# import os
# import uuid
# import time
# import torch
# import gradio as gr
# os.environ["NUMBA_DISABLE_CACHE"] = "1"
# import mecab_patch
# import english_patch
# from melo.api import TTS
# from openvoice.api import ToneColorConverter

# # Set temporary cache locations for Hugging Face Spaces
# os.environ["TORCH_HOME"] = "/tmp/torch"
# os.environ["HF_HOME"] = "/tmp/huggingface"
# os.environ["HF_HUB_CACHE"] = "/tmp/huggingface"
# os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
# os.environ["MPLCONFIGDIR"] = "/tmp"
# os.environ["XDG_CACHE_HOME"] = "/tmp"
# os.environ["XDG_CONFIG_HOME"] = "/tmp"
# os.environ["NUMBA_DISABLE_CACHE"] = "1"
# os.makedirs("/tmp/torch", exist_ok=True)
# os.makedirs("/tmp/huggingface", exist_ok=True)
# os.makedirs("/tmp/flagged", exist_ok=True)

# # Output folder
# output_dir = "/tmp/outputs"
# os.makedirs(output_dir, exist_ok=True)

# # Initialize tone converter
# ckpt_converter = "checkpoints/converter/config.json"
# tone_color_converter = ToneColorConverter(ckpt_converter)

# # Device setting
# device = "cuda" if torch.cuda.is_available() else "cpu"

# def clone_and_speak(text, speaker_wav):
#     if not speaker_wav:
#         return "Please upload a reference .wav file."

#     # import melo.text.english as english
#     # original_g2p = english.g2p

#     # def patched_g2p(text):
#     #     phones, tones, word2ph = original_g2p(text)
#     #     # Fix: wrap ints in list to avoid TypeError
#     #     word2ph_fixed = []
#     #     for item in word2ph:
#     #         if isinstance(item, int):
#     #             word2ph_fixed.append([item])
#     #         else:
#     #             word2ph_fixed.append(item)
#     #     return phones, tones, word2ph_fixed

#     # english.g2p = patched_g2p

#     base_name = f"output_{int(time.time())}_{uuid.uuid4().hex[:6]}"
#     tmp_melo_path = f"{output_dir}/{base_name}_tmp.wav"
#     final_output_path = f"{output_dir}/{base_name}_converted.wav"

#     # Use English speaker model
#     model = TTS(language="EN", device=device)
#     speaker_ids = model.hps.data.spk2id
#     default_speaker_id = next(iter(speaker_ids.values()))

#     # Generate base TTS voice
#     speed = 1.0
#     model.tts_to_file(text, default_speaker_id, tmp_melo_path,speed=speed)

#     # Use speaker_wav as reference to extract style embedding
#     from openvoice import se_extractor
#     ref_se, _ = se_extractor.get_se(speaker_wav, tone_color_converter, vad=False)

#     # Run the tone conversion
#     tone_color_converter.convert(
#         audio_src_path=tmp_melo_path,
#         src_se=ref_se,
#         tgt_se=ref_se,
#         output_path=final_output_path,
#         message="@HuggingFace",
#     )

#     return final_output_path

# # Gradio interface
# gr.Interface(
#     fn=clone_and_speak,
#     inputs=[
#         gr.Textbox(label="Enter Text"),
#         gr.Audio(type="filepath", label="Upload a Reference Voice (.wav)")
#     ],
#     outputs=gr.Audio(label="Synthesized Output"),
#     flagging_dir="/tmp/flagged",
#     title="Text to Voice using Melo TTS + OpenVoice",
#     description="Use Melo TTS for base synthesis and OpenVoice to apply a reference speaker's tone.",
# ).launch()


import os
import time
import uuid
import gradio as gr

from TTS.api import TTS
from openvoice import se_extractor, ToneColorConverter

# Import your local english.py logic
from meloTTS import english

# Paths
device = "cuda" if os.system("nvidia-smi") == 0 else "cpu"
output_dir = "outputs"
os.makedirs(output_dir, exist_ok=True)

# Load OpenVoice tone converter
tone_color_converter = ToneColorConverter(f"{os.getcwd()}/checkpoints", device=device)
tone_color_converter.load_model()

def clone_and_speak(text, speaker_wav):
    if not speaker_wav:
        return "Please upload a reference .wav file."

    base_name = f"output_{int(time.time())}_{uuid.uuid4().hex[:6]}"
    tmp_melo_path = f"{output_dir}/{base_name}_tmp.wav"
    final_output_path = f"{output_dir}/{base_name}_converted.wav"

    # Use English speaker model
    model = TTS(language="EN", device=device)
    speaker_ids = model.hps.data.spk2id
    default_speaker_id = next(iter(speaker_ids.values()))

    # Generate base TTS voice
    model.tts_to_file(text, speaker_id=default_speaker_id, file_path=tmp_melo_path, speed=1.0)

    # Extract style embedding
    ref_se, _ = se_extractor.get_se(speaker_wav, tone_color_converter, vad=False)

    # Convert tone
    tone_color_converter.convert(
        audio_src_path=tmp_melo_path,
        src_se=ref_se,
        tgt_se=ref_se,
        output_path=final_output_path,
        message="@HuggingFace"
    )

    return final_output_path

# Gradio Interface
demo = gr.Interface(
    fn=clone_and_speak,
    inputs=[
        gr.Textbox(label="Text to Synthesize"),
        gr.Audio(label="Reference Voice (WAV)", type="filepath")
    ],
    outputs=gr.Audio(label="Cloned Voice Output"),
    title="Voice Cloner with MeloTTS + OpenVoice"
)

if __name__ == "__main__":
    demo.launch()