if voice_b != "disabled":
    voices.append(voice_b)
if voice_c != "disabled":
    voices.append(voice_c)

if emotion != "None/Custom":
    text = f"[I am really {emotion.lower()},] {text}"
elif prompt.strip() != "":
    text = f"[{prompt},] {text}"

c = None
if voice == "custom_voice":
    if mic_audio is None:
        raise gr.Error("Please provide audio from mic when choosing custom voice")
    c = load_audio(mic_audio, 22050)

if len(voices) == 1 or len(voices) == 0:
    if voice == "custom_voice":
        voice_samples, conditioning_latents = [c], None
    else:
        voice_samples, conditioning_latents = load_voice(voice)
else:
    voice_samples, conditioning_latents = load_voices(voices)
    if voice == "custom_voice":
        voice_samples.extend([c])

sample_voice = voice_samples[0] if len(voice_samples) else None

start_time = time.time()
gen, _ = tts.tts_with_preset(
    text,
    voice_samples=voice_samples,
    conditioning_latents=conditioning_latents,
    preset=preset,
    use_deterministic_seed=seed,
    return_deterministic_state=True,
    k=3,
)

with open("Tortoise_TTS_Runs.log", "a") as f:
    f.write(
        f"{datetime.now()} | Voice: {','.join(voices)} | Text: {text} | Quality: {preset} | Time Taken (s): {time.time()-start_time} | Seed: {seed}\n"
    )

return (
    (22050, sample_voice.squeeze().cpu().numpy()),
    (24000, gen[0].squeeze().cpu().numpy()),
    (24000, gen[1].squeeze().cpu().numpy()),
    (24000, gen[2].squeeze().cpu().numpy()),
)

# Interface components
text = gr.Textbox(lines=4, label="Text:")
emotion = gr.Radio(
    ["None/Custom", "Happy", "Sad", "Angry", "Disgusted", "Arrogant"],
    value="None/Custom",
    label="Select emotion:",
    type="value",
)
prompt = gr.Textbox(lines=1, label="Enter prompt if [Custom] emotion:")
preset = gr.Radio(
    ["ultra_fast", "fast", "standard", "high_quality"],
    value="fast",
    label="Preset mode (determines quality with tradeoff over speed):",
    type="value",
)
voice = gr.Dropdown(
    os.listdir(os.path.join("tortoise", "voices")) + VOICE_OPTIONS,
    value="angie",  # Default voice
    label="Select voice:",
    type="value",
)
mic_audio = gr.Audio(
    label="Record voice (when selected custom_voice):",
    type="filepath"
)
voice_b = gr.Dropdown(
    os.listdir(os.path.join("tortoise", "voices")) + VOICE_OPTIONS,
    value="disabled",
    label="(Optional) Select second voice:",
    type="value",
)
voice_c = gr.Dropdown(
    os.listdir(os.path.join("tortoise", "voices")) + VOICE_OPTIONS,
    value="disabled",
    label="(Optional) Select third voice:",
    type="value",
)
seed = gr.Number(value=0, precision=0, label="Seed (for reproducibility):")

selected_voice = gr.Audio(label="Sample of selected voice (first):")
output_audio_1 = gr.Audio(label="Output [Candidate 1]:")
output_audio_2 = gr.Audio(label="Output [Candidate 2]:")
output_audio_3 = gr.Audio(label="Output [Candidate 3]:")

# Create the Gradio interface
interface = gr.Interface(
    fn=inference,
    inputs=[text, emotion, prompt, voice, mic_audio, voice_b, voice_c, preset, seed],
    outputs=[selected_voice, output_audio_1, output_audio_2, output_audio_3],
    title="RJ VOICE CLONING",
    description=title_html,
    css=".gradio-container { background-color: black; color: orange; }"
)

# Launch the interface
interface.launch(share=True)

with open("Tortoise_TTS_Runs.log", "a") as f:
    f.write(
        f"\n\n-------------------------Tortoise TTS Logs, {datetime.now()}-------------------------\n"
    )

main()