File size: 2,514 Bytes
fcc38f9 | 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 | import re
from PIL import Image
import numpy as np
from typing import List, Dict, Tuple, Union
def parse_llm_raw_response(response: str,special_token_list=None,action_sep=',',max_actions=3) -> Dict:
"""
assume a good format is <think>...</think><answer>...</answer>
returns a dict with keys:
- llm_raw_response: the original response
- llm_response: the response with <think> and <answer> tags
- think_content: the content inside <think> tag
- action_content: the content inside <answer> tag
- actions: a list of actions extracted from action_content
"""
pattern = r'<think>(.*?)</think>\s*<answer>(.*?)</answer>'
match = re.search(pattern, response, re.DOTALL)
format_correct = match is not None
if not match:
think_content, action_content, actions = "", "", []
else:
think_content, action_content = match.group(1), match.group(2)
if special_token_list is not None:
for special_token in special_token_list: # remove all special tokens in responses to forbid confusion in training
action_content = action_content.replace(special_token, "").strip()
think_content = think_content.replace(special_token, "").strip()
actions = [action.strip() for action in action_content.split(action_sep) if action.strip()]
if len(actions) > max_actions:
actions = actions[:max_actions] #Only the first MAX_ACTIONS actions are kept in the rollout.
action_content = (" " + action_sep + " ").join(actions)
llm_response = "<think>" + think_content.strip() + "</think>" + "<answer>" + action_content.strip() + "</answer>"
return {
"llm_raw_response": response,
"llm_response": llm_response,
"think_content": think_content,
"action_content": action_content,
"actions": actions,
"format_correct": format_correct,
}
def convert_numpy_to_PIL(numpy_array: np.ndarray) -> Image.Image:
"""Convert a numpy array to a PIL RGB image."""
if numpy_array.shape[-1] == 3:
# Convert numpy array to RGB PIL Image
return Image.fromarray(numpy_array, mode='RGB')
else:
raise ValueError(f"Unsupported number of channels: {numpy_array.shape[-1]}. Expected 3 (RGB).")
if __name__ == "__main__":
text = """
<think>
I am thinking about the problem.
</think>
<answer>
answer1, answer2, answer3
</answer>
"""
print(parse_llm_raw_response(text)) |