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import asyncio
import base64
import json
import time
from typing import Optional, AsyncGenerator, List, Dict, Any
from ..core.logger import debug_logger
from ..core.config import config
from ..core.models import Task, RequestLog
from .file_cache import FileCache
# Model configuration
MODEL_CONFIG = {
# 图片生成 - GEM_PIX (Gemini 2.5 Flash)
"gemini-2.5-flash-image-landscape": {
"type": "image",
"model_name": "GEM_PIX",
"aspect_ratio": "IMAGE_ASPECT_RATIO_LANDSCAPE"
},
"gemini-2.5-flash-image-portrait": {
"type": "image",
"model_name": "GEM_PIX",
"aspect_ratio": "IMAGE_ASPECT_RATIO_PORTRAIT"
},
# 图片生成 - GEM_PIX_2 (Gemini 3.0 Pro)
"gemini-3.0-pro-image-landscape": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_LANDSCAPE"
},
"gemini-3.0-pro-image-portrait": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_PORTRAIT"
},
"gemini-3.0-pro-image-square": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_SQUARE"
},
"gemini-3.0-pro-image-four-three": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_LANDSCAPE_FOUR_THREE"
},
"gemini-3.0-pro-image-three-four": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_PORTRAIT_THREE_FOUR"
},
# 图片生成 - GEM_PIX_2 (Gemini 3.0 Pro) 2K 放大版
"gemini-3.0-pro-image-landscape-2k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_LANDSCAPE",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_2K"
},
"gemini-3.0-pro-image-portrait-2k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_PORTRAIT",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_2K"
},
"gemini-3.0-pro-image-square-2k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_SQUARE",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_2K"
},
"gemini-3.0-pro-image-four-three-2k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_LANDSCAPE_FOUR_THREE",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_2K"
},
"gemini-3.0-pro-image-three-four-2k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_PORTRAIT_THREE_FOUR",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_2K"
},
# 图片生成 - GEM_PIX_2 (Gemini 3.0 Pro) 4K 放大版
"gemini-3.0-pro-image-landscape-4k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_LANDSCAPE",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_4K"
},
"gemini-3.0-pro-image-portrait-4k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_PORTRAIT",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_4K"
},
"gemini-3.0-pro-image-square-4k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_SQUARE",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_4K"
},
"gemini-3.0-pro-image-four-three-4k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_LANDSCAPE_FOUR_THREE",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_4K"
},
"gemini-3.0-pro-image-three-four-4k": {
"type": "image",
"model_name": "GEM_PIX_2",
"aspect_ratio": "IMAGE_ASPECT_RATIO_PORTRAIT_THREE_FOUR",
"upsample": "UPSAMPLE_IMAGE_RESOLUTION_4K"
},
# 图片生成 - IMAGEN_3_5 (Imagen 4.0)
"imagen-4.0-generate-preview-landscape": {
"type": "image",
"model_name": "IMAGEN_3_5",
"aspect_ratio": "IMAGE_ASPECT_RATIO_LANDSCAPE"
},
"imagen-4.0-generate-preview-portrait": {
"type": "image",
"model_name": "IMAGEN_3_5",
"aspect_ratio": "IMAGE_ASPECT_RATIO_PORTRAIT"
},
# ========== 文生视频 (T2V - Text to Video) ==========
# 不支持上传图片，只使用文本提示词生成
# veo_3_1_t2v_fast_portrait (竖屏)
# 上游模型名: veo_3_1_t2v_fast_portrait
"veo_3_1_t2v_fast_portrait": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_portrait",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False
},
# veo_3_1_t2v_fast_landscape (横屏)
# 上游模型名: veo_3_1_t2v_fast
"veo_3_1_t2v_fast_landscape": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False
},
# veo_2_1_fast_d_15_t2v (需要新增横竖屏)
"veo_2_1_fast_d_15_t2v_portrait": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_2_1_fast_d_15_t2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False
},
"veo_2_1_fast_d_15_t2v_landscape": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_2_1_fast_d_15_t2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False
},
# veo_2_0_t2v (需要新增横竖屏)
"veo_2_0_t2v_portrait": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_2_0_t2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False
},
"veo_2_0_t2v_landscape": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_2_0_t2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False
},
# veo_3_1_t2v_fast_ultra (横竖屏)
"veo_3_1_t2v_fast_portrait_ultra": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_portrait_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False
},
"veo_3_1_t2v_fast_ultra": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False
},
# veo_3_1_t2v_fast_ultra_relaxed (横竖屏)
"veo_3_1_t2v_fast_portrait_ultra_relaxed": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_portrait_ultra_relaxed",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False
},
"veo_3_1_t2v_fast_ultra_relaxed": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_ultra_relaxed",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False
},
# veo_3_1_t2v (横竖屏)
"veo_3_1_t2v_portrait": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_portrait",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False
},
"veo_3_1_t2v_landscape": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False
},
# ========== 首尾帧模型 (I2V - Image to Video) ==========
# 支持1-2张图片：1张作为首帧，2张作为首尾帧
# veo_3_1_i2v_s_fast_fl (需要新增横竖屏)
"veo_3_1_i2v_s_fast_portrait_fl": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_portrait_fl",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
"veo_3_1_i2v_s_fast_fl": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_fl",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
# veo_2_1_fast_d_15_i2v (需要新增横竖屏)
"veo_2_1_fast_d_15_i2v_portrait": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_2_1_fast_d_15_i2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
"veo_2_1_fast_d_15_i2v_landscape": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_2_1_fast_d_15_i2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
# veo_2_0_i2v (需要新增横竖屏)
"veo_2_0_i2v_portrait": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_2_0_i2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
"veo_2_0_i2v_landscape": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_2_0_i2v",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
# veo_3_1_i2v_s_fast_ultra (横竖屏)
"veo_3_1_i2v_s_fast_portrait_ultra_fl": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_portrait_ultra_fl",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
"veo_3_1_i2v_s_fast_ultra_fl": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_ultra_fl",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
# veo_3_1_i2v_s_fast_ultra_relaxed (需要新增横竖屏)
"veo_3_1_i2v_s_fast_portrait_ultra_relaxed": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_portrait_ultra_relaxed",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
"veo_3_1_i2v_s_fast_ultra_relaxed": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_ultra_relaxed",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
# veo_3_1_i2v_s (需要新增横竖屏)
"veo_3_1_i2v_s_portrait": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
"veo_3_1_i2v_s_landscape": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 1,
"max_images": 2
},
# ========== 多图生成 (R2V - Reference Images to Video) ==========
# 支持多张图片,不限制数量
# veo_3_1_r2v_fast (横竖屏)
"veo_3_1_r2v_fast_portrait": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_portrait",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 0,
"max_images": None # 不限制
},
"veo_3_1_r2v_fast": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 0,
"max_images": None # 不限制
},
# veo_3_1_r2v_fast_ultra (横竖屏)
"veo_3_1_r2v_fast_portrait_ultra": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_portrait_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 0,
"max_images": None # 不限制
},
"veo_3_1_r2v_fast_ultra": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 0,
"max_images": None # 不限制
},
# veo_3_1_r2v_fast_ultra_relaxed (横竖屏)
"veo_3_1_r2v_fast_portrait_ultra_relaxed": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_portrait_ultra_relaxed",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 0,
"max_images": None # 不限制
},
"veo_3_1_r2v_fast_ultra_relaxed": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_ultra_relaxed",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 0,
"max_images": None # 不限制
},
# ========== 视频放大 (Video Upsampler) ==========
# 仅 3.1 支持，需要先生成视频后再放大，可能需要 30 分钟
# T2V 4K 放大版
"veo_3_1_t2v_fast_portrait_4k": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_portrait",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False,
"upsample": {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
},
"veo_3_1_t2v_fast_4k": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False,
"upsample": {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
},
"veo_3_1_t2v_fast_portrait_ultra_4k": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_portrait_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False,
"upsample": {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
},
"veo_3_1_t2v_fast_ultra_4k": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False,
"upsample": {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
},
# T2V 1080P 放大版
"veo_3_1_t2v_fast_portrait_1080p": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_portrait",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False,
"upsample": {"resolution": "VIDEO_RESOLUTION_1080P", "model_key": "veo_3_1_upsampler_1080p"}
},
"veo_3_1_t2v_fast_1080p": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False,
"upsample": {"resolution": "VIDEO_RESOLUTION_1080P", "model_key": "veo_3_1_upsampler_1080p"}
},
"veo_3_1_t2v_fast_portrait_ultra_1080p": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_portrait_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": False,
"upsample": {"resolution": "VIDEO_RESOLUTION_1080P", "model_key": "veo_3_1_upsampler_1080p"}
},
"veo_3_1_t2v_fast_ultra_1080p": {
"type": "video",
"video_type": "t2v",
"model_key": "veo_3_1_t2v_fast_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": False,
"upsample": {"resolution": "VIDEO_RESOLUTION_1080P", "model_key": "veo_3_1_upsampler_1080p"}
},
# I2V 4K 放大版
"veo_3_1_i2v_s_fast_portrait_ultra_fl_4k": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_portrait_ultra_fl",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 1,
"max_images": 2,
"upsample": {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
},
"veo_3_1_i2v_s_fast_ultra_fl_4k": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_ultra_fl",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 1,
"max_images": 2,
"upsample": {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
},
# I2V 1080P 放大版
"veo_3_1_i2v_s_fast_portrait_ultra_fl_1080p": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_portrait_ultra_fl",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 1,
"max_images": 2,
"upsample": {"resolution": "VIDEO_RESOLUTION_1080P", "model_key": "veo_3_1_upsampler_1080p"}
},
"veo_3_1_i2v_s_fast_ultra_fl_1080p": {
"type": "video",
"video_type": "i2v",
"model_key": "veo_3_1_i2v_s_fast_ultra_fl",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 1,
"max_images": 2,
"upsample": {"resolution": "VIDEO_RESOLUTION_1080P", "model_key": "veo_3_1_upsampler_1080p"}
},
# R2V 4K 放大版
"veo_3_1_r2v_fast_portrait_ultra_4k": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_portrait_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 0,
"max_images": None,
"upsample": {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
},
"veo_3_1_r2v_fast_ultra_4k": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 0,
"max_images": None,
"upsample": {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
},
# R2V 1080P 放大版
"veo_3_1_r2v_fast_portrait_ultra_1080p": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_portrait_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_PORTRAIT",
"supports_images": True,
"min_images": 0,
"max_images": None,
"upsample": {"resolution": "VIDEO_RESOLUTION_1080P", "model_key": "veo_3_1_upsampler_1080p"}
},
"veo_3_1_r2v_fast_ultra_1080p": {
"type": "video",
"video_type": "r2v",
"model_key": "veo_3_1_r2v_fast_ultra",
"aspect_ratio": "VIDEO_ASPECT_RATIO_LANDSCAPE",
"supports_images": True,
"min_images": 0,
"max_images": None,
"upsample": {"resolution": "VIDEO_RESOLUTION_1080P", "model_key": "veo_3_1_upsampler_1080p"}
}
}
class GenerationHandler:
"""统一生成处理器"""
def __init__(self, flow_client, token_manager, load_balancer, db, concurrency_manager, proxy_manager):
self.flow_client = flow_client
self.token_manager = token_manager
self.load_balancer = load_balancer
self.db = db
self.concurrency_manager = concurrency_manager
self.file_cache = FileCache(
cache_dir="tmp",
default_timeout=config.cache_timeout,
proxy_manager=proxy_manager
)
async def check_token_availability(self, is_image: bool, is_video: bool) -> bool:
"""检查Token可用性
Args:
is_image: 是否检查图片生成Token
is_video: 是否检查视频生成Token
Returns:
True表示有可用Token, False表示无可用Token
"""
token_obj = await self.load_balancer.select_token(
for_image_generation=is_image,
for_video_generation=is_video
)
return token_obj is not None
async def handle_generation(
self,
model: str,
prompt: str,
images: Optional[List[bytes]] = None,
stream: bool = False
) -> AsyncGenerator:
"""统一生成入口
Args:
model: 模型名称
prompt: 提示词
images: 图片列表 (bytes格式)
stream: 是否流式输出
"""
start_time = time.time()
token = None
# 1. 验证模型
if model not in MODEL_CONFIG:
error_msg = f"不支持的模型: {model}"
debug_logger.log_error(error_msg)
yield self._create_error_response(error_msg)
return
model_config = MODEL_CONFIG[model]
generation_type = model_config["type"]
debug_logger.log_info(f"[GENERATION] 开始生成 - 模型: {model}, 类型: {generation_type}, Prompt: {prompt[:50]}...")
# 非流式模式: 只检查可用性
if not stream:
is_image = (generation_type == "image")
is_video = (generation_type == "video")
available = await self.check_token_availability(is_image, is_video)
if available:
if is_image:
message = "所有Token可用于图片生成。请启用流式模式使用生成功能。"
else:
message = "所有Token可用于视频生成。请启用流式模式使用生成功能。"
else:
if is_image:
message = "没有可用的Token进行图片生成"
else:
message = "没有可用的Token进行视频生成"
yield self._create_completion_response(message, is_availability_check=True)
return
# 向用户展示开始信息
if stream:
yield self._create_stream_chunk(
f"✨ {'视频' if generation_type == 'video' else '图片'}生成任务已启动\n",
role="assistant"
)
# 2. 选择Token
debug_logger.log_info(f"[GENERATION] 正在选择可用Token...")
if generation_type == "image":
token = await self.load_balancer.select_token(for_image_generation=True, model=model)
else:
token = await self.load_balancer.select_token(for_video_generation=True, model=model)
if not token:
error_msg = self._get_no_token_error_message(generation_type)
debug_logger.log_error(f"[GENERATION] {error_msg}")
if stream:
yield self._create_stream_chunk(f"❌ {error_msg}\n")
yield self._create_error_response(error_msg)
return
debug_logger.log_info(f"[GENERATION] 已选择Token: {token.id} ({token.email})")
try:
# 3. 确保AT有效
debug_logger.log_info(f"[GENERATION] 检查Token AT有效性...")
if stream:
yield self._create_stream_chunk("初始化生成环境...\n")
if not await self.token_manager.is_at_valid(token.id):
error_msg = "Token AT无效或刷新失败"
debug_logger.log_error(f"[GENERATION] {error_msg}")
if stream:
yield self._create_stream_chunk(f"❌ {error_msg}\n")
yield self._create_error_response(error_msg)
return
# 重新获取token (AT可能已刷新)
token = await self.token_manager.get_token(token.id)
# 4. 确保Project存在
debug_logger.log_info(f"[GENERATION] 检查/创建Project...")
project_id = await self.token_manager.ensure_project_exists(token.id)
debug_logger.log_info(f"[GENERATION] Project ID: {project_id}")
# 5. 根据类型处理
if generation_type == "image":
debug_logger.log_info(f"[GENERATION] 开始图片生成流程...")
async for chunk in self._handle_image_generation(
token, project_id, model_config, prompt, images, stream
):
yield chunk
else: # video
debug_logger.log_info(f"[GENERATION] 开始视频生成流程...")
async for chunk in self._handle_video_generation(
token, project_id, model_config, prompt, images, stream
):
yield chunk
# 6. 记录使用
is_video = (generation_type == "video")
await self.token_manager.record_usage(token.id, is_video=is_video)
# 重置错误计数 (请求成功时清空连续错误计数)
await self.token_manager.record_success(token.id)
debug_logger.log_info(f"[GENERATION] ✅ 生成成功完成")
# 7. 记录成功日志
duration = time.time() - start_time
# 构建响应数据，包含生成的URL
response_data = {
"status": "success",
"model": model,
"prompt": prompt[:100]
}
# 添加生成的URL（如果有）
if hasattr(self, '_last_generated_url') and self._last_generated_url:
response_data["url"] = self._last_generated_url
# 清除临时存储
self._last_generated_url = None
await self._log_request(
token.id,
f"generate_{generation_type}",
{"model": model, "prompt": prompt[:100], "has_images": images is not None and len(images) > 0},
response_data,
200,
duration
)
except Exception as e:
error_msg = f"生成失败: {str(e)}"
debug_logger.log_error(f"[GENERATION] ❌ {error_msg}")
if stream:
yield self._create_stream_chunk(f"❌ {error_msg}\n")
if token:
# 记录错误（所有错误统一处理，不再特殊处理429）
await self.token_manager.record_error(token.id)
yield self._create_error_response(error_msg)
# 记录失败日志
duration = time.time() - start_time
await self._log_request(
token.id if token else None,
f"generate_{generation_type if model_config else 'unknown'}",
{"model": model, "prompt": prompt[:100], "has_images": images is not None and len(images) > 0},
{"error": error_msg},
500,
duration
)
def _get_no_token_error_message(self, generation_type: str) -> str:
"""获取无可用Token时的详细错误信息"""
if generation_type == "image":
return "没有可用的Token进行图片生成。所有Token都处于禁用、冷却、锁定或已过期状态。"
else:
return "没有可用的Token进行视频生成。所有Token都处于禁用、冷却、配额耗尽或已过期状态。"
async def _handle_image_generation(
self,
token,
project_id: str,
model_config: dict,
prompt: str,
images: Optional[List[bytes]],
stream: bool
) -> AsyncGenerator:
"""处理图片生成 (同步返回)"""
# 获取并发槽位
if self.concurrency_manager:
if not await self.concurrency_manager.acquire_image(token.id):
yield self._create_error_response("图片并发限制已达上限")
return
try:
# 上传图片 (如果有)
image_inputs = []
if images and len(images) > 0:
if stream:
yield self._create_stream_chunk(f"上传 {len(images)} 张参考图片...\n")
# 支持多图输入
for idx, image_bytes in enumerate(images):
media_id = await self.flow_client.upload_image(
token.at,
image_bytes,
model_config["aspect_ratio"]
)
image_inputs.append({
"name": media_id,
"imageInputType": "IMAGE_INPUT_TYPE_REFERENCE"
})
if stream:
yield self._create_stream_chunk(f"已上传第 {idx + 1}/{len(images)} 张图片\n")
# 调用生成API
if stream:
yield self._create_stream_chunk("正在生成图片...\n")
result = await self.flow_client.generate_image(
at=token.at,
project_id=project_id,
prompt=prompt,
model_name=model_config["model_name"],
aspect_ratio=model_config["aspect_ratio"],
image_inputs=image_inputs
)
# 提取URL和mediaId
media = result.get("media", [])
if not media:
yield self._create_error_response("生成结果为空")
return
image_url = media[0]["image"]["generatedImage"]["fifeUrl"]
media_id = media[0].get("name") # 用于 upsample
# 检查是否需要 upsample
upsample_resolution = model_config.get("upsample")
if upsample_resolution and media_id:
resolution_name = "4K" if "4K" in upsample_resolution else "2K"
if stream:
yield self._create_stream_chunk(f"正在放大图片到 {resolution_name}...\n")
# 4K/2K 图片重试逻辑 - 最多重试3次
max_retries = 3
for retry_attempt in range(max_retries):
try:
# 调用 upsample API
encoded_image = await self.flow_client.upsample_image(
at=token.at,
project_id=project_id,
media_id=media_id,
target_resolution=upsample_resolution
)
if encoded_image:
debug_logger.log_info(f"[UPSAMPLE] 图片已放大到 {resolution_name}")
if stream:
yield self._create_stream_chunk(f"✅ 图片已放大到 {resolution_name}\n")
# 缓存放大后的图片 (如果启用)
# 日志统一记录原图URL (放大后的base64数据太大，不适合存储)
self._last_generated_url = image_url
if config.cache_enabled:
try:
if stream:
yield self._create_stream_chunk(f"缓存 {resolution_name} 图片中...\n")
cached_filename = await self.file_cache.cache_base64_image(encoded_image, resolution_name)
local_url = f"{self._get_base_url()}/tmp/{cached_filename}"
if stream:
yield self._create_stream_chunk(f"✅ {resolution_name} 图片缓存成功\n")
yield self._create_stream_chunk(
f"",
finish_reason="stop"
)
else:
yield self._create_completion_response(
local_url,
media_type="image"
)
return
except Exception as e:
debug_logger.log_error(f"Failed to cache {resolution_name} image: {str(e)}")
if stream:
yield self._create_stream_chunk(f"⚠️ 缓存失败: {str(e)}，返回 base64...\n")
# 缓存未启用或缓存失败，返回 base64 格式
base64_url = f"data:image/jpeg;base64,{encoded_image}"
if stream:
yield self._create_stream_chunk(
f"",
finish_reason="stop"
)
else:
yield self._create_completion_response(
base64_url,
media_type="image"
)
return
else:
debug_logger.log_warning("[UPSAMPLE] 返回结果为空")
if stream:
yield self._create_stream_chunk(f"⚠️ 放大失败，返回原图...\n")
break # 空结果不重试
except Exception as e:
error_str = str(e)
debug_logger.log_error(f"[UPSAMPLE] 放大失败 (尝试 {retry_attempt + 1}/{max_retries}): {error_str}")
# 检查是否是可重试错误（403、reCAPTCHA、超时等）
retry_reason = self.flow_client._get_retry_reason(error_str)
if retry_reason and retry_attempt < max_retries - 1:
if stream:
yield self._create_stream_chunk(f"⚠️ 放大遇到{retry_reason}，正在重试 ({retry_attempt + 2}/{max_retries})...\n")
# 等待一小段时间后重试
await asyncio.sleep(1)
continue
else:
if stream:
yield self._create_stream_chunk(f"⚠️ 放大失败: {error_str}，返回原图...\n")
break
# 缓存图片 (如果启用)
local_url = image_url
if config.cache_enabled:
try:
if stream:
yield self._create_stream_chunk("缓存图片中...\n")
cached_filename = await self.file_cache.download_and_cache(image_url, "image")
local_url = f"{self._get_base_url()}/tmp/{cached_filename}"
if stream:
yield self._create_stream_chunk("✅ 图片缓存成功,准备返回缓存地址...\n")
except Exception as e:
debug_logger.log_error(f"Failed to cache image: {str(e)}")
# 缓存失败不影响结果返回,使用原始URL
local_url = image_url
if stream:
yield self._create_stream_chunk(f"⚠️ 缓存失败: {str(e)}\n正在返回源链接...\n")
else:
if stream:
yield self._create_stream_chunk("缓存已关闭,正在返回源链接...\n")
# 返回结果
# 存储URL用于日志记录
self._last_generated_url = local_url
if stream:
yield self._create_stream_chunk(
f"",
finish_reason="stop"
)
else:
yield self._create_completion_response(
local_url, # 直接传URL,让方法内部格式化
media_type="image"
)
finally:
# 释放并发槽位
if self.concurrency_manager:
await self.concurrency_manager.release_image(token.id)
async def _handle_video_generation(
self,
token,
project_id: str,
model_config: dict,
prompt: str,
images: Optional[List[bytes]],
stream: bool
) -> AsyncGenerator:
"""处理视频生成 (异步轮询)"""
# 获取并发槽位
if self.concurrency_manager:
if not await self.concurrency_manager.acquire_video(token.id):
yield self._create_error_response("视频并发限制已达上限")
return
try:
# 获取模型类型和配置
video_type = model_config.get("video_type")
supports_images = model_config.get("supports_images", False)
min_images = model_config.get("min_images", 0)
max_images = model_config.get("max_images", 0)
# 根据账号tier自动调整模型 key
model_key = model_config["model_key"]
user_tier = token.user_paygate_tier or "PAYGATE_TIER_ONE"
# TIER_TWO 账号需要使用 ultra 版本的模型
if user_tier == "PAYGATE_TIER_TWO":
# 如果模型 key 不包含 ultra，自动添加
if "ultra" not in model_key:
# veo_3_1_i2v_s_fast_fl -> veo_3_1_i2v_s_fast_ultra_fl
# veo_3_1_i2v_s_fast_portrait_fl -> veo_3_1_i2v_s_fast_portrait_ultra_fl
# veo_3_1_t2v_fast -> veo_3_1_t2v_fast_ultra
# veo_3_1_t2v_fast_portrait -> veo_3_1_t2v_fast_portrait_ultra
# veo_3_0_r2v_fast -> veo_3_0_r2v_fast_ultra
if "_fl" in model_key:
model_key = model_key.replace("_fl", "_ultra_fl")
else:
# 直接在末尾添加 _ultra
model_key = model_key + "_ultra"
if stream:
yield self._create_stream_chunk(f"TIER_TWO 账号自动切换到 ultra 模型: {model_key}\n")
debug_logger.log_info(f"[VIDEO] TIER_TWO 账号，模型自动调整: {model_config['model_key']} -> {model_key}")
# TIER_ONE 账号需要使用非 ultra 版本
elif user_tier == "PAYGATE_TIER_ONE":
# 如果模型 key 包含 ultra，需要移除（避免用户误用）
if "ultra" in model_key:
# veo_3_1_i2v_s_fast_ultra_fl -> veo_3_1_i2v_s_fast_fl
# veo_3_1_t2v_fast_ultra -> veo_3_1_t2v_fast
model_key = model_key.replace("_ultra_fl", "_fl").replace("_ultra", "")
if stream:
yield self._create_stream_chunk(f"TIER_ONE 账号自动切换到标准模型: {model_key}\n")
debug_logger.log_info(f"[VIDEO] TIER_ONE 账号，模型自动调整: {model_config['model_key']} -> {model_key}")
# 更新 model_config 中的 model_key
model_config = dict(model_config) # 创建副本避免修改原配置
model_config["model_key"] = model_key
# 图片数量
image_count = len(images) if images else 0
# ========== 验证和处理图片 ==========
# T2V: 文生视频 - 不支持图片
if video_type == "t2v":
if image_count > 0:
if stream:
yield self._create_stream_chunk("⚠️ 文生视频模型不支持上传图片,将忽略图片仅使用文本提示词生成\n")
debug_logger.log_warning(f"[T2V] 模型 {model_config['model_key']} 不支持图片,已忽略 {image_count} 张图片")
images = None # 清空图片
image_count = 0
# I2V: 首尾帧模型 - 需要1-2张图片
elif video_type == "i2v":
if image_count < min_images or image_count > max_images:
error_msg = f"❌ 首尾帧模型需要 {min_images}-{max_images} 张图片,当前提供了 {image_count} 张"
if stream:
yield self._create_stream_chunk(f"{error_msg}\n")
yield self._create_error_response(error_msg)
return
# R2V: 多图生成 - 支持多张图片,不限制数量
elif video_type == "r2v":
# 不再限制最大图片数量
pass
# ========== 上传图片 ==========
start_media_id = None
end_media_id = None
reference_images = []
# I2V: 首尾帧处理
if video_type == "i2v" and images:
if image_count == 1:
# 只有1张图: 仅作为首帧
if stream:
yield self._create_stream_chunk("上传首帧图片...\n")
start_media_id = await self.flow_client.upload_image(
token.at, images[0], model_config["aspect_ratio"]
)
debug_logger.log_info(f"[I2V] 仅上传首帧: {start_media_id}")
elif image_count == 2:
# 2张图: 首帧+尾帧
if stream:
yield self._create_stream_chunk("上传首帧和尾帧图片...\n")
start_media_id = await self.flow_client.upload_image(
token.at, images[0], model_config["aspect_ratio"]
)
end_media_id = await self.flow_client.upload_image(
token.at, images[1], model_config["aspect_ratio"]
)
debug_logger.log_info(f"[I2V] 上传首尾帧: {start_media_id}, {end_media_id}")
# R2V: 多图处理
elif video_type == "r2v" and images:
if stream:
yield self._create_stream_chunk(f"上传 {image_count} 张参考图片...\n")
for idx, img in enumerate(images): # 上传所有图片,不限制数量
media_id = await self.flow_client.upload_image(
token.at, img, model_config["aspect_ratio"]
)
reference_images.append({
"imageUsageType": "IMAGE_USAGE_TYPE_ASSET",
"mediaId": media_id
})
debug_logger.log_info(f"[R2V] 上传了 {len(reference_images)} 张参考图片")
# ========== 调用生成API ==========
if stream:
yield self._create_stream_chunk("提交视频生成任务...\n")
# I2V: 首尾帧生成
if video_type == "i2v" and start_media_id:
if end_media_id:
# 有首尾帧
result = await self.flow_client.generate_video_start_end(
at=token.at,
project_id=project_id,
prompt=prompt,
model_key=model_config["model_key"],
aspect_ratio=model_config["aspect_ratio"],
start_media_id=start_media_id,
end_media_id=end_media_id,
user_paygate_tier=token.user_paygate_tier or "PAYGATE_TIER_ONE"
)
else:
# 只有首帧 - 需要去掉 model_key 中的 _fl
# 情况1: _fl_ 在中间 (如 veo_3_1_i2v_s_fast_fl_ultra_relaxed -> veo_3_1_i2v_s_fast_ultra_relaxed)
# 情况2: _fl 在结尾 (如 veo_3_1_i2v_s_fast_ultra_fl -> veo_3_1_i2v_s_fast_ultra)
actual_model_key = model_config["model_key"].replace("_fl_", "_")
if actual_model_key.endswith("_fl"):
actual_model_key = actual_model_key[:-3]
debug_logger.log_info(f"[I2V] 单帧模式，model_key: {model_config['model_key']} -> {actual_model_key}")
result = await self.flow_client.generate_video_start_image(
at=token.at,
project_id=project_id,
prompt=prompt,
model_key=actual_model_key,
aspect_ratio=model_config["aspect_ratio"],
start_media_id=start_media_id,
user_paygate_tier=token.user_paygate_tier or "PAYGATE_TIER_ONE"
)
# R2V: 多图生成
elif video_type == "r2v" and reference_images:
result = await self.flow_client.generate_video_reference_images(
at=token.at,
project_id=project_id,
prompt=prompt,
model_key=model_config["model_key"],
aspect_ratio=model_config["aspect_ratio"],
reference_images=reference_images,
user_paygate_tier=token.user_paygate_tier or "PAYGATE_TIER_ONE"
)
# T2V 或 R2V无图: 纯文本生成
else:
result = await self.flow_client.generate_video_text(
at=token.at,
project_id=project_id,
prompt=prompt,
model_key=model_config["model_key"],
aspect_ratio=model_config["aspect_ratio"],
user_paygate_tier=token.user_paygate_tier or "PAYGATE_TIER_ONE"
)
# 获取task_id和operations
operations = result.get("operations", [])
if not operations:
yield self._create_error_response("生成任务创建失败")
return
operation = operations[0]
task_id = operation["operation"]["name"]
scene_id = operation.get("sceneId")
# 保存Task到数据库
task = Task(
task_id=task_id,
token_id=token.id,
model=model_config["model_key"],
prompt=prompt,
status="processing",
scene_id=scene_id
)
await self.db.create_task(task)
# 轮询结果
if stream:
yield self._create_stream_chunk(f"视频生成中...\n")
# 检查是否需要放大
upsample_config = model_config.get("upsample")
async for chunk in self._poll_video_result(token, project_id, operations, stream, upsample_config):
yield chunk
finally:
# 释放并发槽位
if self.concurrency_manager:
await self.concurrency_manager.release_video(token.id)
async def _poll_video_result(
self,
token,
project_id: str,
operations: List[Dict],
stream: bool,
upsample_config: Optional[Dict] = None
) -> AsyncGenerator:
"""轮询视频生成结果
Args:
upsample_config: 放大配置 {"resolution": "VIDEO_RESOLUTION_4K", "model_key": "veo_3_1_upsampler_4k"}
"""
max_attempts = config.max_poll_attempts
poll_interval = config.poll_interval
# 如果需要放大，轮询次数加倍（放大可能需要 30 分钟）
if upsample_config:
max_attempts = max_attempts * 3 # 放大需要更长时间
for attempt in range(max_attempts):
await asyncio.sleep(poll_interval)
try:
result = await self.flow_client.check_video_status(token.at, operations)
checked_operations = result.get("operations", [])
if not checked_operations:
continue
operation = checked_operations[0]
status = operation.get("status")
# 状态更新 - 每20秒报告一次 (poll_interval=3秒, 20秒约7次轮询)
progress_update_interval = 7 # 每7次轮询 = 21秒
if stream and attempt % progress_update_interval == 0: # 每20秒报告一次
progress = min(int((attempt / max_attempts) * 100), 95)
yield self._create_stream_chunk(f"生成进度: {progress}%\n")
# 检查状态
if status == "MEDIA_GENERATION_STATUS_SUCCESSFUL":
# 成功
metadata = operation["operation"].get("metadata", {})
video_info = metadata.get("video", {})
video_url = video_info.get("fifeUrl")
video_media_id = video_info.get("mediaGenerationId")
aspect_ratio = video_info.get("aspectRatio", "VIDEO_ASPECT_RATIO_LANDSCAPE")
if not video_url:
yield self._create_error_response("视频URL为空")
return
# ========== 视频放大处理 ==========
if upsample_config and video_media_id:
if stream:
resolution_name = "4K" if "4K" in upsample_config["resolution"] else "1080P"
yield self._create_stream_chunk(f"\n视频生成完成，开始 {resolution_name} 放大处理...（可能需要 30 分钟）\n")
try:
# 提交放大任务
upsample_result = await self.flow_client.upsample_video(
at=token.at,
project_id=project_id,
video_media_id=video_media_id,
aspect_ratio=aspect_ratio,
resolution=upsample_config["resolution"],
model_key=upsample_config["model_key"]
)
upsample_operations = upsample_result.get("operations", [])
if upsample_operations:
if stream:
yield self._create_stream_chunk("放大任务已提交，继续轮询...\n")
# 递归轮询放大结果（不再放大）
async for chunk in self._poll_video_result(
token, project_id, upsample_operations, stream, None
):
yield chunk
return
else:
if stream:
yield self._create_stream_chunk("⚠️ 放大任务创建失败，返回原始视频\n")
except Exception as e:
debug_logger.log_error(f"Video upsample failed: {str(e)}")
if stream:
yield self._create_stream_chunk(f"⚠️ 放大失败: {str(e)}，返回原始视频\n")
# 缓存视频 (如果启用)
local_url = video_url
if config.cache_enabled:
try:
if stream:
yield self._create_stream_chunk("正在缓存视频文件...\n")
cached_filename = await self.file_cache.download_and_cache(video_url, "video")
local_url = f"{self._get_base_url()}/tmp/{cached_filename}"
if stream:
yield self._create_stream_chunk("✅ 视频缓存成功,准备返回缓存地址...\n")
except Exception as e:
debug_logger.log_error(f"Failed to cache video: {str(e)}")
# 缓存失败不影响结果返回,使用原始URL
local_url = video_url
if stream:
yield self._create_stream_chunk(f"⚠️ 缓存失败: {str(e)}\n正在返回源链接...\n")
else:
if stream:
yield self._create_stream_chunk("缓存已关闭,正在返回源链接...\n")
# 更新数据库
task_id = operation["operation"]["name"]
await self.db.update_task(
task_id,
status="completed",
progress=100,
result_urls=[local_url],
completed_at=time.time()
)
# 存储URL用于日志记录
self._last_generated_url = local_url
# 返回结果
if stream:
yield self._create_stream_chunk(
f"<video src='{local_url}' controls style='max-width:100%'></video>",
finish_reason="stop"
)
else:
yield self._create_completion_response(
local_url, # 直接传URL,让方法内部格式化
media_type="video"
)
return
elif status == "MEDIA_GENERATION_STATUS_FAILED":
# 生成失败 - 提取错误信息
error_info = operation.get("operation", {}).get("error", {})
error_code = error_info.get("code", "unknown")
error_message = error_info.get("message", "未知错误")
# 更新数据库任务状态
task_id = operation["operation"]["name"]
await self.db.update_task(
task_id,
status="failed",
error_message=f"{error_message} (code: {error_code})",
completed_at=time.time()
)
# 返回友好的错误消息，提示用户重试
friendly_error = f"视频生成失败: {error_message}，请重试"
if stream:
yield self._create_stream_chunk(f"❌ {friendly_error}\n")
yield self._create_error_response(friendly_error)
return
elif status.startswith("MEDIA_GENERATION_STATUS_ERROR"):
# 其他错误状态
yield self._create_error_response(f"视频生成失败: {status}")
return
except Exception as e:
debug_logger.log_error(f"Poll error: {str(e)}")
continue
# 超时
yield self._create_error_response(f"视频生成超时 (已轮询{max_attempts}次)")
# ========== 响应格式化 ==========
def _create_stream_chunk(self, content: str, role: str = None, finish_reason: str = None) -> str:
"""创建流式响应chunk"""
import json
import time
chunk = {
"id": f"chatcmpl-{int(time.time())}",
"object": "chat.completion.chunk",
"created": int(time.time()),
"model": "flow2api",
"choices": [{
"index": 0,
"delta": {},
"finish_reason": finish_reason
}]
}
if role:
chunk["choices"][0]["delta"]["role"] = role
if finish_reason:
chunk["choices"][0]["delta"]["content"] = content
else:
chunk["choices"][0]["delta"]["reasoning_content"] = content
return f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
def _create_completion_response(self, content: str, media_type: str = "image", is_availability_check: bool = False) -> str:
"""创建非流式响应
Args:
content: 媒体URL或纯文本消息
media_type: 媒体类型 ("image" 或 "video")
is_availability_check: 是否为可用性检查响应 (纯文本消息)
Returns:
JSON格式的响应
"""
import json
import time
# 可用性检查: 返回纯文本消息
if is_availability_check:
formatted_content = content
else:
# 媒体生成: 根据媒体类型格式化内容为Markdown
if media_type == "video":
formatted_content = f"```html\n<video src='{content}' controls></video>\n```"
else: # image
formatted_content = f""
response = {
"id": f"chatcmpl-{int(time.time())}",
"object": "chat.completion",
"created": int(time.time()),
"model": "flow2api",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": formatted_content
},
"finish_reason": "stop"
}]
}
return json.dumps(response, ensure_ascii=False)
def _create_error_response(self, error_message: str) -> str:
"""创建错误响应"""
import json
error = {
"error": {
"message": error_message,
"type": "invalid_request_error",
"code": "generation_failed"
}
}
return json.dumps(error, ensure_ascii=False)
def _get_base_url(self) -> str:
"""获取基础URL用于缓存文件访问"""
# 优先使用配置的cache_base_url
if config.cache_base_url:
return config.cache_base_url
# 否则使用服务器地址
return f"http://{config.server_host}:{config.server_port}"
async def _log_request(
self,
token_id: Optional[int],
operation: str,
request_data: Dict[str, Any],
response_data: Dict[str, Any],
status_code: int,
duration: float
):
"""记录请求到数据库"""
try:
log = RequestLog(
token_id=token_id,
operation=operation,
request_body=json.dumps(request_data, ensure_ascii=False),
response_body=json.dumps(response_data, ensure_ascii=False),
status_code=status_code,
duration=duration
)
await self.db.add_request_log(log)
except Exception as e:
# 日志记录失败不影响主流程
debug_logger.log_error(f"Failed to log request: {e}")
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