import streamlit as st import tensorflow as tf from transformers import AutoTokenizer, TFAutoModelForCausalLM, TFAutoModelForSequenceClassification # MODEL TO CALL generator_name = "Alirani/synopsis_generator" 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/synopsis_classifier" 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_input('Type the beginning of your synopsis') button_synopsis = st.button('Get synopsis') if button_synopsis: if (len(prod_title.split(' ')) > 0) & (len(option_genres) > 0) : 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 ! 😊")