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99a7ebb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | from __future__ import annotations
import base64
import time
import uuid
from typing import Any, Iterable, Iterator
from fastapi import HTTPException
from services.protocol.conversation import (
ConversationRequest,
ImageOutput,
encode_images,
stream_image_outputs_with_pool,
stream_text_deltas,
text_backend,
)
from utils.helper import extract_image_from_message_content, extract_response_prompt, has_response_image_generation_tool
def is_text_response_request(body: dict[str, Any]) -> bool:
return not has_response_image_generation_tool(body)
def extract_response_image(input_value: object) -> tuple[bytes, str] | None:
if isinstance(input_value, dict):
images = extract_image_from_message_content(input_value.get("content"))
return images[0] if images else None
if not isinstance(input_value, list):
return None
for item in reversed(input_value):
if isinstance(item, dict) and str(item.get("type") or "").strip() == "input_image":
image_url = str(item.get("image_url") or "")
if image_url.startswith("data:"):
header, _, data = image_url.partition(",")
mime = header.split(";")[0].removeprefix("data:")
return base64.b64decode(data), mime or "image/png"
if isinstance(item, dict):
images = extract_image_from_message_content(item.get("content"))
if images:
return images[0]
return None
def messages_from_input(input_value: object, instructions: object = None) -> list[dict[str, Any]]:
messages: list[dict[str, Any]] = []
system_text = str(instructions or "").strip()
if system_text:
messages.append({"role": "system", "content": system_text})
if isinstance(input_value, str):
if input_value.strip():
messages.append({"role": "user", "content": input_value.strip()})
return messages
if isinstance(input_value, dict):
messages.append({
"role": str(input_value.get("role") or "user"),
"content": extract_response_prompt([input_value]) or input_value.get("content") or "",
})
return messages
if isinstance(input_value, list):
if all(isinstance(item, dict) and item.get("type") for item in input_value):
text = extract_response_prompt(input_value)
if text:
messages.append({"role": "user", "content": text})
return messages
for item in input_value:
if isinstance(item, dict):
messages.append({
"role": str(item.get("role") or "user"),
"content": extract_response_prompt([item]) or item.get("content") or "",
})
return messages
def text_output_item(text: str, item_id: str | None = None, status: str = "completed") -> dict[str, Any]:
return {
"id": item_id or f"msg_{uuid.uuid4().hex}",
"type": "message",
"status": status,
"role": "assistant",
"content": [{"type": "output_text", "text": text, "annotations": []}],
}
def image_output_items(prompt: str, data: list[dict[str, Any]], item_id: str | None = None) -> list[dict[str, Any]]:
output = []
for item in data:
b64_json = str(item.get("b64_json") or "").strip()
if b64_json:
output.append({
"id": item_id or f"ig_{len(output) + 1}",
"type": "image_generation_call",
"status": "completed",
"result": b64_json,
"revised_prompt": str(item.get("revised_prompt") or prompt).strip() or prompt,
})
return output
def response_created(response_id: str, model: str, created: int) -> dict[str, Any]:
return {
"type": "response.created",
"response": {
"id": response_id,
"object": "response",
"created_at": created,
"status": "in_progress",
"error": None,
"incomplete_details": None,
"model": model,
"output": [],
"parallel_tool_calls": False,
},
}
def response_completed(response_id: str, model: str, created: int, output: list[dict[str, Any]]) -> dict[str, Any]:
return {
"type": "response.completed",
"response": {
"id": response_id,
"object": "response",
"created_at": created,
"status": "completed",
"error": None,
"incomplete_details": None,
"model": model,
"output": output,
"parallel_tool_calls": False,
},
}
def stream_text_response(backend, body: dict[str, Any]) -> Iterator[dict[str, Any]]:
model = str(body.get("model") or "auto").strip() or "auto"
messages = messages_from_input(body.get("input"), body.get("instructions"))
response_id = f"resp_{uuid.uuid4().hex}"
item_id = f"msg_{uuid.uuid4().hex}"
created = int(time.time())
full_text = ""
yield response_created(response_id, model, created)
yield {"type": "response.output_item.added", "output_index": 0, "item": text_output_item("", item_id, "in_progress")}
request = ConversationRequest(model=model, messages=messages)
for delta in stream_text_deltas(backend, request):
full_text += delta
yield {"type": "response.output_text.delta", "item_id": item_id, "output_index": 0, "content_index": 0, "delta": delta}
yield {"type": "response.output_text.done", "item_id": item_id, "output_index": 0, "content_index": 0, "text": full_text}
item = text_output_item(full_text, item_id, "completed")
yield {"type": "response.output_item.done", "output_index": 0, "item": item}
yield response_completed(response_id, model, created, [item])
def stream_image_response(image_outputs: Iterable[ImageOutput], prompt: str, model: str) -> Iterator[dict[str, Any]]:
response_id = f"resp_{uuid.uuid4().hex}"
created = int(time.time())
yield response_created(response_id, model, created)
for output in image_outputs:
if output.kind == "message":
text = output.text
item = text_output_item(text)
yield {"type": "response.output_text.delta", "item_id": item["id"], "output_index": 0, "content_index": 0, "delta": text}
yield {"type": "response.output_text.done", "item_id": item["id"], "output_index": 0, "content_index": 0, "text": text}
yield {"type": "response.output_item.done", "output_index": 0, "item": item}
yield response_completed(response_id, model, created, [item])
return
if output.kind != "result":
continue
items = image_output_items(prompt, output.data)
if items:
item = items[0]
yield {"type": "response.output_item.done", "output_index": 0, "item": item}
yield response_completed(response_id, model, created, [item])
return
raise RuntimeError("image generation failed")
def collect_response(events: Iterable[dict[str, Any]]) -> dict[str, Any]:
completed = {}
for event in events:
if event.get("type") == "response.completed":
completed = event.get("response") if isinstance(event.get("response"), dict) else {}
if not completed:
raise RuntimeError("response generation failed")
return completed
def response_events(body: dict[str, Any]) -> Iterator[dict[str, Any]]:
if is_text_response_request(body):
yield from stream_text_response(text_backend(), body)
return
prompt = extract_response_prompt(body.get("input"))
if not prompt:
raise HTTPException(status_code=400, detail={"error": "input text is required"})
model = str(body.get("model") or "gpt-image-2").strip() or "gpt-image-2"
image_info = extract_response_image(body.get("input"))
if image_info:
image_data, mime_type = image_info
images = encode_images([(image_data, "image.png", mime_type)])
else:
images = None
image_outputs = stream_image_outputs_with_pool(ConversationRequest(
prompt=prompt,
model=model,
size=None if images else "1:1",
response_format="b64_json",
images=images,
))
yield from stream_image_response(image_outputs, prompt, model)
def handle(body: dict[str, Any]) -> dict[str, Any] | Iterator[dict[str, Any]]:
events = response_events(body)
if body.get("stream"):
return events
return collect_response(events)
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