app.py

import os
import io
import asyncio
import time
import shutil
import numpy as np
import psutil
import soundfile as sf
import subprocess
import tempfile
from concurrent.futures import ThreadPoolExecutor
from typing import Optional, Generator
from contextlib import asynccontextmanager
import logging
import aiofiles 
import torch
from fastapi import FastAPI, HTTPException, UploadFile, File, Form, Query
from fastapi.responses import Response, StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field

# Ensure the cloned neutts-air repository is in the path
import sys
sys.path.append(os.path.join(os.getcwd(), 'neutts-air'))
from neuttsair.neutts import NeuTTSAir

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("NeuTTS-API")

# --- Configuration & Utility Functions ---

# Explicitly use CPU as per Dockerfile and Hugging Face free tier compatibility
DEVICE = "cpu" 
# Configure Max Workers for concurrent synthesis threads (1-2 is safe for CPU-only)
MAX_WORKERS = 2
tts_executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)
SAMPLE_RATE = 24000
CLEANUP_THRESHOLD = 3600 # 1 hour in seconds
TEMP_AUDIO_DIR = "temp_audio"
GENERATED_AUDIO_DIR = "generated_audio"
os.makedirs(TEMP_AUDIO_DIR, exist_ok=True)
os.makedirs(GENERATED_AUDIO_DIR, exist_ok=True)

class TTSRequestModel(BaseModel):
    """Model for non-file inputs to synthesis and streaming."""
    text: str = Field(..., min_length=1, max_length=1000)
    speed: float = Field(default=1.0, ge=0.5, le=2.0)
    output_format: str = Field(default="wav", pattern="^(wav|mp3|flac)$")


def convert_to_wav_blocking(input_path: str) -> str:
    """
    NEW FUNCTION: Uses FFmpeg to convert any uploaded audio format (WebM, MP4, etc.) 
    to a 24kHz, 16-bit PCM WAV file, which is required by soundfile/libsndfile.
    This function must run in the ThreadPoolExecutor.
    """
    # Create a unique temporary filename for the converted WAV file
    # We use tempfile.NamedTemporaryFile to safely create a path
    # and then delete the file handle so ffmpeg can write to it.
    with tempfile.NamedTemporaryFile(suffix=".wav", dir=TEMP_AUDIO_DIR, delete=False) as tmp:
        output_path = tmp.name
        
    logger.info(f"Converting '{os.path.basename(input_path)}' to WAV (24kHz, mono) at {os.path.basename(output_path)}")

    # FFmpeg command details:
    # -y: overwrite output file if it exists
    # -i: input file path
    # -f wav: output format is WAV
    # -ar 24000: set sample rate to 24000 (required by NeuTTS)
    # -ac 1: set audio channels to 1 (mono)
    # -c:a pcm_s16le: set codec to uncompressed 16-bit PCM (standard WAV)
    command = [
        "ffmpeg", 
        "-y", 
        "-i", input_path, 
        "-f", "wav",
        "-ar", str(SAMPLE_RATE), 
        "-ac", "1", 
        "-c:a", "pcm_s16le", 
        output_path
    ]

    try:
        # Run the FFmpeg command
        # Use a short timeout to prevent runaway processes
        result = subprocess.run(command, check=True, capture_output=True, text=True, timeout=30)
        logger.info(f"FFmpeg conversion successful.")
        return output_path
    except subprocess.CalledProcessError as e:
        logger.error(f"FFmpeg conversion failed: {e.stderr}")
        # Clean up the output path if FFmpeg failed to write it
        if os.path.exists(output_path):
            os.unlink(output_path)
        # Provide the last line of the FFmpeg error to the user
        error_detail = e.stderr.splitlines()[-1] if e.stderr else "Unknown FFmpeg error."
        raise HTTPException(status_code=400, detail=f"Audio format conversion failed: {error_detail}")
    except subprocess.TimeoutExpired:
        logger.error("FFmpeg conversion timed out.")
        if os.path.exists(output_path):
            os.unlink(output_path)
        raise HTTPException(status_code=504, detail="Audio conversion timed out after 30 seconds.")
    except Exception as e:
        logger.error(f"General conversion error: {e}")
        if os.path.exists(output_path):
            os.unlink(output_path)
        raise HTTPException(status_code=500, detail="An unexpected error occurred during audio conversion.")
# --- Model Wrapper and Logic ---

class NeuTTSWrapper:
    def __init__(self, device: str = "cpu"):
        self.tts_model = None
        self.device = device
        self.load_model()

    def load_model(self):
        try:
            logger.info(f"Loading NeuTTSAir model on device: {self.device}")
            # Ensure we respect the CPU configuration
            self.tts_model = NeuTTSAir(backbone_device=self.device, codec_device=self.device)
            logger.info("✅ NeuTTSAir model loaded successfully.")
        except Exception as e:
            logger.error(f"❌ Model loading failed: {e}")
            raise

    def _convert_to_streamable_format(self, audio_data: np.ndarray, audio_format: str) -> bytes:
        """Converts NumPy audio array to streamable bytes in the specified format."""
        audio_buffer = io.BytesIO()
        try:
            sf.write(audio_buffer, audio_data, SAMPLE_RATE, format=audio_format)
        except Exception as e:
            logger.error(f"Failed to write audio data to format {audio_format}: {e}")
            raise
        audio_buffer.seek(0)
        return audio_buffer.read()

    def _split_text_into_chunks(self, text: str) -> list[str]:
        """Simple sentence splitting for streaming (can be enhanced with regex)."""
        sentences = [s.strip() for s in text.split('.') if s.strip()]
        if not sentences:
            sentences = [text.strip()]
        return sentences

    def generate_speech_blocking(self, text: str, ref_audio_path: str, reference_text: str) -> np.ndarray:
        """Blocking synthesis for standard endpoint."""
        
        
        ref_s = self.tts_model.encode_reference(ref_audio_path)
        
        # 3. Infer full text
        with torch.no_grad():
            audio = self.tts_model.infer(text, ref_s, reference_text)
        return audio

    def stream_producer(self, queue: asyncio.Queue, text: str, ref_audio_path: str, reference_text: str):
        """
        [PRODUCER] Runs in a thread, generates audio chunks, and puts them into a queue.
        """
        try:
            logger.info("Starting audio production thread...")
            ref_s = self.tts_model.encode_reference(ref_audio_path)
            sentences = self._split_text_into_chunks(text)
            
            for i, sentence in enumerate(sentences):
                if not sentence.strip():
                    continue
                
                logger.debug(f"Producing chunk {i+1}/{len(sentences)}: '{sentence[:30]}...'")
                
                with torch.no_grad():
                    audio_chunk = self.tts_model.infer(sentence, ref_s, reference_text)
                
                queue.put_nowait(audio_chunk)
                
        except Exception as e:
            logger.error(f"Error in producer thread: {e}")
            queue.put_nowait(e)
        finally:
            queue.put_nowait(None)

async def stream_consumer(queue: asyncio.Queue, output_format: str):
    """
    [CONSUMER] Asynchronously gets items from the queue and yields them to the client.
    """
    logger.info("Starting audio consumption...")
    while True:
        # Wait for an item to appear in the queue
        item = await queue.get()
        
        if isinstance(item, Exception):
            logger.error(f"Consumer received an error from the producer: {item}")
            break
            
        if item is None:
            # Sentinel value received, meaning the stream is finished
            logger.info("Consumer received end-of-stream signal.")
            break
        
        # We have a valid audio chunk, convert it to the desired format
        audio_bytes = await run_blocking_task_async(
            app.state.tts_wrapper._convert_to_streamable_format,
            item, # The NumPy array from the queue
            output_format
        )
        yield audio_bytes
# --- Asynchronous Offloading ---

async def run_blocking_task_async(func, *args, **kwargs):
    """Offloads a blocking function call to the ThreadPoolExecutor."""
    loop = asyncio.get_event_loop()
    return await loop.run_in_executor(
        tts_executor,
        lambda: func(*args, **kwargs)
    )

async def save_upload_file_async(upload_file: UploadFile) -> str:
    """Asynchronously saves the UploadFile to disk."""
    temp_filename = os.path.join(TEMP_AUDIO_DIR, f"{time.time()}_{upload_file.filename}")
    try:
        # Use asyncio to read the file chunks in a non-blocking manner
        async with aiofiles.open(temp_filename, 'wb') as out_file:
            while content := await upload_file.read(1024 * 1024):
                await out_file.write(content)
        return temp_filename
    except Exception as e:
        logger.error(f"Error saving file: {e}")
        raise HTTPException(status_code=500, detail="Could not save reference audio file")

# --- FastAPI Lifespan Manager (Kokoro Feature) ---

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Modern lifespan management: initialize model on startup, shutdown executor."""
    try:
        app.state.tts_wrapper = NeuTTSWrapper(device=DEVICE)
    except Exception as e:
        logger.error(f"Fatal startup error: {e}")
        # Terminate the application if the model can't load
        tts_executor.shutdown(wait=False) 
        raise RuntimeError("Model initialization failed.")
    
    yield # Application serves requests
    
    # Shutdown
    logger.info("Shutting down ThreadPoolExecutor.")
    tts_executor.shutdown(wait=False)

# --- FastAPI Application Setup ---
app = FastAPI(
    title="NeuTTS Air Instant Cloning API", 
    version="2.0.0-PROD-ENHANCED", 
    docs_url="/docs", 
    lifespan=lifespan
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# --- New Endpoints and Enhancements ---

@app.get("/")
async def root():
    return {"message": "NeuTTS Air API v2.0 - Ready for Instant Voice Cloning"}

@app.get("/health")
async def health_check():
    """Enhanced health check (Kokoro Feature + Original Metrics)"""
    mem = psutil.virtual_memory()
    disk = psutil.disk_usage('/')
    
    return {
        "status": "healthy",
        "model_loaded": hasattr(app.state, 'tts_wrapper') and app.state.tts_wrapper.tts_model is not None,
        "device": DEVICE,
        "concurrency_limit": MAX_WORKERS,
        "memory_usage": {
            "total_gb": round(mem.total / (1024**3), 2),
            "used_percent": mem.percent
        },
        "disk_usage": {
            "total_gb": round(disk.total / (1024**3), 2),
            "used_percent": disk.percent
        }
    }

@app.delete("/cleanup")
async def cleanup_files():
    """Maintenance endpoint to remove old generated and temporary files."""
    await run_blocking_task_async(cleanup_files_blocking)
    return {"message": "Cleanup initiated successfully."}

def cleanup_files_blocking():
    """Blocking file cleanup logic (original NeuTTS feature)."""
    now = time.time()
    deleted_count = 0
    
    for directory in [GENERATED_AUDIO_DIR, TEMP_AUDIO_DIR]:
        for filename in os.listdir(directory):
            filepath = os.path.join(directory, filename)
            if os.path.isfile(filepath):
                try:
                    # Original cleanup logic: delete if older than CLEANUP_THRESHOLD
                    if now - os.path.getctime(filepath) > CLEANUP_THRESHOLD:
                        os.remove(filepath)
                        deleted_count += 1
                except Exception as e:
                    logger.warning(f"Failed to delete {filepath}: {e}")
    
    logger.info(f"Cleanup completed: {deleted_count} files removed.")
    return deleted_count


# --- Core Synthesis Endpoints ---

@app.post("/synthesize", response_class=Response)
async def text_to_speech(
    text: str = Form(...),
    reference_text: str = Form(...),
    speed: float = Form(1.0, ge=0.5, le=2.0),
    output_format: str = Form("wav", pattern="^(wav|mp3|flac)$"),
    reference_audio: UploadFile = File(...)):
    """
    Standard blocking TTS endpoint with Multi-Format Output (Kokoro Feature).
    Includes FFmpeg conversion for uploaded audio format compatibility.
    """
    if not hasattr(app.state, 'tts_wrapper'):
        raise HTTPException(status_code=503, detail="Service unavailable: Model not loaded")
    
    # 1. Asynchronously save reference audio (original upload)
    temp_ref_path = await save_upload_file_async(reference_audio)
    converted_wav_path = None # NEW: Initialize for cleanup
    start_time = time.time()
        
    try:
        # 2. **NEW STEP**: Convert the uploaded file (WebM, etc.) to a 24kHz WAV file using FFmpeg
        converted_wav_path = await run_blocking_task_async(
            convert_to_wav_blocking,
            temp_ref_path
        )

        # 3. Offload the ENTIRE blocking process (encode + infer) to a thread
        audio_data = await run_blocking_task_async(
            app.state.tts_wrapper.generate_speech_blocking,
            text,
            converted_wav_path, # IMPORTANT: Pass the CONVERTED WAV path
            reference_text
        )
                
        # 4. Convert to requested format (Blocking, but usually fast)
        audio_bytes = await run_blocking_task_async(
            app.state.tts_wrapper._convert_to_streamable_format,
            audio_data,
            output_format
        )
                
        # 5. Save to disk (Original NeuTTS requirement)
        audio_filename = f"tts_{time.time()}.{output_format}"
        final_path = os.path.join(GENERATED_AUDIO_DIR, audio_filename)
        await run_blocking_task_async(
            lambda: open(final_path, 'wb').write(audio_bytes)
        )
                
        processing_time = time.time() - start_time
        audio_duration = len(audio_data) / SAMPLE_RATE
        return Response(
            content=audio_bytes,
            media_type=f"audio/{'mpeg' if output_format == 'mp3' else output_format}",
            headers={
                "Content-Disposition": f"attachment; filename={audio_filename}",
                "X-Processing-Time": f"{processing_time:.2f}s",
                "X-Audio-Duration": f"{audio_duration:.2f}s"
            }
        )
    except Exception as e:
        logger.error(f"Synthesis error: {e}")
        # Reraise HTTPExceptions that may have come from the conversion step
        if isinstance(e, HTTPException):
             raise
        raise HTTPException(status_code=500, detail=f"Synthesis failed: {e}")
    finally:
        # 6. Clean up BOTH the original file AND the converted WAV file
        if os.path.exists(temp_ref_path):
            os.unlink(temp_ref_path)
        if converted_wav_path and os.path.exists(converted_wav_path):
            os.unlink(converted_wav_path)

@app.post("/synthesize/stream")
async def stream_text_to_speech_cloning(
    text: str = Form(..., min_length=1, max_length=5000),
    reference_text: str = Form(...),
    output_format: str = Form("mp3", pattern="^(wav|mp3|flac)$"),
    reference_audio: UploadFile = File(...)
):
    """
    TRUE streaming endpoint using the definitive producer-consumer pattern.
    """
    if not hasattr(app.state, 'tts_wrapper'):
        raise HTTPException(status_code=503, detail="Service unavailable: Model not loaded")

    temp_ref_path = await save_upload_file_async(reference_audio)
    
    async def cleanup_and_run_stream():
        """A nested async generator to handle the entire producer-consumer lifecycle and cleanup."""
        converted_wav_path = None
        queue = asyncio.Queue()
        loop = asyncio.get_event_loop()
        try:
            # Convert the uploaded file to the required WAV format
            converted_wav_path = await run_blocking_task_async(convert_to_wav_blocking, temp_ref_path)
            
            # Start the producer (the model) in a background thread.
            # It will start putting audio chunks into the queue.
            loop.run_in_executor(
                tts_executor,
                app.state.tts_wrapper.stream_producer,
                queue, text, converted_wav_path, reference_text
            )
            
            # Start the consumer, which gets chunks from the queue and yields them to the client.
            async for chunk in stream_consumer(queue, output_format):
                yield chunk

        finally:
            # This block guarantees cleanup after the stream is finished or fails
            if os.path.exists(temp_ref_path):
                os.unlink(temp_ref_path)
            if converted_wav_path and os.path.exists(converted_wav_path):
                os.unlink(converted_wav_path)
            logger.info("Cleaned up temporary stream files.")

    return StreamingResponse(
        cleanup_and_run_stream(),
        media_type=f"audio/{'mpeg' if output_format == 'mp3' else output_format}",
        headers={
            "Content-Disposition": "attachment; filename=tts_live_stream.mp3",
            "Cache-Control": "no-cache",
            "X-Accel-Buffering": "no" # Header to prevent proxy buffering
        }
    )

@app.get("/audio/{filename}")
async def get_audio(filename: str):
    """Original NeuTTS feature to serve generated audio files."""
    file_path = os.path.join(GENERATED_AUDIO_DIR, filename)
    if not os.path.exists(file_path):
        raise HTTPException(status_code=404, detail="Audio file not found")
    
    return Response(
        content=open(file_path, "rb").read(),
        media_type=f"audio/{filename.split('.')[-1]}", # Simple media type detection
        headers={"Content-Disposition": f"attachment; filename={filename}"}
    )