# OpenAI-compatible Audio Transcription API (ASR)
# POST /v1/audio/transcriptions
# Required params: model, catelogid, audio_file (audio URL or base64)
# Optional params: language
#
# Example request:
# {
#     "model": "qwen3-asr-flash",
#     "catelogid": "asr",
#     "audio_file": "https://example.com/audio.wav"
# }
#
# Response:
# {
#     "text": "识别出的文本内容",
#     "usage": { "duration_seconds": 5.2 }
# }

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.audio_file:
    d = return_error('Missing required parameter: audio_file')
    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 (ASR is synchronous)
return await inference(request, env=env)