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import subprocess
from typing import Any, Dict, List
from swarms.utils.loguru_logger import initialize_logger
from pydantic import BaseModel
from swarms.structs.agent import Agent
logger = initialize_logger(log_folder="pandas_utils")
try:
import pandas as pd
except ImportError:
logger.error("Failed to import pandas")
subprocess.run(["pip", "install", "pandas"])
import pandas as pd
def display_agents_info(agents: List[Agent]) -> None:
"""
Displays information about all agents in a list using a DataFrame.
:param agents: List of Agent instances.
"""
# Extracting relevant information from each agent
agent_data = []
for agent in agents:
try:
agent_info = {
"ID": agent.id,
"Name": agent.agent_name,
"Description": agent.description,
"max_loops": agent.max_loops,
# "Docs": agent.docs,
"System Prompt": agent.system_prompt,
"LLM Model": agent.llm.model_name, # type: ignore
}
agent_data.append(agent_info)
except AttributeError as e:
logger.error(
f"Failed to extract information from agent {agent}: {e}"
)
continue
# Creating a DataFrame to display the data
try:
df = pd.DataFrame(agent_data)
except Exception as e:
logger.error(f"Failed to create DataFrame: {e}")
return
# Displaying the DataFrame
try:
print(df)
except Exception as e:
logger.error(f"Failed to print DataFrame: {e}")
def dict_to_dataframe(data: Dict[str, Any]) -> pd.DataFrame:
"""
Converts a dictionary into a pandas DataFrame.
:param data: Dictionary to convert.
:return: A pandas DataFrame representation of the dictionary.
"""
# Convert dictionary to DataFrame
df = pd.json_normalize(data)
return df
def pydantic_model_to_dataframe(model: BaseModel) -> pd.DataFrame:
"""
Converts a Pydantic Base Model into a pandas DataFrame.
:param model: Pydantic Base Model to convert.
:return: A pandas DataFrame representation of the Pydantic model.
"""
# Convert Pydantic model to dictionary
model_dict = model.dict()
# Convert dictionary to DataFrame
df = dict_to_dataframe(model_dict)
return df
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