Create app.py
Browse files
app.py
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import nltk
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nltk.download('punkt')
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def fragment_text(text, tokenizer):
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sentences = nltk.tokenize.sent_tokenize(text)
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max_len = tokenizer.max_len_single_sentence
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chunks = []
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chunk = ""
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count = -1
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for sentence in sentences:
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count += 1
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combined_length = len(tokenizer.tokenize(sentence)) + len(chunk)
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if combined_length <= max_len:
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chunk += sentence + " "
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else:
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chunks.append(chunk.strip())
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chunk = sentence + " "
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if chunk != "":
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chunks.append(chunk.strip())
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return chunks
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def summarize_text(text):
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chunks = fragment_text(text, tokenizer)
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summaries = []
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for chunk in chunks:
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input = tokenizer(chunk, return_tensors='pt')
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output = model.generate(**input)
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summary = tokenizer.decode(*output, skip_special_tokens=True)
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summaries.append(summary)
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final_summary = " ".join(summaries)
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return final_summary
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# Importa o modelo de sumarização
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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# Define o modelo de sumarização
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checkpoint = "tclopess/bart_samsum"
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
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# Adiciona um campo de input
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text_input = st.text_area("Cola ou digite o texto a ser resumido:")
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# Adiciona um botão
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button = st.button("Resumo")
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# Chama a função de sumarização se o botão for clicado
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if button:
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summary = summarize_text(text_input)
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st.write("Resumo:")
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st.write(summary)
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