Update app.py

app.py

@@ -907,93 +907,87 @@ custom_css = """
 }
 """
 # Create the Gradio interface with proper output handling
-with gr.Blocks(title="Affective Virtual Environments - Chunked Processing", css=custom_css) as interface:
+with gr.Blocks(title="Entornos Virtuales Afectivos - Procesamiento por Segmentos", css=custom_css) as interface:
     gr.Markdown("# Bello")
     gr.Markdown(
     """
-***Bello*** explores the affective nuances of the human voice.  
-Using multimodal speech emotion recognition techniques, the project analyzes acoustic, prosodic,  
-and semantic parameters of spoken language to generate immersive 360° virtual environments.
+***Bello*** explora las sutilezas afectivas de la voz humana.  
+Usando técnicas multimodales de reconocimiento de emociones en el habla, el proyecto analiza parámetros acústicos, prosódicos  
+y semánticos del lenguaje hablado para generar entornos virtuales inmersivos en 360°.
 
-### How to interact
+### Cómo interactuar
 
-1. Record your voice saying whatever you want—a poem, a song, a story, or any sentence that comes to mind.  
-2. Set the length to split your recording into chunks.  
-3. Check the box if you want to generate audio for each chunk.  
-4. Generate your Affective Virtual Environment and wait for the results.  
-5. Download the HTML file.  
-6. Open your creation with any web browser.  
+1. Graba tu voz diciendo lo que quieras—un poema, una canción, una historia, o cualquier frase que se te ocurra.  
+2. Establece la duración de cada segmento para dividir tu grabación en trozos.  
+3. Marca la casilla si quieres generar audio para cada segmento.  
+4. Genera tu Entorno Virtual Afectivo y espera los resultados.  
+5. Descarga el archivo HTML.  
+6. Abre tu creación con cualquier navegador web.  
 
 ---
-**Learn more:**  
-• Video Tutorial: [How to Use this Space](https://youtu.be/eVD1lzwVhi8)  
+**Más información:**  
+• Video Tutorial: [Cómo usar este espacio](https://youtu.be/eVD1lzwVhi8)  
 
-• For more information about the project, visit: [www.emotional-machines.com](https://www.emotional-machines.com)
+• Para más detalles del proyecto, visita: [www.emotional-machines.com](https://www.emotional-machines.com)
     """
     )
 
     
     with gr.Row():
         with gr.Column(scale=2):
-            audio_input = gr.Audio(label="Input Audio", type="filepath", sources=["microphone", "upload"])
+            audio_input = gr.Audio(label="Audio de Entrada", type="filepath", sources=["microphone", "upload"])
             
-            # Add example audio selection
-           # example_selector = gr.Dropdown(
-           #     label="Select Example Audio",
-           #     choices=["Happy Speech", "Sad Story", "Neutral News"],
-           #     value=None,
-           #     info="Choose from pre-recorded example speeches"
-           # )
+            # Ejemplos de audio (opcional)
+            # example_selector = gr.Dropdown(
+            #     label="Seleccionar Audio de Ejemplo",
+            #     choices=["Discurso Feliz", "Historia Triste", "Noticias Neutrales"],
+            #     value=None,
+            #     info="Elige entre audios pregrabados de ejemplo"
+            # )
             
-            # Add button to load selected example
-            #load_example_btn = gr.Button("Load Example", variant="secondary")
+            #load_example_btn = gr.Button("Cargar Ejemplo", variant="secondary")
         
         with gr.Column(scale=1):
-            # Add chunk duration input
             chunk_duration_input = gr.Number(
-                label="Chunk Duration (seconds)",
+                label="Duración de Segmento (segundos)",
                 value=10,
                 minimum=1,
                 maximum=60,
                 step=1,
-                info="Duration of each audio segment to process (1-60 seconds)"
+                info="Duración de cada segmento de audio a procesar (1-60 segundos)"
             )
-            # Add checkbox for audio generation
             generate_audio_checkbox = gr.Checkbox(
-                label="Generate Audio (may take longer)", 
+                label="Generar Audio (puede tardar más)", 
                 value=False,
-                info="Uncheck to skip music generation and speed up processing"
+                info="Desmarca para omitir la generación de música y acelerar el procesamiento"
             )
             with gr.Row():
-                process_btn = gr.Button("Generate", variant="primary")
-                clear_btn = gr.Button("Clear All", variant="secondary")
+                process_btn = gr.Button("Generar", variant="primary")
+                clear_btn = gr.Button("Borrar Todo", variant="secondary")
     
-    # Add a loading indicator
     loading_indicator = gr.HTML("""
         <div id="loading" style="display: none; text-align: center; margin: 20px;">
-            <p style="font-size: 18px; color: #4a4a4a;">Processing audio chunks...</p>
+            <p style="font-size: 18px; color: #4a4a4a;">Procesando segmentos de audio...</p>
             <div style="border: 4px solid #f3f3f3; border-top: 4px solid #3498db; border-radius: 50%; width: 30px; height: 30px; animation: spin 2s linear infinite; margin: 0 auto;"></div>
             <style>@keyframes spin { 0% { transform: rotate(0deg); } 100% { transform: rotate(360deg); } }</style>
         </div>
     """)
     
-    # Create output components for each chunk type
     output_containers = []
-    group_components = []  # Store group components separately
+    group_components = []  # Contenedores de grupos
     
-    # We'll create up to 20 chunk slots to accommodate different chunk durations
     for i in range(20):
         with gr.Group(visible=False) as chunk_group:
-            gr.Markdown(f"### Chunk {i+1} Results")
+            gr.Markdown(f"### Resultados del Segmento {i+1}")
             with gr.Row():
-                emotion_output = gr.Label(label="Acoustic Emotion Prediction")
-                transcription_output = gr.Label(label="Transcribed Text")
-                sentiment_output = gr.Label(label="Sentimental Analysis")
+                emotion_output = gr.Label(label="Predicción de Emoción Acústica")
+                transcription_output = gr.Label(label="Texto Transcrito")
+                sentiment_output = gr.Label(label="Análisis Sentimental")
             with gr.Row():
-                image_output = gr.Image(label="Generated Equirectangular Image")
-                image_360_output = gr.File(label="Download 360 Image", type="filepath")
+                image_output = gr.Image(label="Imagen Equirectangular Generada")
+                image_360_output = gr.File(label="Descargar Imagen 360", type="filepath")
             with gr.Row():
-                audio_output = gr.Audio(label="Generated Music")
+                audio_output = gr.Audio(label="Música Generada")
             gr.HTML("<hr style='margin: 20px 0; border: 1px solid #ccc;'>")
         
         group_components.append(chunk_group)
@@ -1006,37 +1000,16 @@ and semantic parameters of spoken language to generate immersive 360° virtual e
             'music': audio_output
         })
     
-    # Enhanced Download 360 Viewer Section
     with gr.Group(visible=True, elem_classes="download-section") as download_group:
-        gr.Markdown("""
-    
-        
-        
-        """)         
-        
-        # Enhanced download button
         viewer_html_output = gr.File(
-            label=" Once processing is complete, download your AVE from here 🚀", 
+            label="Una vez finalizado el procesamiento, descarga tu EVA aquí 🚀", 
             type="filepath",
             interactive=False,
             elem_classes="download-button"
         )
     
-    # Add a hidden HTML component for JavaScript execution
     js_output = gr.HTML(visible=False)
     
-    # Function to handle example selection
-    def load_example(example_name):
-        if not example_name:
-            return None, None
-        
-        # Get the path to the example audio file
-        example_path = load_example_audio(example_name)
-        
-        # Return the example path to update the audio component
-        return example_path, example_name
-    
-    # Set up the button clicks
     process_btn.click(
         fn=process_and_display,
         inputs=[audio_input, generate_audio_checkbox, chunk_duration_input],
@@ -1063,14 +1036,4 @@ and semantic parameters of spoken language to generate immersive 360° virtual e
         ]] + [loading_indicator, chunk_duration_input, viewer_html_output, js_output]
     )
     
-    #load_example_btn.click(
-    #    fn=load_example,
-    #    inputs=[example_selector],
-    #    outputs=[audio_input, example_selector]
-    #)
-    
-    # Check if we're running on Hugging Face Spaces
-    is_spaces = os.getenv('SPACE_ID') is not None
-    
-    # Launch with appropriate settings
-    interface.launch(share=True)  # Only share when not on Spaces
+    interface.launch(share=True)