# KTV Pipeline / GPU Service Inference API
# POST /v1/pipeline/submit
# catelogid 固定为 ktv_pipeline，无需传 catelogid
#
# Required params:
#   model: string - 模型名称，如 "ky-asr-transcribe"
#
# 各服务特定参数见 API 文档
#
# 同步服务直接返回结果：
# {
#   "taskid": "luid_xxx",
#   "taskstatus": "SUCCEEDED",
#   ...服务特定结果字段...,
#   "usage": {...}
# }
#
# 异步服务返回任务信息：
# {
#   "taskid": "luid_xxx",
#   "taskstatus": "PENDING"
# }
# 异步结果通过 /v1/tasks?taskid=xxx 查询

debug_params('params_kw', params_kw)

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)

catelogid = 'ktv_pipeline'
params_kw.catelogid = catelogid

env = request._run_ns
async with get_sor_context(env, 'llmage') as sor:
	# Look up llm by model name through llm_api_map (fixed catelogid=ktv_pipeline)
	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 = ${catelogid}$ OR b.name = ${catelogid}$)
	and a.name=${model}$
	and a.status = 'published'"""
	recs = await sor.sqlExe(sql, {
		'catelogid': catelogid,
		'model': params_kw.model
	})
	if len(recs) == 0:
		debug(f'{params_kw.model=} not found under ktv_pipeline catalog')
		d = return_error(f'Model "{params_kw.model}" not found or not available')
		return json_response(d, status=400)
	params_kw.llmid = recs[0].id
	params_kw.llmcatelogid = catelogid

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
if not params_kw.transno:
	params_kw.transno = getID()

# Call inference
return await inference(request, env=env)