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Update app.py

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import os
from fastapi import FastAPI, HTTPException, File, UploadFile, Depends, Security, Form
from fastapi.security.api_key import APIKeyHeader, APIKey
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from typing import Optional
import numpy as np
import io
import soundfile as sf
import base64
import logging
import torch
import librosa
from pathlib import Path
from pydub import AudioSegment
from moviepy.editor import VideoFileClip
import traceback
from logging.handlers import RotatingFileHandler
import boto3
from botocore.exceptions import NoCredentialsError
import time
import tempfile
import magic

# Import functions from other modules
from asr import transcribe, ASR_LANGUAGES, ASR_SAMPLING_RATE
from tts import synthesize, TTS_LANGUAGES
from lid import identify

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Add a file handler
file_handler = RotatingFileHandler('app.log', maxBytes=10000000, backupCount=5)
file_handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)

app = FastAPI(title="MMS: Scaling Speech Technology to 1000+ languages")

# S3 Configuration
S3_BUCKET = os.environ.get("S3_BUCKET")
S3_REGION = os.environ.get("S3_REGION")
S3_ACCESS_KEY_ID = os.environ.get("AWS_ACCESS_KEY_ID")
S3_SECRET_ACCESS_KEY = os.environ.get("AWS_SECRET_ACCESS_KEY")

# API Key Configuration
API_KEY = os.environ.get("API_KEY")
api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)

# Initialize S3 client
s3_client = boto3.client(
    's3',
    aws_access_key_id=S3_ACCESS_KEY_ID,
    aws_secret_access_key=S3_SECRET_ACCESS_KEY,
    region_name=S3_REGION
)

# Define request models
class AudioRequest(BaseModel):
    audio: str  # Base64 encoded audio or video data
    language: Optional[str] = None

class TTSRequest(BaseModel):
    text: str
    language: Optional[str] = None
    speed: float = 1.0

class LanguageRequest(BaseModel):
    language: Optional[str] = None

async def get_api_key(api_key_header: str = Security(api_key_header)):
    if api_key_header == API_KEY:
        return api_key_header
    raise HTTPException(status_code=403, detail="Could not validate credentials")

def extract_audio_from_file(input_bytes):
    with tempfile.NamedTemporaryFile(delete=False, suffix='.tmp') as temp_file:
        temp_file.write(input_bytes)
        temp_file_path = temp_file.name

    try:
        # Log file info
        file_info = magic.from_file(temp_file_path, mime=True)
        logger.info(f"Received file of type: {file_info}")

        # Try reading with soundfile first
        try:
            audio_array, sample_rate = sf.read(temp_file_path)
            logger.info(f"Successfully read audio with soundfile. Shape: {audio_array.shape}, Sample rate: {sample_rate}")
            return audio_array, sample_rate
        except Exception as e:
            logger.info(f"Could not read with soundfile: {str(e)}")

        # Try reading as video
        try:
            video = VideoFileClip(temp_file_path)
            audio = video.audio
            if audio is not None:
                audio_array = audio.to_soundarray()
                sample_rate = audio.fps
                audio_array = audio_array.mean(axis=1) if len(audio_array.shape) > 1 and audio_array.shape[1] > 1 else audio_array
                audio_array = audio_array.astype(np.float32)
                audio_array /= np.max(np.abs(audio_array))
                video.close()
                logger.info(f"Successfully extracted audio from video. Shape: {audio_array.shape}, Sample rate: {sample_rate}")
                return audio_array, sample_rate
            else:
                logger.info("Video file contains no audio")
        except Exception as e:
            logger.info(f"Could not read as video: {str(e)}")

        # Try reading with pydub
        try:
            audio = AudioSegment.from_file(temp_file_path)
            audio_array = np.array(audio.get_array_of_samples())
            audio_array = audio_array.astype(np.float32) / (2**15 if audio.sample_width == 2 else 2**7)
            audio_array = audio_array.reshape((-1, 2)).mean(axis=1) if audio.channels == 2 else audio_array
            logger.info(f"Successfully read audio with pydub. Shape: {audio_array.shape}, Sample rate: {audio.frame_rate}")
            return audio_array, audio.frame_rate
        except Exception as e:
            logger.info(f"Could not read with pydub: {str(e)}")

        raise ValueError(f"Unsupported file format: {file_info}")
    finally:
        os.unlink(temp_file_path)

@app.post("/transcribe")
async def transcribe_audio(request: AudioRequest, api_key: APIKey = Depends(get_api_key)):
    start_time = time.time()
    try:
        input_bytes = base64.b64decode(request.audio)
        audio_array, sample_rate = extract_audio_from_file(input_bytes)

        # Ensure audio_array is float32
        audio_array = audio_array.astype(np.float32)

        # Resample if necessary
        if sample_rate != ASR_SAMPLING_RATE:
            audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=ASR_SAMPLING_RATE)

        if request.language is None:
            # If no language is provided, use language identification
            identified_language = identify(audio_array)
            result = transcribe(audio_array, identified_language)
        else:
            result = transcribe(audio_array, request.language)

        processing_time = time.time() - start_time
        return JSONResponse(content={"transcription": result, "processing_time_seconds": processing_time})
    except Exception as e:
        logger.error(f"Error in transcribe_audio: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred during transcription", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.post("/transcribe_file")
async def transcribe_audio_file(
    file: UploadFile = File(...),
    language: Optional[str] = Form(None),
    api_key: APIKey = Depends(get_api_key)
):
    start_time = time.time()
    try:
        contents = await file.read()
        audio_array, sample_rate = extract_audio_from_file(contents)

        # Ensure audio_array is float32
        audio_array = audio_array.astype(np.float32)

        # Resample if necessary
        if sample_rate != ASR_SAMPLING_RATE:
            audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=ASR_SAMPLING_RATE)

        if language is None:
            # If no language is provided, use language identification
            identified_language = identify(audio_array)
            result = transcribe(audio_array, identified_language)
        else:
            result = transcribe(audio_array, language)

        processing_time = time.time() - start_time
        return JSONResponse(content={"transcription": result, "processing_time_seconds": processing_time})
    except Exception as e:
        logger.error(f"Error in transcribe_audio_file: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred during transcription", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.post("/synthesize")
async def synthesize_speech(request: TTSRequest, api_key: APIKey = Depends(get_api_key)):
    start_time = time.time()
    logger.info(f"Synthesize request received: text='{request.text}', language='{request.language}', speed={request.speed}")
    try:
        if request.language is None:
            # If no language is provided, default to English
            lang_code = "eng"
        else:
            # Extract the ISO code from the full language name
            lang_code = request.language.split()[0].strip()
        
        # Input validation
        if not request.text:
            raise ValueError("Text cannot be empty")
        if lang_code not in TTS_LANGUAGES:
            raise ValueError(f"Unsupported language: {lang_code}")
        if not 0.5 <= request.speed <= 2.0:
            raise ValueError(f"Speed must be between 0.5 and 2.0, got {request.speed}")
        
        logger.info(f"Calling synthesize function with lang_code: {lang_code}")
        result, filtered_text = synthesize(request.text, lang_code, request.speed)
        logger.info(f"Synthesize function completed. Filtered text: '{filtered_text}'")
        
        if result is None:
            logger.error("Synthesize function returned None")
            raise ValueError("Synthesis failed to produce audio")
        
        sample_rate, audio = result
        logger.info(f"Synthesis result: sample_rate={sample_rate}, audio_shape={audio.shape if isinstance(audio, np.ndarray) else 'not numpy array'}, audio_dtype={audio.dtype if isinstance(audio, np.ndarray) else type(audio)}")
        
        logger.info("Converting audio to numpy array")
        audio = np.array(audio, dtype=np.float32)
        logger.info(f"Converted audio shape: {audio.shape}, dtype: {audio.dtype}")
        
        logger.info("Normalizing audio")
        max_value = np.max(np.abs(audio))
        if max_value == 0:
            logger.warning("Audio array is all zeros")
            raise ValueError("Generated audio is silent (all zeros)")
        audio = audio / max_value
        logger.info(f"Normalized audio range: [{audio.min()}, {audio.max()}]")
        
        logger.info("Converting to int16")
        audio = (audio * 32767).astype(np.int16)
        logger.info(f"Int16 audio shape: {audio.shape}, dtype: {audio.dtype}")
        
        logger.info("Writing audio to buffer")
        buffer = io.BytesIO()
        sf.write(buffer, audio, sample_rate, format='wav')
        buffer.seek(0)
        logger.info(f"Buffer size: {buffer.getbuffer().nbytes} bytes")
        
        # Generate a unique filename
        filename = f"synthesized_audio_{int(time.time())}.wav"
        
        # Upload to S3 without ACL
        try:
            s3_client.upload_fileobj(
                buffer, 
                S3_BUCKET, 
                filename, 
                ExtraArgs={'ContentType': 'audio/wav'}
            )
            logger.info(f"File uploaded successfully to S3: {filename}")
            
            # Generate the public URL with the correct format
            url = f"https://s3.{S3_REGION}.amazonaws.com/{S3_BUCKET}/{filename}"
            logger.info(f"Public URL generated: {url}")
            
            processing_time = time.time() - start_time
            return JSONResponse(content={"audio_url": url, "processing_time_seconds": processing_time})
        
        except NoCredentialsError:
            logger.error("AWS credentials not available or invalid")
            raise HTTPException(status_code=500, detail="Could not upload file to S3: Missing or invalid credentials")
        except Exception as e:
            logger.error(f"Failed to upload to S3: {str(e)}")
            raise HTTPException(status_code=500, detail=f"Could not upload file to S3: {str(e)}")

    except ValueError as ve:
        logger.error(f"ValueError in synthesize_speech: {str(ve)}", exc_info=True)
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=400,
            content={"message": "Invalid input", "details": str(ve), "processing_time_seconds": processing_time}
        )
    except Exception as e:
        logger.error(f"Unexpected error in synthesize_speech: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "type": type(e).__name__,
            "traceback": traceback.format_exc()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An unexpected error occurred during speech synthesis", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.post("/identify")
async def identify_language(request: AudioRequest, api_key: APIKey = Depends(get_api_key)):
    start_time = time.time()
    try:
        input_bytes = base64.b64decode(request.audio)
        audio_array, sample_rate = extract_audio_from_file(input_bytes)
        result = identify(audio_array)
        processing_time = time.time() - start_time
        return JSONResponse(content={"language_identification": result, "processing_time_seconds": processing_time})
    except Exception as e:
        logger.error(f"Error in identify_language: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred during language identification", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.post("/identify_file")
async def identify_language_file(
    file: UploadFile = File(...),
    api_key: APIKey = Depends(get_api_key)
):
    start_time = time.time()
    try:
        contents = await file.read()
        audio_array, sample_rate = extract_audio_from_file(contents)
        result = identify(audio_array)
        processing_time = time.time() - start_time
        return JSONResponse(content={"language_identification": result, "processing_time_seconds": processing_time})
    except Exception as e:
        logger.error(f"Error in identify_language_file: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred during language identification", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.post("/asr_languages")
async def get_asr_languages(request: LanguageRequest, api_key: APIKey = Depends(get_api_key)):
    start_time = time.time()
    try:
        if request.language is None or request.language == "":
            # If no language is provided, return all languages
            matching_languages = ASR_LANGUAGES
        else:
            matching_languages = [lang for lang in ASR_LANGUAGES if lang.lower().startswith(request.language.lower())]
        
        processing_time = time.time() - start_time
        return JSONResponse(content={"languages": matching_languages, "processing_time_seconds": processing_time})
    except Exception as e:
        logger.error(f"Error in get_asr_languages: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred while fetching ASR languages", "details": error_details, "processing_time_seconds": processing_time}
        )

@app.post("/tts_languages")
async def get_tts_languages(request: LanguageRequest, api_key: APIKey = Depends(get_api_key)):
    start_time = time.time()
    try:
        if request.language is None or request.language == "":
            # If no language is provided, return all languages
            matching_languages = TTS_LANGUAGES
        else:
            matching_languages = [lang for lang in TTS_LANGUAGES if lang.lower().startswith(request.language.lower())]
        
        processing_time = time.time() - start_time
        return JSONResponse(content={"languages": matching_languages, "processing_time_seconds": processing_time})
    except Exception as e:
        logger.error(f"Error in get_tts_languages: {str(e)}", exc_info=True)
        error_details = {
            "error": str(e),
            "traceback": traceback.format_exc()
        }
        processing_time = time.time() - start_time
        return JSONResponse(
            status_code=500,
            content={"message": "An error occurred while fetching TTS languages", "details": error_details, "processing_time_seconds": processing_time}
        )

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

@app.get("/")
async def root():
    return {
        "message": "Welcome to the MMS Speech Technology API",
        "version": "1.0",
        "endpoints": [
            "/transcribe",
            "/transcribe_file",
            "/synthesize",
            "/identify",
            "/identify_file",
            "/asr_languages",
            "/tts_languages",
            "/health"
        ]
    }

	import os
	from fastapi import FastAPI, HTTPException, File, UploadFile, Depends, Security, Form
	from fastapi.security.api_key import APIKeyHeader, APIKey
	from fastapi.responses import JSONResponse
	from pydantic import BaseModel
	from typing import Optional
	import numpy as np
	import io
	import soundfile as sf
	import base64
	import logging
	import torch
	import librosa
	from pathlib import Path
	from pydub import AudioSegment
	from moviepy.editor import VideoFileClip
	import traceback
	from logging.handlers import RotatingFileHandler
	import boto3
	from botocore.exceptions import NoCredentialsError
	import time
	import tempfile
	import magic

	# Import functions from other modules
	from asr import transcribe, ASR_LANGUAGES, ASR_SAMPLING_RATE
	from tts import synthesize, TTS_LANGUAGES
	from lid import identify

	# Configure logging
	logging.basicConfig(level=logging.INFO)
	logger = logging.getLogger(__name__)

	# Add a file handler
	file_handler = RotatingFileHandler('app.log', maxBytes=10000000, backupCount=5)
	file_handler.setLevel(logging.INFO)
	formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
	file_handler.setFormatter(formatter)
	logger.addHandler(file_handler)

	app = FastAPI(title="MMS: Scaling Speech Technology to 1000+ languages")

	# S3 Configuration
	S3_BUCKET = os.environ.get("S3_BUCKET")
	S3_REGION = os.environ.get("S3_REGION")
	S3_ACCESS_KEY_ID = os.environ.get("AWS_ACCESS_KEY_ID")
	S3_SECRET_ACCESS_KEY = os.environ.get("AWS_SECRET_ACCESS_KEY")

	# API Key Configuration
	API_KEY = os.environ.get("API_KEY")
	api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)

	# Initialize S3 client
	s3_client = boto3.client(
	's3',
	aws_access_key_id=S3_ACCESS_KEY_ID,
	aws_secret_access_key=S3_SECRET_ACCESS_KEY,
	region_name=S3_REGION
	)

	# Define request models
	class AudioRequest(BaseModel):
	audio: str # Base64 encoded audio or video data
	language: Optional[str] = None

	class TTSRequest(BaseModel):
	text: str
	language: Optional[str] = None
	speed: float = 1.0

	class LanguageRequest(BaseModel):
	language: Optional[str] = None

	async def get_api_key(api_key_header: str = Security(api_key_header)):
	if api_key_header == API_KEY:
	return api_key_header
	raise HTTPException(status_code=403, detail="Could not validate credentials")

	def extract_audio_from_file(input_bytes):
	with tempfile.NamedTemporaryFile(delete=False, suffix='.tmp') as temp_file:
	temp_file.write(input_bytes)
	temp_file_path = temp_file.name

	try:
	# Log file info
	file_info = magic.from_file(temp_file_path, mime=True)
	logger.info(f"Received file of type: {file_info}")

	# Try reading with soundfile first
	try:
	audio_array, sample_rate = sf.read(temp_file_path)
	logger.info(f"Successfully read audio with soundfile. Shape: {audio_array.shape}, Sample rate: {sample_rate}")
	return audio_array, sample_rate
	except Exception as e:
	logger.info(f"Could not read with soundfile: {str(e)}")

	# Try reading as video
	try:
	video = VideoFileClip(temp_file_path)
	audio = video.audio
	if audio is not None:
	audio_array = audio.to_soundarray()
	sample_rate = audio.fps
	audio_array = audio_array.mean(axis=1) if len(audio_array.shape) > 1 and audio_array.shape[1] > 1 else audio_array
	audio_array = audio_array.astype(np.float32)
	audio_array /= np.max(np.abs(audio_array))
	video.close()
	logger.info(f"Successfully extracted audio from video. Shape: {audio_array.shape}, Sample rate: {sample_rate}")
	return audio_array, sample_rate
	else:
	logger.info("Video file contains no audio")
	except Exception as e:
	logger.info(f"Could not read as video: {str(e)}")

	# Try reading with pydub
	try:
	audio = AudioSegment.from_file(temp_file_path)
	audio_array = np.array(audio.get_array_of_samples())
	audio_array = audio_array.astype(np.float32) / (215 if audio.sample_width == 2 else 27)
	audio_array = audio_array.reshape((-1, 2)).mean(axis=1) if audio.channels == 2 else audio_array
	logger.info(f"Successfully read audio with pydub. Shape: {audio_array.shape}, Sample rate: {audio.frame_rate}")
	return audio_array, audio.frame_rate
	except Exception as e:
	logger.info(f"Could not read with pydub: {str(e)}")

	raise ValueError(f"Unsupported file format: {file_info}")
	finally:
	os.unlink(temp_file_path)

	@app.post("/transcribe")
	async def transcribe_audio(request: AudioRequest, api_key: APIKey = Depends(get_api_key)):
	start_time = time.time()
	try:
	input_bytes = base64.b64decode(request.audio)
	audio_array, sample_rate = extract_audio_from_file(input_bytes)

	# Ensure audio_array is float32
	audio_array = audio_array.astype(np.float32)

	# Resample if necessary
	if sample_rate != ASR_SAMPLING_RATE:
	audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=ASR_SAMPLING_RATE)

	if request.language is None:
	# If no language is provided, use language identification
	identified_language = identify(audio_array)
	result = transcribe(audio_array, identified_language)
	else:
	result = transcribe(audio_array, request.language)

	processing_time = time.time() - start_time
	return JSONResponse(content={"transcription": result, "processing_time_seconds": processing_time})
	except Exception as e:
	logger.error(f"Error in transcribe_audio: {str(e)}", exc_info=True)
	error_details = {
	"error": str(e),
	"traceback": traceback.format_exc()
	}
	processing_time = time.time() - start_time
	return JSONResponse(
	status_code=500,
	content={"message": "An error occurred during transcription", "details": error_details, "processing_time_seconds": processing_time}
	)

	@app.post("/transcribe_file")
	async def transcribe_audio_file(
	file: UploadFile = File(...),
	language: Optional[str] = Form(None),
	api_key: APIKey = Depends(get_api_key)
	):
	start_time = time.time()
	try:
	contents = await file.read()
	audio_array, sample_rate = extract_audio_from_file(contents)

	# Ensure audio_array is float32
	audio_array = audio_array.astype(np.float32)

	# Resample if necessary
	if sample_rate != ASR_SAMPLING_RATE:
	audio_array = librosa.resample(audio_array, orig_sr=sample_rate, target_sr=ASR_SAMPLING_RATE)

	if language is None:
	# If no language is provided, use language identification
	identified_language = identify(audio_array)
	result = transcribe(audio_array, identified_language)
	else:
	result = transcribe(audio_array, language)

	processing_time = time.time() - start_time
	return JSONResponse(content={"transcription": result, "processing_time_seconds": processing_time})
	except Exception as e:
	logger.error(f"Error in transcribe_audio_file: {str(e)}", exc_info=True)
	error_details = {
	"error": str(e),
	"traceback": traceback.format_exc()
	}
	processing_time = time.time() - start_time
	return JSONResponse(
	status_code=500,
	content={"message": "An error occurred during transcription", "details": error_details, "processing_time_seconds": processing_time}
	)

	@app.post("/synthesize")
	async def synthesize_speech(request: TTSRequest, api_key: APIKey = Depends(get_api_key)):
	start_time = time.time()
	logger.info(f"Synthesize request received: text='{request.text}', language='{request.language}', speed={request.speed}")
	try:
	if request.language is None:
	# If no language is provided, default to English
	lang_code = "eng"
	else:
	# Extract the ISO code from the full language name
	lang_code = request.language.split()[0].strip()

	# Input validation
	if not request.text:
	raise ValueError("Text cannot be empty")
	if lang_code not in TTS_LANGUAGES:
	raise ValueError(f"Unsupported language: {lang_code}")
	if not 0.5 <= request.speed <= 2.0:
	raise ValueError(f"Speed must be between 0.5 and 2.0, got {request.speed}")

	logger.info(f"Calling synthesize function with lang_code: {lang_code}")
	result, filtered_text = synthesize(request.text, lang_code, request.speed)
	logger.info(f"Synthesize function completed. Filtered text: '{filtered_text}'")

	if result is None:
	logger.error("Synthesize function returned None")
	raise ValueError("Synthesis failed to produce audio")

	sample_rate, audio = result
	logger.info(f"Synthesis result: sample_rate={sample_rate}, audio_shape={audio.shape if isinstance(audio, np.ndarray) else 'not numpy array'}, audio_dtype={audio.dtype if isinstance(audio, np.ndarray) else type(audio)}")

	logger.info("Converting audio to numpy array")
	audio = np.array(audio, dtype=np.float32)
	logger.info(f"Converted audio shape: {audio.shape}, dtype: {audio.dtype}")

	logger.info("Normalizing audio")
	max_value = np.max(np.abs(audio))
	if max_value == 0:
	logger.warning("Audio array is all zeros")
	raise ValueError("Generated audio is silent (all zeros)")
	audio = audio / max_value
	logger.info(f"Normalized audio range: [{audio.min()}, {audio.max()}]")

	logger.info("Converting to int16")
	audio = (audio * 32767).astype(np.int16)
	logger.info(f"Int16 audio shape: {audio.shape}, dtype: {audio.dtype}")

	logger.info("Writing audio to buffer")
	buffer = io.BytesIO()
	sf.write(buffer, audio, sample_rate, format='wav')
	buffer.seek(0)
	logger.info(f"Buffer size: {buffer.getbuffer().nbytes} bytes")

	# Generate a unique filename
	filename = f"synthesized_audio_{int(time.time())}.wav"

	# Upload to S3 without ACL
	try:
	s3_client.upload_fileobj(
	buffer,
	S3_BUCKET,
	filename,
	ExtraArgs={'ContentType': 'audio/wav'}
	)
	logger.info(f"File uploaded successfully to S3: {filename}")

	# Generate the public URL with the correct format
	url = f"https://s3.{S3_REGION}.amazonaws.com/{S3_BUCKET}/{filename}"
	logger.info(f"Public URL generated: {url}")

	processing_time = time.time() - start_time
	return JSONResponse(content={"audio_url": url, "processing_time_seconds": processing_time})

	except NoCredentialsError:
	logger.error("AWS credentials not available or invalid")
	raise HTTPException(status_code=500, detail="Could not upload file to S3: Missing or invalid credentials")
	except Exception as e:
	logger.error(f"Failed to upload to S3: {str(e)}")
	raise HTTPException(status_code=500, detail=f"Could not upload file to S3: {str(e)}")

	except ValueError as ve:
	logger.error(f"ValueError in synthesize_speech: {str(ve)}", exc_info=True)
	processing_time = time.time() - start_time
	return JSONResponse(
	status_code=400,
	content={"message": "Invalid input", "details": str(ve), "processing_time_seconds": processing_time}
	)
	except Exception as e:
	logger.error(f"Unexpected error in synthesize_speech: {str(e)}", exc_info=True)
	error_details = {
	"error": str(e),
	"type": type(e).__name__,
	"traceback": traceback.format_exc()
	}
	processing_time = time.time() - start_time
	return JSONResponse(
	status_code=500,
	content={"message": "An unexpected error occurred during speech synthesis", "details": error_details, "processing_time_seconds": processing_time}
	)

	@app.post("/identify")
	async def identify_language(request: AudioRequest, api_key: APIKey = Depends(get_api_key)):
	start_time = time.time()
	try:
	input_bytes = base64.b64decode(request.audio)
	audio_array, sample_rate = extract_audio_from_file(input_bytes)
	result = identify(audio_array)
	processing_time = time.time() - start_time
	return JSONResponse(content={"language_identification": result, "processing_time_seconds": processing_time})
	except Exception as e:
	logger.error(f"Error in identify_language: {str(e)}", exc_info=True)
	error_details = {
	"error": str(e),
	"traceback": traceback.format_exc()
	}
	processing_time = time.time() - start_time
	return JSONResponse(
	status_code=500,
	content={"message": "An error occurred during language identification", "details": error_details, "processing_time_seconds": processing_time}
	)

	@app.post("/identify_file")
	async def identify_language_file(
	file: UploadFile = File(...),
	api_key: APIKey = Depends(get_api_key)
	):
	start_time = time.time()
	try:
	contents = await file.read()
	audio_array, sample_rate = extract_audio_from_file(contents)
	result = identify(audio_array)
	processing_time = time.time() - start_time
	return JSONResponse(content={"language_identification": result, "processing_time_seconds": processing_time})
	except Exception as e:
	logger.error(f"Error in identify_language_file: {str(e)}", exc_info=True)
	error_details = {
	"error": str(e),
	"traceback": traceback.format_exc()
	}
	processing_time = time.time() - start_time
	return JSONResponse(
	status_code=500,
	content={"message": "An error occurred during language identification", "details": error_details, "processing_time_seconds": processing_time}
	)

	@app.post("/asr_languages")
	async def get_asr_languages(request: LanguageRequest, api_key: APIKey = Depends(get_api_key)):
	start_time = time.time()
	try:
	if request.language is None or request.language == "":
	# If no language is provided, return all languages
	matching_languages = ASR_LANGUAGES
	else:
	matching_languages = [lang for lang in ASR_LANGUAGES if lang.lower().startswith(request.language.lower())]

	processing_time = time.time() - start_time
	return JSONResponse(content={"languages": matching_languages, "processing_time_seconds": processing_time})
	except Exception as e:
	logger.error(f"Error in get_asr_languages: {str(e)}", exc_info=True)
	error_details = {
	"error": str(e),
	"traceback": traceback.format_exc()
	}
	processing_time = time.time() - start_time
	return JSONResponse(
	status_code=500,
	content={"message": "An error occurred while fetching ASR languages", "details": error_details, "processing_time_seconds": processing_time}
	)

	@app.post("/tts_languages")
	async def get_tts_languages(request: LanguageRequest, api_key: APIKey = Depends(get_api_key)):
	start_time = time.time()
	try:
	if request.language is None or request.language == "":
	# If no language is provided, return all languages
	matching_languages = TTS_LANGUAGES
	else:
	matching_languages = [lang for lang in TTS_LANGUAGES if lang.lower().startswith(request.language.lower())]

	processing_time = time.time() - start_time
	return JSONResponse(content={"languages": matching_languages, "processing_time_seconds": processing_time})
	except Exception as e:
	logger.error(f"Error in get_tts_languages: {str(e)}", exc_info=True)
	error_details = {
	"error": str(e),
	"traceback": traceback.format_exc()
	}
	processing_time = time.time() - start_time
	return JSONResponse(
	status_code=500,
	content={"message": "An error occurred while fetching TTS languages", "details": error_details, "processing_time_seconds": processing_time}
	)

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

	@app.get("/")
	async def root():
	return {
	"message": "Welcome to the MMS Speech Technology API",
	"version": "1.0",
	"endpoints": [
	"/transcribe",
	"/transcribe_file",
	"/synthesize",
	"/identify",
	"/identify_file",
	"/asr_languages",
	"/tts_languages",
	"/health"
	]
	}