Spaces:
Running
on
Zero
Running
on
Zero
File size: 1,707 Bytes
c0fff99 |
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 |
"""
Utility functions for model outputs: thinking token stripping, image encoding,
prompt repetition, etc.
"""
import re
import base64
from io import BytesIO
from PIL import Image
from config import ENABLE_PROMPT_REPETITION
# MedGemma wraps internal reasoning in <unused94>...<unused95> tags
THINKING_PATTERN = re.compile(r"<unused94>.*?<unused95>", re.DOTALL)
def strip_thinking_tokens(text: str) -> str:
"""Remove MedGemma's internal thinking tokens from output."""
return THINKING_PATTERN.sub("", text).strip()
def image_to_base64(image: Image.Image, fmt: str = "PNG") -> str:
"""Convert PIL Image to base64 data URL string."""
buffer = BytesIO()
image.save(buffer, format=fmt)
encoded = base64.b64encode(buffer.getvalue()).decode("utf-8")
return f"data:image/{fmt.lower()};base64,{encoded}"
def apply_prompt_repetition(prompt: str) -> str:
"""Repeat the user prompt to improve LLM output quality.
Based on "Prompt Repetition Improves Non-Reasoning LLMs"
(arXiv:2512.14982, Google Research 2025): repeating the input prompt
wins 47/70 benchmark-model combos with 0 losses. Uses the verbose
variant with a transition phrase for clarity.
"""
if not ENABLE_PROMPT_REPETITION:
return prompt
return f"{prompt}\n\nLet me repeat the request:\n\n{prompt}"
def resize_for_medgemma(image: Image.Image, max_size: int = 896) -> Image.Image:
"""Resize image to fit MedGemma's expected input resolution (896x896)."""
if max(image.size) <= max_size:
return image
ratio = max_size / max(image.size)
new_size = (int(image.size[0] * ratio), int(image.size[1] * ratio))
return image.resize(new_size, Image.LANCZOS)
|