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Hugging Face Inference Endpoint — dots.ocr CPU handler.
Bu dosyayi Hub repo kokune yukle (handler.py).
HF modeli /repository altina mount eder; GPU aramaz.
"""
from __future__ import annotations
import base64
import io
import os
from typing import Any
os.environ.setdefault("LOCAL_RANK", "0")
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
import torch
from PIL import Image
from qwen_vl_utils import process_vision_info
from transformers import AutoModelForCausalLM, AutoProcessor
PROMPT_OCR = "Extract the text content from this image."
class EndpointHandler:
def __init__(self, path: str = "") -> None:
model_path = path or os.environ.get("MODEL_DIR") or "/repository"
self.processor = AutoProcessor.from_pretrained(model_path, trust_remote_code=True)
try:
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
trust_remote_code=True,
torch_dtype=torch.float32,
device_map="cpu",
low_cpu_mem_usage=True,
attn_implementation="sdpa",
)
except Exception:
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
trust_remote_code=True,
torch_dtype=torch.float32,
device_map="cpu",
low_cpu_mem_usage=True,
attn_implementation="eager",
)
self.model.eval()
def _load_image(self, raw: Any) -> Image.Image:
if isinstance(raw, Image.Image):
img = raw
elif isinstance(raw, str):
data = raw.split(",", 1)[1] if raw.startswith("data:") else raw
img = Image.open(io.BytesIO(base64.b64decode(data)))
elif isinstance(raw, (bytes, bytearray)):
img = Image.open(io.BytesIO(raw))
else:
raise ValueError("inputs: base64 string veya data:image/... beklenir")
img = img.convert("RGB")
w, h = img.size
m = max(w, h)
if m > 1024:
s = 1024 / float(m)
img = img.resize((max(32, int(w * s)), max(32, int(h * s))), Image.Resampling.LANCZOS)
return img
def __call__(self, data: dict[str, Any]) -> dict[str, Any]:
inputs = data.get("inputs", data)
params = data.get("parameters") or {}
if isinstance(inputs, dict):
raw = inputs.get("image") or inputs.get("image_url") or inputs.get("data")
prompt = inputs.get("prompt") or params.get("prompt") or PROMPT_OCR
else:
raw = inputs
prompt = params.get("prompt") or PROMPT_OCR
image = self._load_image(raw)
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": str(prompt)},
],
}
]
text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
kwargs = {
"text": [text],
"images": image_inputs,
"padding": True,
"return_tensors": "pt",
}
if video_inputs:
kwargs["videos"] = video_inputs
batch = self.processor(**kwargs)
max_new = int(params.get("max_new_tokens") or 2048)
with torch.inference_mode():
out_ids = self.model.generate(**batch, max_new_tokens=max_new)
trimmed = [o[len(i) :] for i, o in zip(batch.input_ids, out_ids)]
text_out = self.processor.batch_decode(
trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
return {"generated_text": text_out, "device": "cpu"}
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