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3fdba7b ca17628 8128625 0ba1f5a 40f9682 8128625 83af6e8 3fdba7b 49630f8 16ceee2 49630f8 3fdba7b 5bdd01a 8128625 ba690ad 8128625 3fdba7b 16ceee2 8128625 3fdba7b 16ceee2 3fdba7b 147fcdd 1fe38b4 bd0b411 1fe38b4 05dd32e | 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 | from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from typing import Dict, List, Any
import torch
import io
from PIL import Image
import base64
import time
import uuid
prompt = """**Task**:
Analyze this document image exhaustively and output in Markdown format.
**Rules**:
- Do not add any comments, provide content only;
- Extract ALL visible text exactly as written;
- Preserve possible additional languages;
- Maintain line breaks, indentation, and spacing;
- Never translate non-English text.
- Do not add unnecessary or additional information. Do not add any links or images. Do not add Chinese symbols.
**Important**: the output format must be Markdown (use bold text, headlines, so on)."""
class EndpointHandler:
def __init__(self, path: str = "Qwen/Qwen3-VL-8B-Instruct"):
# Load tokenizer and model
self.processor = AutoProcessor.from_pretrained(path)
self.model = Qwen3VLForConditionalGeneration.from_pretrained(path, device_map="auto")
self.model.eval()
def __call__(self, data: Dict[str, Any]) -> str:
# Prepare your messages with image and text
inputs = data.get("inputs")
base64image = inputs["base64"]
img_bytes = base64.b64decode(base64image)
pil_img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": pil_img}, # pass PIL image directly
{"type": "text", "text": prompt},
]
}
]
# Process the input and generate a response
inputs = self.processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
)
inputs = inputs.to(self.model.device)
generated_ids = self.model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = self.processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
response = {
"id": f"chatcmpl-{uuid.uuid4().hex}",
"object": "chat.completion",
"created": int(time.time()),
"model": "Qwen/Qwen3-VL-8B-Instruct",
"usage": {
# you might compute these if you can get token counts
"prompt_tokens": None,
"completion_tokens": None,
"total_tokens": None
},
"choices": [
{
"message": {
"role": "assistant",
"content": output_text[0]
},
"finish_reason": "stop",
"index": 0
}
]
}
return response |