main.py

from contextlib import asynccontextmanager
from fastapi import FastAPI, WebSocket, WebSocketDisconnect, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import asyncio
import logging
import os
import traceback
import argparse
import uvicorn
import numpy as np
import librosa
import io

from core import WhisperLiveKit
from audio_processor import AudioProcessor
from language_detector import LanguageDetector

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logging.getLogger().setLevel(logging.WARNING)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)

kit = None
language_detector = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    global kit, language_detector
    kit = WhisperLiveKit()
    language_detector = LanguageDetector(model_name="tiny")
    yield

app = FastAPI(lifespan=lifespan)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allows all origins
    allow_credentials=True,
    allow_methods=["*"],  # Allows all methods
    allow_headers=["*"],  # Allows all headers
)

# Mount static files
app.mount("/static", StaticFiles(directory="static"), name="static")



@app.get("/")
async def read_root():
    return FileResponse("static/index.html")

@app.get("/health")
async def health_check():
    return JSONResponse({"status": "healthy"})

@app.post("/detect-language")
async def detect_language(file: UploadFile = File(...)):
    try:
        # Create uploads directory if it doesn't exist
        os.makedirs("uploads", exist_ok=True)
        
        # Save the uploaded file
        file_path = os.path.join("uploads", file.filename)
        with open(file_path, "wb") as buffer:
            contents = await file.read()
            buffer.write(contents)
        
        # Use the language detector with the saved file
        if language_detector:
            detected_lang, confidence = language_detector.detect_language_from_file(file_path)
            
            # Clean up - remove the temporary file
            os.remove(file_path)
            
            return JSONResponse({
                "language": detected_lang,
                "confidence": float(confidence)
            })
        else:
            return JSONResponse(
                {"error": "Language detector not initialized"},
                status_code=500
            )
            
    except Exception as e:
        logger.error(f"Error in language detection: {e}")
        logger.error(f"Traceback: {traceback.format_exc()}")
        # Clean up in case of error
        if 'file_path' in locals() and os.path.exists(file_path):
            os.remove(file_path)
        return JSONResponse(
            {"error": str(e)},
            status_code=500
        )

async def handle_websocket_results(websocket, results_generator):
    """Consumes results from the audio processor and sends them via WebSocket."""
    try:
        async for response in results_generator:
            try:
                logger.debug(f"Sending response: {response}")
                if isinstance(response, dict):
                    # Ensure the response has a consistent format
                    if 'buffer_transcription' in response:
                        await websocket.send_json({
                            'buffer_transcription': response['buffer_transcription']
                        })
                    elif 'full_transcription' in response:
                        await websocket.send_json({
                            'full_transcription': response['full_transcription']
                        })
                    else:
                        await websocket.send_json(response)
                else:
                    # If response is not a dict, wrap it in a text field
                    await websocket.send_json({"text": str(response)})
            except Exception as e:
                logger.error(f"Error sending message: {e}")
                logger.error(f"Traceback: {traceback.format_exc()}")
                raise
    except Exception as e:
        logger.warning(f"Error in WebSocket results handler: {e}")
        logger.warning(f"Traceback: {traceback.format_exc()}")

@app.websocket("/asr")
async def websocket_endpoint(websocket: WebSocket):
    logger.info("New WebSocket connection request")
    audio_processor = None
    websocket_task = None

    try:
        await websocket.accept()
        logger.info("WebSocket connection accepted")
        
        audio_processor = AudioProcessor()
        results_generator = await audio_processor.create_tasks()
        websocket_task = asyncio.create_task(handle_websocket_results(websocket, results_generator))

        while True:
            try:
                message = await websocket.receive_bytes()
                logger.debug(f"Received audio chunk of size: {len(message)}")
                await audio_processor.process_audio(message)
            except WebSocketDisconnect:
                logger.info("WebSocket connection closed")
                break
            except Exception as e:
                logger.error(f"Error processing WebSocket message: {e}")
                logger.error(f"Traceback: {traceback.format_exc()}")
                break

    except Exception as e:
        logger.error(f"Error in WebSocket endpoint: {e}")
        logger.error(f"Traceback: {traceback.format_exc()}")
    finally:
        if audio_processor:
            await audio_processor.cleanup()
        if websocket_task:
            websocket_task.cancel()
            try:
                await websocket_task
            except asyncio.CancelledError:
                pass

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="0.0.0.0", help="Host to run the server on")
    parser.add_argument("--port", type=int, default=8000, help="Port to run the server on")
    parser.add_argument("--model", type=str, default="base", help="Whisper model to use")
    parser.add_argument("--backend", type=str, default="faster-whisper", help="Backend to use")
    parser.add_argument("--task", type=str, default="transcribe", help="Task to perform")
    args = parser.parse_args()

    uvicorn.run(app, host=args.host, port=args.port)