4f189dc931
This CL implements: - The logic to export log to Excel. - Route the export logic in the UI. - Demonstrate this functionality in `./examples/promptui` project.
139 lines
4.8 KiB
Python
139 lines
4.8 KiB
Python
"""Export logs into Excel file"""
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import os
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import pickle
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from pathlib import Path
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from typing import Any, Dict, List, Type, Union
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import pandas as pd
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import yaml
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from theflow.storage import storage
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from theflow.utils.modules import import_dotted_string
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from kotaemon.base import BaseComponent
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def from_log_to_dict(pipeline_cls: Type[BaseComponent], log_config: dict) -> dict:
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"""Export the log to panda dataframes
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Args:
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pipeline_cls (Type[BaseComponent]): Pipeline class
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log_config (dict): Log config
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Returns:
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dataframe
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"""
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# get the directory
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pipeline_log_path = storage.url(pipeline_cls().config.store_result)
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dirs = list(sorted([f.path for f in os.scandir(pipeline_log_path) if f.is_dir()]))
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ids = []
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params: Dict[str, List[Any]] = {}
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inputs: Dict[str, List[Any]] = {}
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outputs: Dict[str, List[Any]] = {}
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for idx, each_dir in enumerate(dirs):
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ids.append(str(Path(each_dir).name))
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# get the params
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params_file = os.path.join(each_dir, "params.pkl")
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if os.path.exists(params_file):
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with open(params_file, "rb") as f:
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each_params = pickle.load(f)
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for key, value in each_params.items():
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if key not in params:
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params[key] = [None] * len(dirs)
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params[key][idx] = value
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progress_file = os.path.join(each_dir, "progress.pkl")
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if os.path.exists(progress_file):
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with open(progress_file, "rb") as f:
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progress = pickle.load(f)
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# get the inputs
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for each_input in log_config["inputs"]:
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name = each_input["name"]
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step = each_input["step"]
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if name not in inputs:
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inputs[name] = [None] * len(dirs)
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variable = each_input.get("variable", "")
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if variable:
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inputs[name][idx] = progress[step]["input"]["kwargs"][variable]
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else:
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inputs[name][idx] = progress[step]["input"]
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# get the outputs
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for each_output in log_config["outputs"]:
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name = each_output["name"]
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step = each_output["step"]
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if name not in outputs:
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outputs[name] = [None] * len(dirs)
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outputs[name][idx] = progress[step]["output"]
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if each_output.get("item", ""):
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outputs[name][idx] = outputs[name][each_output["item"]]
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return {"ids": ids, **params, **inputs, **outputs}
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def export(config: dict, pipeline_def, output_path):
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"""Export from config to Excel file"""
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pipeline_name = f"{pipeline_def.__module__}.{pipeline_def.__name__}"
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# export to Excel
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if not config.get("logs", {}):
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raise ValueError(f"Pipeline {pipeline_name} has no logs to export")
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pds: Dict[str, pd.DataFrame] = {}
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for log_name, log_def in config["logs"].items():
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pds[log_name] = pd.DataFrame(from_log_to_dict(pipeline_def, log_def))
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# from the list of pds, export to Excel to output_path
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with pd.ExcelWriter(output_path, engine="openpyxl") as writer: # type: ignore
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for log_name, df in pds.items():
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df.to_excel(writer, sheet_name=log_name)
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def export_from_dict(
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config: Union[str, dict],
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pipeline: Union[str, Type[BaseComponent]],
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output_path: str,
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):
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"""CLI to export the logs of a pipeline into Excel file
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Args:
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config_path (str): Path to the config file
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pipeline_name (str): Name of the pipeline
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output_path (str): Path to the output Excel file
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"""
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# get the pipeline class and the relevant config dict
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config_dict: dict
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if isinstance(config, str):
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with open(config) as f:
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config_dict = yaml.safe_load(f)
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elif isinstance(config, dict):
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config_dict = config
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else:
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raise TypeError(f"`config` must be str or dict, not {type(config)}")
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pipeline_name: str
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pipeline_cls: Type[BaseComponent]
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pipeline_config: dict
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if isinstance(pipeline, str):
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if pipeline not in config_dict:
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raise ValueError(f"Pipeline {pipeline} not found in config file")
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pipeline_name = pipeline
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pipeline_cls = import_dotted_string(pipeline, safe=False)
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pipeline_config = config_dict[pipeline]
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elif isinstance(pipeline, type) and issubclass(pipeline, BaseComponent):
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pipeline_name = f"{pipeline.__module__}.{pipeline.__name__}"
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if pipeline_name not in config_dict:
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raise ValueError(f"Pipeline {pipeline_name} not found in config file")
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pipeline_cls = pipeline
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pipeline_config = config_dict[pipeline_name]
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else:
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raise TypeError(
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f"`pipeline` must be str or subclass of BaseComponent, not {type(pipeline)}"
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)
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export(pipeline_config, pipeline_cls, output_path)
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