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import streamlit as st |
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import tensorflow as tf |
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from transformers import AutoTokenizer, TFAutoModelForCausalLM, TFAutoModelForSequenceClassification |
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generator_name = "Alirani/synopsis_generator" |
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tokenizer_gen = AutoTokenizer.from_pretrained(generator_name) |
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model_gen = TFAutoModelForCausalLM.from_pretrained(generator_name) |
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def generate_synopsis(model, tokenizer, title): |
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input_ids = tokenizer(title, return_tensors="tf") |
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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']) |
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synopsis = tokenizer.decode(output[0], skip_special_tokens=True) |
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processed_synopsis = "".join(synopsis.split('|')[2].rpartition('.')[:2]).strip() |
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return processed_synopsis |
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classifier_name = "Alirani/synopsis_classifier" |
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tokenizer_clf = AutoTokenizer.from_pretrained(classifier_name) |
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model_clf = TFAutoModelForSequenceClassification.from_pretrained(classifier_name) |
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def generate_classification(model, tokenizer, title, overview): |
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tokens = tokenizer(f"{title} | {overview}", padding=True, truncation=True, return_tensors="tf") |
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output = model(**tokens).logits |
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predicted_class_id = int(tf.math.argmax(output, axis=-1)[0]) |
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return model.config.id2label[predicted_class_id] |
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favicon = "https://i.ibb.co/JRdhFZg/favicon-32x32.png" |
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st.set_page_config(page_title="Synopsis Generator", page_icon = favicon, layout = 'wide', initial_sidebar_state = 'auto') |
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st.title('Demo of a Synopsis Classifier & Generator') |
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functionality = st.radio("Choose the function to use", |
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["Classification", "Generation"], |
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captions = ['Classify title & synopsis into genres', 'Generate synopsis from title & genres'], |
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index = None) |
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if functionality == "Classification" : |
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st.header('Classify a story') |
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prod_title = st.text_input('Type a title to classify a synopsis') |
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prod_synopsis = st.text_area('Type a synopsis to classify it') |
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button_classify = st.button('Get genre') |
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if button_classify: |
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if (len(prod_title.split(' ')) > 0) & len(prod_synopsis.split(' ')) > 0: |
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classified_genre = generate_classification(model_clf, tokenizer_clf, prod_title, prod_synopsis) |
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st.write('The genre of the title & synopsis is : ', classified_genre) |
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else: |
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st.write('Write a title & synopsis for the classifier to work !') |
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elif functionality == "Generation": |
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st.header('Generate a story') |
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prod_title = st.text_input('Type a title to generate a synopsis') |
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option_genres = st.selectbox( |
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'Select a genre to tailor your synopsis', |
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('Family', 'Romance', 'Comedy', 'Action', 'Documentary', 'Adventure', 'Drama', 'Mystery', 'Crime', 'Thriller', 'Science Fiction', 'History', 'Music', 'Western', 'Fantasy', 'TV Movie', 'Horror', 'Animation', 'Reality'), |
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index=None, |
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placeholder="Select genres..." |
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) |
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complete_synopsis = st.toggle('Synopsis completion') |
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if complete_synopsis: |
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pre_synopsis = st.text_input('Type the beginning of your synopsis') |
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button_synopsis = st.button('Get synopsis') |
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if button_synopsis: |
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if (len(prod_title.split(' ')) > 0) & (len(option_genres) > 0) : |
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gen_synopsis = generate_synopsis(model_gen, tokenizer_gen, f"{prod_title} | {option_genres} | {pre_synopsis}") |
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st.text_area('Generated synopsis', value=gen_synopsis, disabled=True) |
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else: |
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st.write('Write a title & select a genre for the generator to work !') |
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else: |
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button_synopsis = st.button('Get synopsis') |
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if button_synopsis: |
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if len(prod_title.split(' ')) > 0: |
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gen_synopsis = generate_synopsis(model_gen, tokenizer_gen, f"{prod_title} | {option_genres} | ") |
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st.text_area('Generated synopsis', value=gen_synopsis, disabled=True) |
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else: |
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st.write('Write a title for the generator to work !') |
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else: |
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st.write("Select a functionality ! 😊") |
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