = tokenizer.batch_decode(corrections[0], skip_special_tokens=True) | |
feedback = f'\'{suggestion}\' might be a little better' | |
return f'FEEDBACK: {feedback}' | |
iface = gr.Interface( | |
chat, | |
["text", "state"], | |
["chatbot", "text"], | |
allow_screenshot=False, | |
allow_flagging="never", | |
) | |
iface.launch() | |