Spaces:
Paused
Paused
File size: 6,925 Bytes
9d7ddb9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | from __future__ import annotations
from fastapi import APIRouter, File, Form, Header, HTTPException, Request, UploadFile
from fastapi.concurrency import run_in_threadpool
from pydantic import BaseModel, ConfigDict, Field
from api.support import require_identity, resolve_image_base_url
from services.content_filter import check_request, request_text
from services.log_service import LoggedCall
from services.protocol import (
anthropic_v1_messages,
openai_v1_chat_complete,
openai_v1_image_edit,
openai_v1_image_generations,
openai_v1_models,
openai_v1_response,
)
class ImageGenerationRequest(BaseModel):
prompt: str = Field(..., min_length=1)
model: str = "gpt-image-2"
n: int = Field(default=1, ge=1, le=4)
size: str | None = None
response_format: str = "b64_json"
history_disabled: bool = True
stream: bool | None = None
class ChatCompletionRequest(BaseModel):
model_config = ConfigDict(extra="allow")
model: str | None = None
prompt: str | None = None
n: int | None = None
stream: bool | None = None
modalities: list[str] | None = None
messages: list[dict[str, object]] | None = None
class ResponseCreateRequest(BaseModel):
model_config = ConfigDict(extra="allow")
model: str | None = None
input: object | None = None
tools: list[dict[str, object]] | None = None
tool_choice: object | None = None
stream: bool | None = None
class AnthropicMessageRequest(BaseModel):
model_config = ConfigDict(extra="allow")
model: str | None = None
messages: list[dict[str, object]] | None = None
system: object | None = None
stream: bool | None = None
async def filter_or_log(call: LoggedCall, text: str) -> None:
try:
await run_in_threadpool(check_request, text)
except HTTPException as exc:
call.log("调用失败", status="failed", error=str(exc.detail))
raise
def create_router() -> APIRouter:
router = APIRouter()
@router.get("/v1/models")
async def list_models(authorization: str | None = Header(default=None)):
require_identity(authorization)
try:
return await run_in_threadpool(openai_v1_models.list_models)
except Exception as exc:
raise HTTPException(status_code=502, detail={"error": str(exc)}) from exc
@router.post("/v1/images/generations")
async def generate_images(
body: ImageGenerationRequest,
request: Request,
authorization: str | None = Header(default=None),
):
identity = require_identity(authorization)
payload = body.model_dump(mode="python")
payload["base_url"] = resolve_image_base_url(request)
call = LoggedCall(identity, "/v1/images/generations", body.model, "文生图", request_text=body.prompt)
await filter_or_log(call, body.prompt)
return await call.run(openai_v1_image_generations.handle, payload)
@router.post("/v1/images/edits")
async def edit_images(
request: Request,
authorization: str | None = Header(default=None),
image: list[UploadFile] | None = File(default=None),
image_list: list[UploadFile] | None = File(default=None, alias="image[]"),
prompt: str = Form(...),
model: str = Form(default="gpt-image-2"),
n: int = Form(default=1),
size: str | None = Form(default=None),
response_format: str = Form(default="b64_json"),
stream: bool | None = Form(default=None),
):
identity = require_identity(authorization)
call = LoggedCall(identity, "/v1/images/edits", model, "图生图", request_text=prompt)
if n < 1 or n > 4:
raise HTTPException(status_code=400, detail={"error": "n must be between 1 and 4"})
await filter_or_log(call, prompt)
uploads = [*(image or []), *(image_list or [])]
if not uploads:
raise HTTPException(status_code=400, detail={"error": "image file is required"})
images: list[tuple[bytes, str, str]] = []
for upload in uploads:
image_data = await upload.read()
if not image_data:
raise HTTPException(status_code=400, detail={"error": "image file is empty"})
images.append((image_data, upload.filename or "image.png", upload.content_type or "image/png"))
payload = {
"prompt": prompt,
"images": images,
"model": model,
"n": n,
"size": size,
"response_format": response_format,
"stream": stream,
"base_url": resolve_image_base_url(request),
}
return await call.run(openai_v1_image_edit.handle, payload)
@router.post("/v1/chat/completions")
async def create_chat_completion(body: ChatCompletionRequest, authorization: str | None = Header(default=None)):
identity = require_identity(authorization)
payload = body.model_dump(mode="python")
model = str(payload.get("model") or "auto")
request_preview = request_text(payload.get("prompt"), payload.get("messages"))
call = LoggedCall(identity, "/v1/chat/completions", model, "文本生成", request_text=request_preview)
await filter_or_log(call, request_preview)
return await call.run(openai_v1_chat_complete.handle, payload)
@router.post("/v1/responses")
async def create_response(body: ResponseCreateRequest, authorization: str | None = Header(default=None)):
identity = require_identity(authorization)
payload = body.model_dump(mode="python")
model = str(payload.get("model") or "auto")
request_preview = request_text(payload.get("input"), payload.get("instructions"))
call = LoggedCall(identity, "/v1/responses", model, "Responses", request_text=request_preview)
await filter_or_log(call, request_preview)
return await call.run(openai_v1_response.handle, payload)
@router.post("/v1/messages")
async def create_message(
body: AnthropicMessageRequest,
authorization: str | None = Header(default=None),
x_api_key: str | None = Header(default=None, alias="x-api-key"),
anthropic_version: str | None = Header(default=None, alias="anthropic-version"),
):
identity = require_identity(authorization or (f"Bearer {x_api_key}" if x_api_key else None))
payload = body.model_dump(mode="python")
model = str(payload.get("model") or "auto")
request_preview = request_text(payload.get("system"), payload.get("messages"), payload.get("tools"))
call = LoggedCall(identity, "/v1/messages", model, "Messages", request_text=request_preview)
await filter_or_log(call, request_preview)
return await call.run(anthropic_v1_messages.handle, payload, sse="anthropic")
return router
|