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()