Arquivos
Agent-S/gui_agents/s3/utils/common_utils.py
T
2025-10-04 02:40:47 +00:00

197 linhas
6.5 KiB
Python

import re
import time
from io import BytesIO
from PIL import Image
from typing import Tuple, Dict
from gui_agents.s3.memory.procedural_memory import PROCEDURAL_MEMORY
import logging
logger = logging.getLogger("desktopenv.agent")
def create_pyautogui_code(agent, code: str, obs: Dict) -> str:
"""
Attempts to evaluate the code into a pyautogui code snippet with grounded actions using the observation screenshot.
Args:
agent (ACI): The grounding agent to use for evaluation.
code (str): The code string to evaluate.
obs (Dict): The current observation containing the screenshot.
Returns:
exec_code (str): The pyautogui code to execute the grounded action.
Raises:
Exception: If there is an error in evaluating the code.
"""
agent.assign_screenshot(obs) # Necessary for grounding
exec_code = eval(code)
return exec_code
def call_llm_safe(
agent, temperature: float = 0.0, use_thinking: bool = False, **kwargs
) -> str:
# Retry if fails
max_retries = 3 # Set the maximum number of retries
attempt = 0
response = ""
while attempt < max_retries:
try:
response = agent.get_response(
temperature=temperature, use_thinking=use_thinking, **kwargs
)
assert response is not None, "Response from agent should not be None"
print("Response success!")
break # If successful, break out of the loop
except Exception as e:
attempt += 1
print(f"Attempt {attempt} failed: {e}")
if attempt == max_retries:
print("Max retries reached. Handling failure.")
time.sleep(1.0)
return response if response is not None else ""
def call_llm_formatted(generator, format_checkers, **kwargs):
"""
Calls the generator agent's LLM and ensures correct formatting.
Args:
generator (ACI): The generator agent to call.
obs (Dict): The current observation containing the screenshot.
format_checkers (Callable): Functions that take the response and return a tuple of (success, feedback).
**kwargs: Additional keyword arguments for the LLM call.
Returns:
response (str): The formatted response from the generator agent.
"""
max_retries = 3 # Set the maximum number of retries
attempt = 0
response = ""
if kwargs.get("messages") is None:
messages = generator.messages.copy() # Copy messages to avoid modifying the original
else:
messages = kwargs["messages"]
del kwargs["messages"] # Remove messages from kwargs to avoid passing it twice
while attempt < max_retries:
response = call_llm_safe(generator, messages=messages, **kwargs)
# Prepare feedback messages for incorrect formatting
feedback_msgs = []
for format_checker in format_checkers:
success, feedback = format_checker(response)
if not success:
feedback_msgs.append(feedback)
if not feedback_msgs:
# logger.info(f"Response formatted correctly on attempt {attempt} for {generator.engine.model}")
break
logger.error(
f"Response formatting error on attempt {attempt} for {generator.engine.model}. Response: {response} {', '.join(feedback_msgs)}"
)
messages.append(
{
"role": "assistant",
"content": [{"type": "text", "text": response}],
}
)
logger.info(f"Bad response: {response}")
delimiter = "\n- "
formatting_feedback = f"- {delimiter.join(feedback_msgs)}"
messages.append(
{
"role": "user",
"content": [
{
"type": "text",
"text": PROCEDURAL_MEMORY.FORMATTING_FEEDBACK_PROMPT.replace(
"FORMATTING_FEEDBACK", formatting_feedback
),
}
],
}
)
logger.info("Feedback:\n%s", formatting_feedback)
attempt += 1
if attempt == max_retries:
logger.error(
"Max retries reached when formatting response. Handling failure."
)
time.sleep(1.0)
return response
def split_thinking_response(full_response: str) -> Tuple[str, str]:
try:
# Extract thoughts section
thoughts = full_response.split("<thoughts>")[-1].split("</thoughts>")[0].strip()
# Extract answer section
answer = full_response.split("<answer>")[-1].split("</answer>")[0].strip()
return answer, thoughts
except Exception as e:
return full_response, ""
def parse_code_from_string(input_string):
"""Parses a string to extract each line of code enclosed in triple backticks (```)
Args:
input_string (str): The input string containing code snippets.
Returns:
str: The last code snippet found in the input string, or an empty string if no code is found.
"""
input_string = input_string.strip()
# This regular expression will match both ```code``` and ```python code```
# and capture the `code` part. It uses a non-greedy match for the content inside.
pattern = r"```(?:\w+\s+)?(.*?)```"
# Find all non-overlapping matches in the string
matches = re.findall(pattern, input_string, re.DOTALL)
if len(matches) == 0:
# return []
return ""
relevant_code = matches[
-1
] # We only care about the last match given it is the grounded action
return relevant_code
def extract_agent_functions(code):
"""Extracts all agent function calls from the given code.
Args:
code (str): The code string to search for agent function calls.
Returns:
list: A list of all agent function calls found in the code.
"""
pattern = r"(agent\.\w+\(\s*.*\))" # Matches
return re.findall(pattern, code)
def compress_image(image_bytes: bytes = None, image: Image = None) -> bytes:
"""Compresses an image represented as bytes.
Compression involves resizing image into half its original size and saving to webp format.
Args:
image_bytes (bytes): The image data to compress.
Returns:
bytes: The compressed image data.
"""
if not image:
image = Image.open(BytesIO(image_bytes))
output = BytesIO()
image.save(output, format="WEBP")
compressed_image_bytes = output.getvalue()
return compressed_image_bytes