app.py

import gradio as gr
from gradio_client import Client
import os
import tempfile

# Crea il client Gradio per l'inferenza
inference_client = Client("http://127.0.0.1:6969/")

# Cartella di output in assets
output_folder = "assets/generated"
os.makedirs(output_folder, exist_ok=True)

def process_audio(audio_file):
    if audio_file is None:
        return None, "Nessun file audio caricato."
    
    # Ottieni il percorso temporaneo del file caricato
    input_file = audio_file.name
    
    try:
        # Prepara il percorso di output
        output_filename = f"generated_{os.path.basename(input_file)}"
        output_path = os.path.join(output_folder, output_filename)

        # Esegui la predizione
        result = inference_client.predict(
            -24,  # Pece
            0,    # Raggio del filtro
            0,    # Rapporto feature di ricerca
            0,    # Inviluppo del volume
            0,    # Proteggi le consonanti sorde
            1,    # Lunghezza del luppolo
            "pm", # Algoritmo di estrazione del passo
            input_file,  # Percorso del file audio di input
            output_path, # Percorso del file audio di output
            "logs/master/master.pth", # Modello vocale
            "logs/master/added_IVF124_Flat_nprobe_1_master-v2_v2.index", # File di indice
            True, # Dividere l'audio
            True, # Sintonizzazione automatica
            True, # Audio pulito
            0,    # Forza pulita
            "WAV", # Export Format
            api_name="/run_infer_script"
        )

        return output_path, f"Elaborazione completata. File salvato in {output_path}"

    except Exception as e:
        return None, f"Errore durante l'elaborazione: {str(e)}"

# Creazione dell'interfaccia Gradio
iface = gr.Interface(
    fn=process_audio,
    inputs=gr.Audio(type="filepath", label="Carica file audio (WAV o MP3)"),
    outputs=[
        gr.Audio(type="filepath", label="Audio elaborato"),
        gr.Textbox(label="Messaggio")
    ],
    title="Elaborazione Audio con Applio",
    description="Carica un file audio WAV o MP3 per elaborarlo con Applio."
)

# Avvio dell'interfaccia
iface.launch()

# import gradio as gr
# import sys
# import os
# import logging

# now_dir = os.getcwd()
# sys.path.append(now_dir)

# # Tabs
# from tabs.inference.inference import inference_tab
# from tabs.train.train import train_tab
# from tabs.extra.extra import extra_tab
# from tabs.report.report import report_tab
# from tabs.download.download import download_tab
# from tabs.tts.tts import tts_tab
# from tabs.voice_blender.voice_blender import voice_blender_tab
# from tabs.settings.presence import presence_tab, load_config_presence
# from tabs.settings.flask_server import flask_server_tab
# from tabs.settings.fake_gpu import fake_gpu_tab, gpu_available, load_fake_gpu
# from tabs.settings.themes import theme_tab
# from tabs.plugins.plugins import plugins_tab
# from tabs.settings.version import version_tab
# from tabs.settings.lang import lang_tab
# from tabs.settings.restart import restart_tab

# # Assets
# import assets.themes.loadThemes as loadThemes
# from assets.i18n.i18n import I18nAuto
# import assets.installation_checker as installation_checker
# from assets.discord_presence import RPCManager
# from assets.flask.server import start_flask, load_config_flask
# from core import run_prerequisites_script

# run_prerequisites_script("False", "True", "True", "True")

# i18n = I18nAuto()
# if load_config_presence() == True:
#     RPCManager.start_presence()
# installation_checker.check_installation()
# logging.getLogger("uvicorn").disabled = True
# logging.getLogger("fairseq").disabled = True
# if load_config_flask() == True:
#     print("Starting Flask server")
#     start_flask()

# my_applio = loadThemes.load_json()
# if my_applio:
#     pass
# else:
#     my_applio = "ParityError/Interstellar"

# with gr.Blocks(theme=my_applio, title="Applio") as Applio:
#     gr.Markdown("# Applio")
#     gr.Markdown(
#         i18n(
#             "Ultimate voice cloning tool, meticulously optimized for unrivaled power, modularity, and user-friendly experience."
#         )
#     )
#     gr.Markdown(
#         i18n(
#             "[Support](https://discord.gg/IAHispano) — [Discord Bot](https://discord.com/oauth2/authorize?client_id=1144714449563955302&permissions=1376674695271&scope=bot%20applications.commands) — [Find Voices](https://applio.org/models) — [GitHub](https://github.com/IAHispano/Applio)"
#         )
#     )
#     with gr.Tab(i18n("Inference")):
#         inference_tab()

#     with gr.Tab(i18n("Train")):
#         if gpu_available() or load_fake_gpu():
#             train_tab()
#         else:
#             gr.Markdown(
#                 i18n(
#                     "Currently, training is unsupported due to the absence of a GPU. If you have a PC with a GPU and wish to train a model, please refer to our installation guide here: [Applio Installation Guide](https://docs.applio.org/get-started/installation/). For those without a GPU-enabled PC, explore alternative options here: [Applio Alternatives](https://docs.applio.org/get-started/alternatives/)."
#                 )
#             )

#     with gr.Tab(i18n("TTS")):
#         tts_tab()

# #    with gr.Tab(i18n("Voice Blender")):
# #        voice_blender_tab()

# #    with gr.Tab(i18n("Plugins")):
# #        plugins_tab()

#     with gr.Tab(i18n("Download")):
#         download_tab()

#     with gr.Tab(i18n("Report a Bug")):
#         report_tab()

#     with gr.Tab(i18n("Extra")):
#         extra_tab()

# #    with gr.Tab(i18n("Settings")):
# #        presence_tab()
# #        flask_server_tab()
# #        if not gpu_available():
# #            fake_gpu_tab()
# #        theme_tab()
# #        version_tab()
# #        lang_tab()
# #        restart_tab()


# def launch_gradio():
#     Applio.launch()


# if __name__ == "__main__":
#     launch_gradio()