import streamlit as st
from streamlit.elements.altair import generate_chart
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import pipeline

@st.cache(allow_output_mutation=True)
def load_model():
  model_ckpt = "flax-community/gpt2-rap-lyric-generator"
  tokenizer = AutoTokenizer.from_pretrained(model_ckpt,from_flax=True)
  model = AutoModelForCausalLM.from_pretrained(model_ckpt,from_flax=True)
  return tokenizer, model

@st.cache()
def load_rappers():
  text_file = open("rappers.txt")
  rappers = text_file.readlines()
  rappers.sort()
  return rappers


title = st.title("Loading model")
tokenizer, model = load_model()
text_generation = pipeline("text-generation", model=model, tokenizer=tokenizer)
title.title("Rap lyrics generator")
#artist = st.text_input("Enter the artist", "Wu-Tang Clan")
list_of_rappers = load_rappers()
artist = st.radio("Choose your rapper", tuple(list_of_rappers))
song_name = st.text_input("Enter the desired song name", "Shaolin")



if st.button("Generate lyrics", help="Press me!"):
    st.title(f"{artist}: {song_name}")
    prefix_text = f"<BOS>{song_name} [Verse 1:{artist}]"
    generated_song = text_generation(prefix_text, max_length=750, do_sample=True)[0]
    for count, line in enumerate(generated_song['generated_text'].split("\n")):
      if count == 0:
        st.write(line[line.find('['):])
        continue
      if"<EOS>" in line:
        break
      if "<BOS>" in line:
        st.write(line[5:])
        continue
      st.write(line)