Delete app.py

app.py

@@ -1,65 +0,0 @@
-import nltk
-import streamlit as st
-import transformers
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-nltk.download('punkt', force=True)
-
-
-def fragment_text(text, tokenizer):
-
-  sentences = nltk.tokenize.sent_tokenize(text)
-  max_len = tokenizer.max_len_single_sentence
-
-  chunks = []
-  chunk = ""
-  count = -1
-
-  for sentence in sentences:
-    count += 1
-    combined_length = len(tokenizer.tokenize(sentence)) + len(chunk)
-
-    if combined_length <= max_len:
-      chunk += sentence + " "
-    else:
-      chunks.append(chunk.strip())
-      chunk = sentence + " "
-
-  if chunk != "":
-    chunks.append(chunk.strip())
-
-  return chunks
-
-def summarize_text(text):
-
-  chunks = fragment_text(text, tokenizer)
-
-  summaries = []
-  for chunk in chunks:
-    input = tokenizer(chunk, return_tensors='pt')
-    output = model.generate(**input)
-    summary = tokenizer.decode(*output, skip_special_tokens=True)
-    summaries.append(summary)
-
-  final_summary = " ".join(summaries)
-  return final_summary
-
-# Importa o modelo de sumarização
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-# Define o modelo de sumarização
-checkpoint = "tclopess/bart_samsum"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
-
-# Adiciona um campo de input
-text_input = st.text_area("Cola ou digite o texto a ser resumido:")
-
-# Adiciona um botão
-button = st.button("Resumo")
-
-# Chama a função de sumarização se o botão for clicado
-if button:
-  summary = summarize_text(text_input)
-  st.write("Resumo:")
-  st.write(summaries)