Create app.py

app.py

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+# interfaceV2.py
+
+import gradio as gr
+import sounddevice as sd
+from scipy.io.wavfile import write
+import tempfile
+import shutil
+import os
+import subprocess
+import sys
+from whisper_audio_transcriber import transcribe_audio, guardar_transcripcion
+from call_openai_api import  moni as rtff   # Asegúrate de que el archivo call_open_api.py esté en el mismo directorio
+
+
+# Paths to files (adjusted as per your specified structure)
+AUDIO_RECORD_PATH = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/assets/audio/grabacion_gradio.wav")
+#VIDEO_PATH = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/assets/video/data_video_sun_5s.mp4")
+VIDEO_PATH = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/assets/video/data_video_sun.mp4")
+#TRANSCRIPTION_TEXT_PATH = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/results/transcripcion.txt")
+TRANSCRIPTION_TEXT_PATH = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/results/transcripcion.txt")
+RESULT_AUDIO_TEMP_PATH = os.path.abspath( "C:/programacionEjercicios/miwav2lipv6/results/audiov2.wav")
+RESULT_AUDIO_FINAL_PATH = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/assets/audio/audio.wav")
+RESULT_VIDEO_PATH = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/results/result_voice.mp4")
+TEXT_TO_SPEECH_PATH = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/src/text_to_speech.py")
+
+# Function to record 8-second audio
+def grabar_audio(duration=8, sample_rate=44100):
+    print("Starting recording...")
+    audio_data = sd.rec(int(duration * sample_rate), samplerate=sample_rate, channels=1)
+    print(f"Recording in progress for {duration} seconds...")
+    sd.wait()
+    print("Recording completed.")
+
+    temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+    write(temp_audio.name, sample_rate, audio_data)
+    print("Audio temporarily saved at:", temp_audio.name)
+    temp_audio.close()  # Asegúrate de cerrarlo antes de usarlo
+    os.makedirs(os.path.dirname(AUDIO_RECORD_PATH), exist_ok=True)
+    shutil.copy(temp_audio.name, AUDIO_RECORD_PATH)
+    print(f"Recording copied to: {AUDIO_RECORD_PATH}")
+
+    return AUDIO_RECORD_PATH, "Recording completed."
+
+# Function to transcribe audio with Whisper
+def transcribir_con_progreso(audio_path):
+    progreso = gr.Progress()
+    progreso(0, "Starting transcription...")
+    model_name = "openai/whisper-large"
+    progreso(25, "Loading Whisper model...")
+
+    transcripcion = transcribe_audio(audio_path, model_name)
+    progreso(75, "Saving transcription...")
+    guardar_transcripcion(transcripcion, filename=TRANSCRIPTION_TEXT_PATH)
+    progreso(100, "Transcription completed.")
+    if not os.path.exists(TRANSCRIPTION_TEXT_PATH):
+        raise FileNotFoundError(f"El archivo {TRANSCRIPTION_TEXT_PATH} no se generó.")
+
+    return transcripcion
+
+# Function to convert text to audio using text_to_speech.py
+def generar_audio_desde_texto():
+    print("Generating audio from text...")
+    result = subprocess.run(
+        [sys.executable, TEXT_TO_SPEECH_PATH],
+        capture_output=True,
+        text=True
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"Error ejecutando text_to_speech.py: {result.stderr}")
+    if result.stdout:
+        print("Output:", result.stdout)
+    if result.stderr:
+        print("Errors:", result.stderr)
+
+    if os.path.exists(RESULT_AUDIO_TEMP_PATH):
+        print(f"Temporary audio generated at: {RESULT_AUDIO_TEMP_PATH}")
+        
+        os.makedirs(os.path.dirname(RESULT_AUDIO_FINAL_PATH), exist_ok=True)
+        shutil.copy(RESULT_AUDIO_TEMP_PATH, RESULT_AUDIO_FINAL_PATH)
+        print(f"Final audio copied to: {RESULT_AUDIO_FINAL_PATH}")
+
+        return RESULT_AUDIO_FINAL_PATH
+    else:
+        print(f"Error: Audio file was not generated in {RESULT_AUDIO_FINAL_PATH} ")
+        return None
+
+# Function to process video and audio using run_inference.py with the generated audio file
+def procesar_video_audio():
+    print("Starting video and audio processing...")
+    run_inference_path = os.path.abspath("C:/programacionEjercicios/miwav2lipv6/src/run_inference.py")
+
+    result = subprocess.run(
+        [sys.executable, run_inference_path, "--audio", RESULT_AUDIO_FINAL_PATH, "--video", VIDEO_PATH],
+        capture_output=True,
+        text=True
+    )
+
+    if result.stdout:
+        print("Output:", result.stdout)
+    if result.stderr:
+        print("Errors:", result.stderr)
+
+    if os.path.exists(RESULT_VIDEO_PATH):
+        print(f"Processed video saved at: {RESULT_VIDEO_PATH}")
+        return RESULT_VIDEO_PATH
+    else:
+        print("Error: Video file was not generated at 'results/result_voice.mp4'")
+        return None
+
+# Gradio Interface Configuration
+def interfaz():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            with gr.Column():
+                gr.Video(VIDEO_PATH, loop=True, autoplay=True, height=300, width=500)
+                grabar_button = gr.Button("Comenzando la grabacion de audio")
+                estado_grabacion = gr.Textbox(label="Recording Status", interactive=False)
+
+            with gr.Column():
+                output_audio = gr.Audio(AUDIO_RECORD_PATH, label="Audio Grabado", interactive=False)
+                output_audio_speech = gr.Audio(RESULT_AUDIO_FINAL_PATH, label="Audio TTS", interactive=False)
+                video_resultado = gr.Video(RESULT_VIDEO_PATH,label="Video procesado", interactive=False)
+                texto_transcripcion = gr.Textbox(label="Texto transcrito")
+                progreso_transcripcion = gr.Textbox(label="Transcription Status", interactive=False)
+
+            # Full flow: recording, transcription, text-to-speech, and video processing
+            """
+            def flujo_completo():
+                _, mensaje_grabacion = grabar_audio()
+                transcripcion = transcribir_con_progreso(AUDIO_RECORD_PATH)
+                audio_generado = generar_audio_desde_texto()
+                video_path = procesar_video_audio()
+
+                # Ensure function always returns 5 outputs for Gradio, even in error cases
+                if video_path and audio_generado:
+                    return mensaje_grabacion, AUDIO_RECORD_PATH, transcripcion, audio_generado, video_path
+                else:
+                    return mensaje_grabacion, AUDIO_RECORD_PATH, transcripcion, audio_generado or "Audio generation failed", video_path or "Video generation failed"
+            """
+            def flujo_completo():
+                try:
+                    print("Inicio del flujo completo...")
+                    # Grabar audio
+                    audio_path, mensaje_grabacion = grabar_audio()
+                    print("Audio grabado en:", audio_path)
+                    # Transcribir audio
+                    transcripcion = transcribir_con_progreso(audio_path)
+                    print("Transcripción completada:", transcripcion)
+                    
+                    #respuesta_openai = rtff(transcripcion)
+                    respuesta_openai = rtff(TRANSCRIPTION_TEXT_PATH)
+                    print("Respuesta generada  por OpenAI")
+                    
+                    # Generar audio desde texto
+                    audio_generado = generar_audio_desde_texto()
+                    print("Audio generado:", audio_generado)
+                    # Procesar video y audio
+                    video_path = procesar_video_audio()
+                    print("Video procesado en:", video_path)
+                    # Devolver resultados si todo fue exitoso
+                    return mensaje_grabacion, audio_path, transcripcion, audio_generado, video_path
+                
+                except Exception as e:
+                    # Imprime el error en la terminal y regresa mensajes de error a la interfaz
+                    print("Error detectado en flujo completo:", str(e))
+                    return (
+                        "Error durante el flujo completo",
+                        None,  # Audio grabado
+                        f"Error: {str(e)}",  # Transcripción
+                        None,  # Audio generado
+                        None   # Video procesado
+                            )
+
+            grabar_button.click(
+                flujo_completo,
+                outputs=[estado_grabacion, output_audio, texto_transcripcion, output_audio_speech, video_resultado]
+            )
+
+    return demo
+
+if __name__ == "__main__":
+    demo = interfaz()
+    demo.launch(allowed_paths=["C:/programacionEjercicios/miwav2lipv6/assets", "C:/programacionEjercicios/miwav2lipv6/results"])