inference.py 1.4 KB

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  1. import json
  2. import io
  3. import base64
  4. import math
  5. from PIL import Image
  6. import requests
  7. from dots_ocr.utils.image_utils import PILimage_to_base64
  8. from openai import OpenAI
  9. import os
  10. def inference_with_vllm(
  11. image,
  12. prompt,
  13. protocol="http",
  14. ip="localhost",
  15. port=8000,
  16. temperature=0.1,
  17. top_p=0.9,
  18. max_completion_tokens=32768,
  19. model_name='model',
  20. ):
  21. addr = f"{protocol}://{ip}:{port}/v1"
  22. client = OpenAI(api_key="{}".format(os.environ.get("API_KEY", "0")), base_url=addr)
  23. messages = []
  24. messages.append(
  25. {
  26. "role": "user",
  27. "content": [
  28. {
  29. "type": "image_url",
  30. "image_url": {"url": PILimage_to_base64(image)},
  31. },
  32. {"type": "text", "text": f"<|img|><|imgpad|><|endofimg|>{prompt}"} # if no "<|img|><|imgpad|><|endofimg|>" here,vllm v1 will add "\n" here
  33. ],
  34. }
  35. )
  36. try:
  37. response = client.chat.completions.create(
  38. messages=messages,
  39. model=model_name,
  40. max_completion_tokens=max_completion_tokens,
  41. temperature=temperature,
  42. top_p=top_p)
  43. response = response.choices[0].message.content
  44. return response
  45. except requests.exceptions.RequestException as e:
  46. print(f"request error: {e}")
  47. return None