import gradio as gr
import base64
import numpy as np
from scipy.io import wavfile
from voice_processing import tts, get_model_names, voice_mapping
from io import BytesIO
import asyncio
from pydub import AudioSegment

async def convert_tts(model_name, tts_text, selected_voice, slang_rate, use_uploaded_voice, voice_upload):
    edge_tts_voice = voice_mapping.get(selected_voice)
    if not edge_tts_voice:
        return {"error": f"Invalid voice '{selected_voice}'."}, None

    voice_upload_file = None
    if use_uploaded_voice and voice_upload is not None:
        with open(voice_upload.name, 'rb') as f:
            voice_upload_file = f.read()

    # Process the text input or uploaded voice
    info, edge_tts_output_path, tts_output_data, edge_output_file = await tts(
        model_name, tts_text, edge_tts_voice, slang_rate, use_uploaded_voice, voice_upload_file
    )

    _, audio_output = tts_output_data

    # Return audio data as bytes
    audio_bytes = None
    if isinstance(audio_output, np.ndarray):
        byte_io = BytesIO()
        wavfile.write(byte_io, 40000, audio_output)
        byte_io.seek(0)
        audio_segment = AudioSegment.from_wav(byte_io)
        mp3_bytes = audio_segment.export(format="mp3").read()
        audio_bytes = mp3_bytes
    else:
        audio_segment = AudioSegment.from_file(BytesIO(audio_output), format="wav")
        mp3_bytes = audio_segment.export(format="mp3").read()
        audio_bytes = mp3_bytes

    return audio_bytes

def get_models():
    return get_model_names()

def get_voices():
    return list(voice_mapping.keys())

iface = gr.Interface(
    fn=convert_tts,
    inputs=[
        gr.Dropdown(choices=get_models(), label="Model", interactive=True),
        gr.Textbox(label="Text", placeholder="Enter text here"),
        gr.Dropdown(choices=get_voices(), label="Voice", interactive=True),
        gr.Slider(minimum=0, maximum=1, step=0.01, label="Slang Rate"),
        gr.Checkbox(label="Use Uploaded Voice"),
        gr.File(label="Voice File")
    ],
    outputs=[
        gr.Audio(label="Result Audio",type="filepath")

        
    ],
    title="Text-to-Speech Conversion"
)

iface.launch()