Spaces:
Runtime error
Runtime error
import streamlit as st | |
import tensorflow as tf | |
from transformers import AutoTokenizer, TFAutoModelForCausalLM, TFAutoModelForSequenceClassification | |
# MODEL TO CALL | |
generator_name = "Alirani/distilgpt2-finetuned-synopsis-genres_final" | |
tokenizer_gen = AutoTokenizer.from_pretrained(generator_name) | |
model_gen = TFAutoModelForCausalLM.from_pretrained(generator_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 | |
classifier_name = "Alirani/overview_classifier_final" | |
tokenizer_clf = AutoTokenizer.from_pretrained(classifier_name) | |
model_clf = TFAutoModelForSequenceClassification.from_pretrained(classifier_name) | |
def generate_classification(model, tokenizer, title, overview): | |
tokens = tokenizer(f"{title} | {overview}", padding=True, truncation=True, return_tensors="tf") | |
output = model(**tokens).logits | |
predicted_class_id = int(tf.math.argmax(output, axis=-1)[0]) | |
return model.config.id2label[predicted_class_id] | |
favicon = "https://i.ibb.co/JRdhFZg/favicon-32x32.png" | |
st.set_page_config(page_title="Synopsis Generator", page_icon = favicon, layout = 'wide', initial_sidebar_state = 'auto') | |
st.title('Demo of a Synopsis Classifier & Generator') | |
functionality = st.radio("Choose the function to use", | |
["Classification", "Generation"], | |
captions = ['Classify title & synopsis into genres', 'Generate synopsis from title & genres'], | |
index = None) | |
if functionality == "Classification" : | |
# CLASSIFY A SYNOPSIS | |
st.header('Classify a story') | |
prod_title = st.text_input('Type a title to classify a synopsis') | |
prod_synopsis = st.text_area('Type a synopsis to classify it') | |
button_classify = st.button('Get genre') | |
if button_classify: | |
if (len(prod_title.split(' ')) > 0) & len(prod_synopsis.split(' ')) > 0: | |
classified_genre = generate_classification(model_clf, tokenizer_clf, prod_title, prod_synopsis) | |
st.write('The genre of the title & synopsis is : ', classified_genre) | |
else: | |
st.write('Write a title & synopsis for the classifier to work !') | |
elif functionality == "Generation": | |
# GENERATE A SYNOPSIS | |
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'), | |
index=None, | |
placeholder="Select genres..." | |
) | |
complete_synopsis = st.toggle('Synopsis completion') | |
if complete_synopsis: | |
pre_synopsis = st.text_area('Type the beginning of your synopsis') | |
button_synopsis = st.button('Get synopsis') | |
if button_synopsis: | |
if (len(prod_title.split(' ')) > 0) & (option_genres): | |
gen_synopsis = generate_synopsis(model_gen, tokenizer_gen, f"{prod_title} | {option_genres} | {pre_synopsis}") | |
st.text_area('Generated synopsis', value=gen_synopsis, disabled=True) | |
else: | |
st.write('Write a title & select a genre for the generator to work !') | |
else: | |
button_synopsis = st.button('Get synopsis') | |
if button_synopsis: | |
if len(prod_title.split(' ')) > 0: | |
gen_synopsis = generate_synopsis(model_gen, tokenizer_gen, 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 !') | |
else: | |
st.write("Select a functionality ! π") | |