import torch
import gradio as gr
from googletrans import Translator
from transformers import T5Tokenizer
from transformers import T5ForConditionalGeneration
from transformers import BartForConditionalGeneration
from transformers import BartTokenizer
from transformers import PreTrainedModel
from transformers import PreTrainedTokenizer

tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')


# Question launcher
class E2EQGPipeline:
    def __init__(
        self,
        model: PreTrainedModel,
        tokenizer: PreTrainedTokenizer
    ):

        self.device = "cuda" if torch.cuda.is_available() else "cpu"

        self.model = model
        self.tokenizer = tokenizer

        self.model_type = "t5"

        self.kwargs = {
            "max_length": 256,
            "num_beams": 4,
            "length_penalty": 1.5,
            "no_repeat_ngram_size": 3,
            "early_stopping": True,
        }

    def generate_questions(self, context: str):
        inputs = self._prepare_inputs_for_e2e_qg(context)

        outs = self.model.generate(
            input_ids=inputs['input_ids'].to(self.device),
            attention_mask=inputs['attention_mask'].to(self.device),
            **self.kwargs
        )

        prediction = self.tokenizer.decode(outs[0], skip_special_tokens=True)

        questions = prediction.split("<sep>")
        questions = [question.strip() for question in questions[:-1]]
        return questions

    def _prepare_inputs_for_e2e_qg(self, context):
        source_text = f"generate questions: {context}"

        inputs = self._tokenize([source_text], padding=False)

        return inputs

    def _tokenize(
        self,
        inputs,
        padding=True,
        truncation=True,
        add_special_tokens=True,
        max_length=512
    ):
        inputs = self.tokenizer.batch_encode_plus(
            inputs,
            max_length=max_length,
            add_special_tokens=add_special_tokens,
            truncation=truncation,
            padding="max_length" if padding else False,
            pad_to_max_length=padding,
            return_tensors="pt"
        )

        return inputs


def generate_questions(text):
    qg_model = T5ForConditionalGeneration.from_pretrained('valhalla/t5-base-e2e-qg')
    qg_tokenizer = T5Tokenizer.from_pretrained('valhalla/t5-base-e2e-qg')
    qg_final_model = E2EQGPipeline(qg_model, qg_tokenizer)
    questions = qg_final_model.generate_questions(text)
    translator = Translator()
    translated_questions = [translator.translate(question, dest='es').text for question in questions]
    return translated_questions


def generate_summary(text):
    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
    summary_ids = model.generate(inputs, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary


def process(text):
    return generate_summary(text), generate_questions(text)


textbox_input = gr.Textbox(label="Pega el text aca:", placeholder="Texto...", lines=15)
summary_output = gr.Textbox(label="Resumen", lines=15)
questions_output = gr.Textbox(label="Preguntas", lines=5)


demo = gr.Interface(fn=process, inputs=textbox_input, outputs=[summary_output, questions_output])
demo.launch()