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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, 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)
print("The corrections are: ", corrections)
if len(corrections) == 0:
feedback = f'Looks good! Keep up the good work'
else:
suggestion = grammar_tokenizer.batch_decode(corrections[0], clean_up_tokenization_spaces=True, skip_special_tokens=True)
feedback = f'\'{"".join(suggestion)}\' might be a little better'
return feedback
iface = gr.Interface(
chat,
["text", "state"],
["chatbot", "state", "text"],
allow_screenshot=False,
allow_flagging="never",
)
iface.launch()
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