import streamlit as st
from transformers import AutoTokenizer, TFAutoModelForCausalLM

# MODEL TO CALL

model_name = "Alirani/distilgpt2-finetuned-synopsis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModelForCausalLM.from_pretrained(model_name)

def generate_synopsis(model, tokenizer, title):
    input_ids = tokenizer(title, return_tensors="tf")
    output = model.generate(input_ids['input_ids'], max_length=150, num_beams=5, no_repeat_ngram_size=2, top_k=50, attention_mask=input_ids['attention_mask'])
    synopsis = tokenizer.decode(output[0], skip_special_tokens=True)
    return synopsis

favicon = "https://i.ibb.co/JRdhFZg/favicon-32x32.png"

st.set_page_config(page_title="LoreFinder-demo", page_icon = favicon, layout = 'wide', initial_sidebar_state = 'auto')

st.title('Demo LoreFinder')

st.header('Generate a story')

prod_title = st.text_input('Type a title to generate a synopsis')

button_synopsis = st.button('Get synopsis')

if button_synopsis:
    if len(prod_title.split(' ')) > 0:
        gen_synopsis = generate_synopsis(model, tokenizer, prod_title)
        st.text_area(gen_synopsis, disabled=True)
    else:
        st.write('Write a title for the generator to work !')