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"""A simple script to run a Flow that can be used for development and debugging."""

import os

import hydra

import flows
from flows.flow_launchers import FlowLauncher, ApiInfo
from flows.utils.general_helpers import read_yaml_file

from flows import logging
from flows.flow_cache import CACHING_PARAMETERS, clear_cache

CACHING_PARAMETERS.do_caching = False  # Set to True to enable caching
# clear_cache() # Uncomment this line to clear the cache

logging.set_verbosity_debug()


if __name__ == "__main__":
    # ~~~ Set the API information ~~~
    # OpenAI backend
    # api_information = ApiInfo("openai", os.getenv("OPENAI_API_KEY"))
    # Azure backend
    api_information = ApiInfo("azure", os.getenv("AZURE_OPENAI_KEY"), os.getenv("AZURE_OPENAI_ENDPOINT"))

    # ~~~ Instantiate the Flow ~~~
    root_dir = "."
    cfg_path = os.path.join(root_dir, "FlowName.yaml")
    cfg = read_yaml_file(cfg_path)

    flow_with_interfaces = {
        "flow": hydra.utils.instantiate(cfg['flow'], _recursive_=False, _convert_="partial"),
        "input_interface": (
            None
            if getattr(cfg, "input_interface", None) is None
            else hydra.utils.instantiate(cfg['input_interface'], _recursive_=False)
        ),
        "output_interface": (
            None
            if getattr(cfg, "output_interface", None) is None
            else hydra.utils.instantiate(cfg['output_interface'], _recursive_=False)
        ),
    }

    # ~~~ Get the data ~~~
    # This can be a list of samples
    data = {"id": 0}  # Add your data here

    # ~~~ Run inference ~~~
    path_to_output_file = None
    # path_to_output_file = "output.jsonl"  # Uncomment this line to save the output to disk

    _, outputs = FlowLauncher.launch(
        flow_with_interfaces=flow_with_interfaces,
        data=data,
        path_to_output_file=path_to_output_file,
        api_information=api_information,
    )

    # ~~~ Print the output ~~~
    flow_output_data = outputs[0]
    print(flow_output_data)

	"""A simple script to run a Flow that can be used for development and debugging."""

	import os

	import hydra

	import flows
	from flows.flow_launchers import FlowLauncher, ApiInfo
	from flows.utils.general_helpers import read_yaml_file

	from flows import logging
	from flows.flow_cache import CACHING_PARAMETERS, clear_cache

	CACHING_PARAMETERS.do_caching = False # Set to True to enable caching
	# clear_cache() # Uncomment this line to clear the cache

	logging.set_verbosity_debug()


	if __name__ == "__main__":
	# ~~~ Set the API information ~~~
	# OpenAI backend
	# api_information = ApiInfo("openai", os.getenv("OPENAI_API_KEY"))
	# Azure backend
	api_information = ApiInfo("azure", os.getenv("AZURE_OPENAI_KEY"), os.getenv("AZURE_OPENAI_ENDPOINT"))

	# ~~~ Instantiate the Flow ~~~
	root_dir = "."
	cfg_path = os.path.join(root_dir, "FlowName.yaml")
	cfg = read_yaml_file(cfg_path)

	flow_with_interfaces = {
	"flow": hydra.utils.instantiate(cfg['flow'], _recursive_=False, _convert_="partial"),
	"input_interface": (
	None
	if getattr(cfg, "input_interface", None) is None
	else hydra.utils.instantiate(cfg['input_interface'], _recursive_=False)
	),
	"output_interface": (
	None
	if getattr(cfg, "output_interface", None) is None
	else hydra.utils.instantiate(cfg['output_interface'], _recursive_=False)
	),
	}

	# ~~~ Get the data ~~~
	# This can be a list of samples
	data = {"id": 0} # Add your data here

	# ~~~ Run inference ~~~
	path_to_output_file = None
	# path_to_output_file = "output.jsonl" # Uncomment this line to save the output to disk

	_, outputs = FlowLauncher.launch(
	flow_with_interfaces=flow_with_interfaces,
	data=data,
	path_to_output_file=path_to_output_file,
	api_information=api_information,
	)

	# ~~~ Print the output ~~~
	flow_output_data = outputs[0]
	print(flow_output_data)