# OpenAI-compatible Video Generation API
# POST /v1/video/generations
# Required params: model, catelogid
# Optional params: prompt, image_url, duration, resolution, n, etc.
#
# Example request:
# {
#     "model": "keling-2.1",
#     "catelogid": "t2v",
#     "prompt": "A beautiful sunset over the ocean",
#     "duration": "5s",
#     "resolution": "1080p"
# }
#
# Response (async task):
# {
#     "id": "vid_xxx",
#     "object": "video.generation",
#     "model": "keling-2.1",
#     "status": "submitted",
#     "taskid": "task_xxx",
#     "created": 1234567890
# }

import json
import time
from functools import partial
from appPublic.log import debug
from appPublic.dictObject import DictObject
from appPublic.uniqueID import getID
from appPublic.timeUtils import curDateString, timestampstr
from sqlor.dbpools import get_sor_context

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)

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')
    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 (video/image generation is typically async via callback)
return await inference(request, env=env)