89 lines
2.4 KiB
Plaintext

# OpenAI-compatible Video Generation API
# POST /v1/video/generations
# Required params: model, llmcatelogid
# Optional params: prompt, image_url, duration, resolution, n, etc.
#
# Example request:
# {
# "model": "keling-2.1",
# "llmcatelogid": "文生视频",
# "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.llmcatelogid:
d = return_error('Missing required parameter: llmcatelogid')
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.llmcatelogid
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.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)