app.py

import gradio as gr
import torch
import os
from TTS.api import TTS
from huggingface_hub import hf_hub_download

# --- ROMANIZER IMPORT ---
try:
    from romanizer import sinhala_to_roman
except ImportError:
    def sinhala_to_roman(text): return text

# --- CONSOLIDATED MODEL LOADING ---
def load_standard_model(repo_id):
    model_path = hf_hub_download(repo_id=repo_id, filename="best_model.pth")
    config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
    return TTS(model_path=model_path, config_path=config_path, gpu=False)

def load_eng_model_with_surgery():
    repo_id = "E-motionAssistant/text-to-speech-VITS-english"
    model_path = hf_hub_download(repo_id=repo_id, filename="best_model.pth")
    config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
    checkpoint = torch.load(model_path, map_location="cpu")
    raw_weights = checkpoint['model']['text_encoder.emb.weight']
    if raw_weights.shape[0] == 137:
        checkpoint['model']['text_encoder.emb.weight'] = raw_weights[:131, :]
        fixed_model_path = "fixed_eng_model.pth"
        torch.save(checkpoint, fixed_model_path)
        return TTS(model_path=fixed_model_path, config_path=config_path, gpu=False)
    return TTS(model_path=model_path, config_path=config_path, gpu=False)

# --- INITIALIZATION ---
print("Loading all models... this may take a moment.")
models = {
    "sinhala": load_standard_model("E-motionAssistant/text-to-speech-VITS-sinhala"),
    "tamil": load_standard_model("E-motionAssistant/text-to-speech-VITS-tamil"),
    "english": load_eng_model_with_surgery()
}

# --- SPECIFIC ENDPOINT FUNCTIONS ---
def tts_english(text):
    output = "english_out.wav"
    models["english"].tts_to_file(text=text, file_path=output)
    return output

def tts_sinhala(text):
    processed = sinhala_to_roman(text)
    output = "sinhala_out.wav"
    models["sinhala"].tts_to_file(text=processed, file_path=output)
    return output

def tts_tamil(text):
    output = "tamil_out.wav"
    models["tamil"].tts_to_file(text=text, file_path=output)
    return output

# --- GRADIO UI WITH TABS ---
with gr.Blocks(title="Multilingual TTS API") as demo:
    gr.Markdown("# Trilingual TTS System")
    gr.Markdown("Choose a tab below to use a specific language endpoint.")
    
    with gr.Tab("English"):
        input_eng = gr.Textbox(label="English Text")
        output_eng = gr.Audio(label="English Audio", type="filepath")
        btn_eng = gr.Button("Synthesize English")
        # api_name creates a specific endpoint: /api/predict/english_tts
        btn_eng.click(tts_english, inputs=input_eng, outputs=output_eng, api_name="english_tts")

    with gr.Tab("Sinhala"):
        input_sin = gr.Textbox(label="Sinhala Text (Input Unicode)")
        output_sin = gr.Audio(label="Sinhala Audio", type="filepath")
        btn_sin = gr.Button("Synthesize Sinhala")
        btn_sin.click(tts_sinhala, inputs=input_sin, outputs=output_sin, api_name="sinhala_tts")

    with gr.Tab("Tamil"):
        input_tam = gr.Textbox(label="Tamil Text")
        output_tam = gr.Audio(label="Tamil Audio", type="filepath")
        btn_tam = gr.Button("Synthesize Tamil")
        btn_tam.click(tts_tamil, inputs=input_tam, outputs=output_tam, api_name="tamil_tts")

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