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import streamlit as st
from transformers import AutoTokenizer, TFAutoModelForCausalLM
# MODEL TO CALL
model_name = "Alirani/distilgpt2-finetuned-synopsis-genres_final"
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)
processed_synopsis = "".join(synopsis.split('|')[2].rpartition('.')[:2]).strip()
return processed_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')
option_genres = st.selectbox(
'Select a genre to tailor your synopsis',
('Family', 'Romance', 'Comedy', 'Action', 'Documentary', 'Adventure', 'Drama', 'Mystery', 'Crime', 'Thriller', 'Science Fiction', 'History', 'Music', 'Western', 'Fantasy', 'TV Movie', 'Horror', 'Animation', 'Reality')
)
button_synopsis = st.button('Get synopsis')
if button_synopsis:
if len(prod_title.split(' ')) > 0:
gen_synopsis = generate_synopsis(model, tokenizer, f"{prod_title} | {option_genres} | ")
st.text_area('Generated synopsis', value=gen_synopsis, disabled=True)
else:
st.write('Write a title for the generator to work !')
|