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/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.write('Generated synopsis : ', gen_synopsis) | |
| 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.write('Generated synopsis : ', gen_synopsis) | |
| else: | |
| st.write('Write a title for the generator to work !') | |
| else: | |
| st.write("Select a functionality ! 😊") | |