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92c5d3d 1e69d45 92c5d3d 1e69d45 92c5d3d 1e69d45 92c5d3d 1e69d45 92c5d3d 1e69d45 92c5d3d | 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 | import base64
import io
import json
import os
from typing import Any, Dict, List, Optional, Tuple
from PIL import Image
from transformers import AutoProcessor
from vllm import LLM, SamplingParams
def _b64_to_pil(data_url: str) -> Image.Image:
if not isinstance(data_url, str) or not data_url.startswith("data:"):
raise ValueError("Expected a data URL starting with 'data:'")
header, b64data = data_url.split(",", 1)
raw = base64.b64decode(b64data)
img = Image.open(io.BytesIO(raw))
img.load()
return img
class EndpointHandler:
"""HF Inference Endpoint handler for Qwen3-VL chat-to-point.
Input:
- { system, user, image(data URL) }
- or legacy OpenAI-style messages with image_url + text
Output:
- { points: [{x,y}], raw: <string> }
where x,y are normalized [0,1]
"""
def __init__(self, path: str = "") -> None:
model_id = os.environ.get("MODEL_ID") or "Qwen/Qwen3-VL-8B-Instruct"
os.environ.setdefault("OMP_NUM_THREADS", "1")
os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
os.environ.setdefault("HF_HUB_ENABLE_QUIC", "1")
os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
# Auto TP detection from visible GPUs
visible = os.environ.get("CUDA_VISIBLE_DEVICES")
if visible and visible.strip():
try:
candidates = [d for d in visible.split(",") if d.strip() and d.strip() != "-1"]
tp = max(1, len(candidates))
except Exception:
tp = 1
else:
try:
import torch # local import to avoid global dependency if CPU-only
tp = max(1, int(torch.cuda.device_count())) if torch.cuda.is_available() else 1
except Exception:
tp = 1
self._model_id = model_id
self._tp = tp
self.llm = None # type: ignore
hub_token = (
os.environ.get("HUGGINGFACE_HUB_TOKEN")
or os.environ.get("HF_HUB_TOKEN")
or os.environ.get("HF_TOKEN")
)
if hub_token and not os.environ.get("HF_TOKEN"):
try:
os.environ["HF_TOKEN"] = hub_token
except Exception:
pass
self.processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True, token=hub_token)
def _ensure_llm(self) -> None:
if self.llm is not None:
return
self.llm = LLM(
model=self._model_id,
tensor_parallel_size=self._tp,
pipeline_parallel_size=1,
gpu_memory_utilization=0.95,
max_model_len=8192,
dtype="auto",
distributed_executor_backend="mp",
enforce_eager=True,
trust_remote_code=True,
)
@staticmethod
def _parse_legacy_messages(messages: List[Dict[str, Any]]) -> Tuple[Optional[str], Optional[str], Optional[str]]:
system_prompt: Optional[str] = None
first_image_data_url: Optional[str] = None
first_text: Optional[str] = None
for msg in messages:
if msg.get("role") == "system" and system_prompt is None:
content = msg.get("content")
if isinstance(content, str):
system_prompt = content
if msg.get("role") == "user":
content = msg.get("content", [])
if not isinstance(content, list):
continue
for part in content:
if part.get("type") == "image_url" and not first_image_data_url:
url = part.get("image_url", {}).get("url")
if isinstance(url, str) and url.startswith("data:"):
first_image_data_url = url
if part.get("type") == "text" and not first_text:
t = part.get("text")
if isinstance(t, str):
first_text = t
return system_prompt, first_text, first_image_data_url
def __call__(self, data: Dict[str, Any]) -> Any:
# Normalize HF toolkit payloads
if isinstance(data, dict) and "inputs" in data:
inputs_val = data.get("inputs")
if isinstance(inputs_val, dict):
data = inputs_val
elif isinstance(inputs_val, (str, bytes, bytearray)):
try:
if isinstance(inputs_val, (bytes, bytearray)):
inputs_val = inputs_val.decode("utf-8")
parsed = json.loads(inputs_val)
if isinstance(parsed, dict):
data = parsed
except Exception:
pass
system_prompt: Optional[str] = None
user_text: Optional[str] = None
image_data_url: Optional[str] = None
if isinstance(data, dict) and ("system" in data or "user" in data or "image" in data):
system_prompt = data.get("system")
user_text = data.get("user")
image_data_url = data.get("image")
if not isinstance(image_data_url, str) or not image_data_url.startswith("data:"):
return {"error": "image must be a data URL (data:...)"}
else:
messages = data.get("messages") if isinstance(data, dict) else None
if not messages:
return {"error": "Provide 'system','user','image' or legacy 'messages'"}
system_prompt, user_text, image_data_url = self._parse_legacy_messages(messages)
if not isinstance(image_data_url, str) or not image_data_url.startswith("data:"):
return {"error": "messages.content image_url.url must be a data URL (data:...)"}
try:
pil = _b64_to_pil(image_data_url)
except Exception as e:
return {"error": f"Failed to decode image: {e}"}
width = getattr(pil, "width", None)
height = getattr(pil, "height", None)
if isinstance(width, int) and isinstance(height, int):
try:
print(f"[qwen3-vl-endpoint] Received image size: {width}x{height}")
except Exception:
pass
if not isinstance(user_text, str):
return {"error": "user text must be provided"}
system_message = {"role": "system", "content": system_prompt or ""}
user_message = {
"role": "user",
"content": [
{"type": "image", "image": pil},
{"type": "text", "text": user_text},
],
}
prompt = self.processor.apply_chat_template(
[system_message, user_message], tokenize=False, add_generation_prompt=True
)
request: Dict[str, Any] = {"prompt": prompt}
request["multi_modal_data"] = {"image": [pil]}
import time
t0 = time.time()
self._ensure_llm()
sampling_params = SamplingParams(max_tokens=16, temperature=0.0, top_p=1.0)
outputs = self.llm.generate([request], sampling_params=sampling_params, use_tqdm=False)
out_text = outputs[0].outputs[0].text
out_text_short = out_text[:20]
t1 = time.time()
try:
print(f"[qwen3-vl-endpoint] Prompt: {user_text}")
print(f"[qwen3-vl-endpoint] Raw output: {out_text_short}")
print(f"[qwen3-vl-endpoint] Inference time: {t1 - t0:.3f}s")
except Exception:
pass
try:
import re
m = re.findall(r"\((-?\d*\.?\d+),\s*(-?\d*\.?\d+)\)", out_text)
if not m:
return {"error": "Failed to parse coordinates from model output."}
x_str, y_str = m[0]
px, py = float(x_str), float(y_str)
if not isinstance(width, int) or not isinstance(height, int):
return {"error": "Missing image dimensions for normalization."}
w, h = float(width), float(height)
px = max(0.0, min(px, w))
py = max(0.0, min(py, h))
nx, ny = px / w, py / h
return {"points": [{"x": nx, "y": ny}], "raw": out_text_short}
except Exception as e:
return {"error": f"Postprocessing failed: {e}"}
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