import streamlit as st
from transformers import pipeline, set_seed
from transformers import AutoTokenizer
from normalizer import Normalizer
import random

import meta
import examples
from utils import (
    remote_css,
    local_css
)


class TextGeneration:
    def __init__(self):
        self.debug = False
        self.dummy_output = None
        self.tokenizer = None
        self.generator = None
        self.task = "text-generation"
        self.model_name_or_path = "HamidRezaAttar/gpt2-product-description-generator"
        set_seed(42)

    def load(self):
        if not self.debug:
            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path)
            self.generator = pipeline(self.task, model=self.model_name_or_path, tokenizer=self.model_name_or_path)

    def generate(self, prompt, generation_kwargs):
        if not self.debug:
            generation_kwargs["num_return_sequences"] = 1

            max_length = len(self.tokenizer(prompt)["input_ids"]) + generation_kwargs["max_length"]
            generation_kwargs["max_length"] = max_length

            generation_kwargs["return_full_text"] = False

            return self.generator(
                prompt,
                **generation_kwargs,
            )[0]["generated_text"]

        return self.dummy_output


@st.cache(allow_output_mutation=True)
def load_text_generator():
    generator = TextGeneration()
    generator.load()
    return generator


def main():
    st.set_page_config(
        page_title="GPT2 - Home",
        page_icon="🏡",
        layout="wide",
        initial_sidebar_state="expanded"
    )
    remote_css("https://fonts.googleapis.com/css2?family=Roboto:wght@300&display=swap%22%20rel=%22stylesheet%22")
    local_css("assets/ltr.css")
    generator = load_text_generator()

    st.sidebar.markdown(meta.SIDEBAR_INFO)

    max_length = st.sidebar.slider(
        label='Max Length',
        help="The maximum length of the sequence to be generated.",
        min_value=1,
        max_value=128,
        value=50,
        step=1
    )
    top_k = st.sidebar.slider(
        label='Top-k',
        help="The number of highest probability vocabulary tokens to keep for top-k-filtering",
        min_value=40,
        max_value=80,
        value=50,
        step=1
    )
    top_p = st.sidebar.slider(
        label='Top-p',
        help="Only the most probable tokens with probabilities that add up to `top_p` or higher are kept for "
             "generation.",
        min_value=0.0,
        max_value=1.0,
        value=0.95,
        step=0.01
    )
    temperature = st.sidebar.slider(
        label='Temperature',
        help="The value used to module the next token probabilities",
        min_value=0.1,
        max_value=10.0,
        value=1.0,
        step=0.05
    )
    do_sample = st.sidebar.selectbox(
        label='Sampling ?',
        options=(True, False),
        help="Whether or not to use sampling; use greedy decoding otherwise.",
    )
    generation_kwargs = {
        "max_length": max_length,
        "top_k": top_k,
        "top_p": top_p,
        "temperature": temperature,
        "do_sample": do_sample,
    }

    st.markdown(meta.HEADER_INFO)
    prompts = list(examples.EXAMPLES.keys()) + ["Custom"]
    prompt = st.selectbox('Examples', prompts, index=len(prompts) - 1)

    if prompt == "Custom":
        prompt_box = meta.PROMPT_BOX
    else:
        prompt_box = random.choice(examples.EXAMPLES[prompt])

    text = st.text_area("Enter text", prompt_box)
    generation_kwargs_ph = st.empty()
    cleaner = Normalizer()
    if st.button("Generate !"):
        with st.spinner(text="Generating ..."):
            generation_kwargs_ph.markdown(", ".join([f"`{k}`: {v}" for k, v in generation_kwargs.items()]))
            if text:
                generated_text = generator.generate(text, generation_kwargs)
                generated_text = cleaner.clean_txt(generated_text)
                st.markdown(
                    f'<p class="ltr ltr-box">'
                    f'<span class="result-text">{text} <span>'
                    f'<span class="result-text generated-text">{generated_text}</span>'
                    f'</p>',
                    unsafe_allow_html=True
                )

if __name__ == '__main__':
    main()