Update app.py

app.py

@@ -1,3 +1,60 @@
+#import gradio as gr
+#gr.load("models/tclopess/bart_samsum").launch()
 import gradio as gr
+import nltk
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-gr.load("models/tclopess/bart_samsum").launch()
+# Carregue o modelo e o tokenizer
+checkpoint = "tclopess/bart_samsum"
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
+
+# Função para fragmentar o texto
+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 generate_summaries(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)
+  return summaries
+
+# Função para exibir o resumo final
+def display_summary(summaries):
+  summary = " ".join(summaries)
+  gr.text("Resumo final:", summary)
+
+# Função `input_fn()` para retornar o campo de input
+def input_fn():
+  # Crie um campo de input do tipo `text`
+  input_text = gr.inputs.Textbox(label="Insira ou cole o texto aqui:")
+
+  # Retorne o campo de input
+  return input_text
+
+# Altere a linha `gr.load()` para usar a função `input_fn()`
+gr.load("models/tclopess/bart_samsum").launch(input_fn=input_fn)