from transformers import AutoModelWithLMHead, AutoTokenizer
import gradio as grad

# make a question
# text2text_tkn = AutoTokenizer.from_pretrained('mrm8488/t5-base-finetuned-question-generation-ap')
# mdl = AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-question-generation-ap')

# summarize
text2text_tkn = AutoTokenizer.from_pretrained('deep-learning-analytics/wikihow-t5-small')
mdl = AutoModelWithLMHead.from_pretrained('deep-learning-analytics/wikihow-t5-small')

def text2text(context, answer):
    input_text = "answer: %s context: %s </s>" % (answer, context)
    features = text2text_tkn([input_text], return_tensors = 'pt')
    output = mdl.generate(
        input_ids = features['input_ids'],
        attention_mask = features['attention_mask'],
        max_length = 64
    )
    response = text2text_tkn.decode(output[0])

    return response

def text2text_summary(para):
    initial_txt = para.strip().replace("\n", "")
    tkn_text = text2text_tkn.encode(initial_txt, return_tensors = 'pt')
    tkn_ids = mdl.generate(
        tkn_text,
        max_length = 250,
        num_beams = 5,
        repetition_penalty = 2.5,
        early_stopping = True
    )

    response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens = True)

    return response

# context = grad.Textbox(lines = 10, label = 'English', placeholder = 'Context')
# ans = grad.Textbox(lines = 1, label = 'Answer')
# out = grad.Textbox(lines = 1, label = 'Generated Question')

para = grad.Textbox(lines = 10, label = 'Paragraph', placeholder = 'Copy paragraph')
out = grad.Textbox(lines = 1, label = 'Summary')

grad.Interface(
   # text2text,
    # inputs = [context, ans],
    text2text_summary,
    inputs = para,
    outputs = out
).launch()