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Create app.py
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app.py
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import torch
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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import streamlit as st
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# Load the tokenizer and model
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tokenizer = GPT2Tokenizer.from_pretrained('webtoon_tokenizer')
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model = GPT2LMHeadModel.from_pretrained('webtoon_model')
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# Define the app
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def main():
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st.title('Webtoon Description Generator')
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# Get the input from the user
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title = st.text_input('Enter the title of the Webtoon:', '')
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# Generate the description
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if st.button('Generate Description'):
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with st.spinner('Generating...'):
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description = generate_description(title)
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st.success(description)
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# Define the function that generates the description
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def generate_description(title):
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# Preprocess the input
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input_text = f"{title.lower()}"
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input_ids = tokenizer.encode(input_text, return_tensors='pt')
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# Generate the output using the model
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output = model.generate(
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input_ids=input_ids,
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max_length=256,
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num_beams=4,
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early_stopping=True,
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no_repeat_ngram_size=2
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)
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# Convert the output to text
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description = tokenizer.decode(output[0], skip_special_tokens=True)
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return description
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if __name__ == '__main__':
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main()
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