Update app.py

app.py

@@ -3,22 +3,25 @@ from modules.extractive import TFIDFSummarizer, TextRankSummarizer, CombinedSumm
 from modules.abstractive import load_summarizers, abstractive_summary
 from modules.preprocessing import Preprocessor, PDFProcessor
 from modules.utils import handle_long_text
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 # Cargar modelos abstractivos finetuneados
 summarizers = load_summarizers()
 
-
 # Función principal para generar resúmenes
 def summarize(input_text, file, summary_type, method, num_sentences, model_name, max_length, num_beams):
     preprocessor = Preprocessor()
 
+    # Procesar archivo si se carga
     if file is not None:
         pdf_processor = PDFProcessor()
         input_text = pdf_processor.pdf_to_text(file.name)
 
+    # Validar entrada de texto
     if not input_text:
         return "Por favor, ingrese texto o cargue un archivo válido."
 
+    # Limpiar texto de entrada
     cleaned_text = preprocessor.clean_text(input_text)
 
     if summary_type == "Extractivo":
@@ -33,6 +36,7 @@ def summarize(input_text, file, summary_type, method, num_sentences, model_name,
         else:
             return "Método no válido para resumen extractivo."
 
+        # Generar resumen extractivo
         return summarizer.summarize(
             preprocessor.split_into_sentences(cleaned_text),
             preprocessor.clean_sentences(preprocessor.split_into_sentences(cleaned_text)),
@@ -42,17 +46,21 @@ def summarize(input_text, file, summary_type, method, num_sentences, model_name,
     elif summary_type == "Abstractivo":
         if model_name not in summarizers:
             return "Modelo no disponible para resumen abstractivo."
+        model, tokenizer = summarizers[model_name]
         return handle_long_text(
             cleaned_text,
-            summarizers[model_name][0],
-            summarizers[model_name][1],
+            model,
+            tokenizer,
             max_length=max_length,
             stride=128,
         )
 
+
     elif summary_type == "Combinado":
         if model_name not in summarizers:
             return "Modelo no disponible para resumen abstractivo."
+
+        # Generar resumen combinado: Extractivo + Abstractivo
         extractive_summary = TFIDFSummarizer().summarize(
             preprocessor.split_into_sentences(cleaned_text),
             preprocessor.clean_sentences(preprocessor.split_into_sentences(cleaned_text)),
@@ -68,7 +76,6 @@ def summarize(input_text, file, summary_type, method, num_sentences, model_name,
 
     return "Seleccione un tipo de resumen válido."
 
-
 # Interfaz dinámica
 with gr.Blocks() as interface:
     gr.Markdown("# Demo: Generador de Resúmenes Inteligente")
@@ -106,7 +113,7 @@ with gr.Blocks() as interface:
         1, 10, value=4, step=1, label="Número de haces (Abstractivo)", visible=False
     )
 
-
+    # Actualización dinámica de opciones
     def update_options(summary_type):
         if summary_type == "Extractivo":
             return (
@@ -131,6 +138,7 @@ with gr.Blocks() as interface:
         outputs=[method, num_sentences, model_name, max_length, num_beams],
     )
 
+    # Botón para generar resumen
     summarize_button = gr.Button("Generar Resumen")
     output = gr.Textbox(lines=10, label="Resumen generado", interactive=True)
     copy_button = gr.Button("Copiar Resumen")
@@ -141,11 +149,10 @@ with gr.Blocks() as interface:
         outputs=output,
     )
 
-
+    # Funcionalidad para copiar resumen
     def copy_summary(summary):
         return summary
 
-
     copy_button.click(
         fn=copy_summary,
         inputs=[output],