import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

tokenizer = AutoTokenizer.from_pretrained(
    "prithivida/parrot_paraphraser_on_T5", use_auth_token=os.environ["AUTH_TOKEN"])
model = AutoModelForSeq2SeqLM.from_pretrained(
    "prithivida/parrot_paraphraser_on_T5", use_auth_token=os.environ["AUTH_TOKEN"])

pln_es_to_en = pipeline('translation_es_to_en',
                        model=AutoModelForSeq2SeqLM.from_pretrained(
                            'Helsinki-NLP/opus-mt-es-en'),
                        tokenizer=AutoTokenizer.from_pretrained(
                            'Helsinki-NLP/opus-mt-es-en')
                        )

pln_en_to_es = pipeline('translation_en_to_es',
                        model=AutoModelForSeq2SeqLM.from_pretrained(
                            'Helsinki-NLP/opus-mt-en-es'),
                        tokenizer=AutoTokenizer.from_pretrained(
                            'Helsinki-NLP/opus-mt-en-es')
                        )


def paraphrase(sentence: str, lang: str, count: str):
    p_count = int(count)
    if p_count <= 0 or len(sentence.strip()) == 0:
        return {'result': []}
    sentence_input = sentence
    if lang == 'ES':
        sentence_input = pln_es_to_en(sentence_input)[0]['translation_text']
    text = f"paraphrase: {sentence_input} </s>"
    encoding = tokenizer.encode_plus(text, padding=True, return_tensors="pt")
    input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
    outputs = model.generate(
        input_ids=input_ids, attention_mask=attention_masks,
        max_length=512,  # 256,
        do_sample=True,
        top_k=120,
        top_p=0.95,
        early_stopping=True,
        num_return_sequences=p_count
    )
    res = []
    for output in outputs:
        line = tokenizer.decode(
            output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
        res.append(line)
    if lang == 'EN':
        return {'result': res}
    else:
        res_es = [pln_en_to_es(x)[0]['translation_text']
                  for x in res]
        return {'result': res_es}
        
def paraphrase_dummy(sentence: str, lang: str, count: str):
    return {'result': []}


iface = gr.Interface(fn=paraphrase,
                     inputs=[
                         gr.inputs.Textbox(
                             lines=2, placeholder=None, label='Sentence'),
                         gr.inputs.Dropdown(
                             ['ES', 'EN'], type="value", label='Language'),
                         gr.inputs.Number(
                             default=3, label='Paraphrases count'),
                     ],
                     outputs=[gr.outputs.JSON(label=None)])
iface.launch()