# OpenAI-compatible Text-to-Speech API
# POST /v1/audio/speech
# Required params: model, catelogid, prompt (text to synthesize)
# Optional params: speaker (voice_id), speed, emotion
#
# Example request:
# {
#     "model": "speech-2.6-turbo",
#     "catelogid": "tts",
#     "prompt": "你好，欢迎使用语音合成服务",
#     "speaker": "female-tianmei",
#     "speed": 1.0,
#     "emotion": "happy"
# }
#
# Response (stream, hex audio chunks):
# {
#     "status": "SUCCEEDED",
#     "audio": "base64_encoded_audio_data"
# }

userid = await get_user()
userorgid = await get_userorgid()
if userid is None:
    debug('need login')
    return openai_403()

# Validate required parameters
if not params_kw.model:
    d = return_error('Missing required parameter: model')
    return json_response(d, status=400)

if not params_kw.catelogid:
    d = return_error('Missing required parameter: catelogid')
    return json_response(d, status=400)

if not params_kw.prompt:
    d = return_error('Missing required parameter: prompt (text to synthesize)')
    return json_response(d, status=400)

lctype = params_kw.catelogid

env = request._run_ns
async with get_sor_context(env, 'llmage') as sor:
    # Look up llm by model name and catalog type through llm_api_map
    sql = """select distinct a.* from llm a
join llm_api_map m on a.id = m.llmid
join llmcatelog b on m.llmcatelogid = b.id
where (b.id = ${lctype}$ OR b.name = ${lctype}$)
    and a.model=${model}$
    and a.status = 'published'"""
    recs = await sor.sqlExe(sql, {
        'lctype': lctype,
        'model': params_kw.model
    })
    if len(recs) == 0:
        debug(f'{params_kw.model=} not found for catalog {lctype}')
        return openai_400()
    params_kw.llmid = recs[0].id

debug(f'{params_kw.llmid=}')

# Check balance
f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
if not f:
    debug(f'{userid=} balance not enough')
    return openai_429()

# Generate task ID and attach to params
if not params_kw.transno:
    params_kw.transno = getID()

# Call inference (TTS can be stream or sync depending on model)
return await inference(request, env=env)