import gradio as gr
import os
from lib.infer import infer_audio
from pydub import AudioSegment

main_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 


                                                              
                                                             
                                                              
                                                             

                                                             
# Function for inference
def inference(model_name, audio, f0_change, f0_method, min_pitch, max_pitch, crepe_hop_length, 
              index_rate, filter_radius, rms_mix_rate, protect, split_infer, min_silence, 
              silence_threshold, seek_step, keep_silence, quefrency, timbre, 
              f0_autotune, output_format):
    
    
    # Perform inference
    inferred_audio = infer_audio(
        model_name,
        audio_path,
        f0_change,
        f0_method,
        min_pitch,
        max_pitch,
        crepe_hop_length,
        index_rate,
        filter_radius,
        rms_mix_rate,
        protect,
        split_infer,
        min_silence,
        silence_threshold,
        seek_step,
        keep_silence,
        quefrency,
        timbre,
        f0_autotune,
        output_format
    )
    
    # Convert the output audio
    os.chdir(main_dir)
    output_audio = AudioSegment.from_file(inferred_audio)
    
    # Save the output audio and return
    output_path = f"output.{output_format}"
    output_audio.export(output_path, format=output_format)
    return output_path

# Gradio UI
with gr.Blocks(theme="Ryouko65777/ryo", js="() => {document.body.classList.toggle('dark');}") as demo:
    gr.Markdown("# Ryo RVC ")

    
    with gr.Tabs():
        audio_input = gr.Audio(label="Input Audio", type="filepath")
   
        model_name = gr.Textbox(label="Model Name")
        f0_change = gr.Number(label="Pitch Change (F0 Change)", value=0)
        f0_method = gr.Dropdown(
            label="F0 Method", 
            choices=
            [
                "crepe",
                "harvest",
                "mangio-crepe",
                "rmvpe",
                "rmvpe+",
                "fcpe", 
                "fcpe_legacy",
                "hybrid[mangio-crepe+rmvpe]",
                "hybrid[mangio-crepe+fcpe]",
                "hybrid[rmvpe+fcpe]",
                "hybrid[mangio-crepe+rmvpe+fcpe]",
            ],
            value="fcpe",
        )
        min_pitch = gr.Textbox(label="Min Pitch", value="50")
        max_pitch = gr.Textbox(label="Max Pitch", value="1100")
        crepe_hop_length = gr.Number(label="CREPE Hop Length", value=120)
        index_rate = gr.Slider(label="Index Rate", minimum=0, maximum=1, value=0.75)
        filter_radius = gr.Number(label="Filter Radius", value=3)
        rms_mix_rate = gr.Slider(label="RMS Mix Rate", minimum=0, maximum=1, value=0.25)
        protect = gr.Slider(label="Protect", minimum=0, maximum=1, value=0.33)
        
        split_infer = gr.Checkbox(label="Enable Split Inference", value=False)
        min_silence = gr.Number(label="Min Silence (ms)", value=500)
        silence_threshold = gr.Number(label="Silence Threshold (dB)", value=-50)
        seek_step = gr.Slider(label="Seek Step (ms)", minimum=1, maximum=10, value=1)
        keep_silence = gr.Number(label="Keep Silence (ms)", value=200)
        quefrency = gr.Number(label="Quefrency", value=0)
        timbre = gr.Number(label="Timbre", value=1)
        f0_autotune = gr.Checkbox(label="Enable F0 Autotune", value=False)
        output_format = gr.Dropdown(label="Output Format", choices=["wav", "flac", "mp3"], value="wav")

    output_audio = gr.Audio(label="Output Audio")

    submit_btn = gr.Button("Run Inference")

    # Define the interaction between input and function
    submit_btn.click(fn=inference, 
                     inputs=[model_name, audio_input, f0_change, f0_method, min_pitch, max_pitch, 
                             crepe_hop_length, index_rate, filter_radius, rms_mix_rate, protect, 
                             split_infer, min_silence, silence_threshold, seek_step, keep_silence, 
                             quefrency, timbre, f0_autotune, output_format], 
                     outputs=output_audio)

# Launch the demo
demo.launch()