import copy
import glob
import inspect
import json
import os
import random
import sys
import re
from typing import Dict, List, Any, Callable, Tuple, TextIO
from argparse import ArgumentParser
import black


from comfyui_to_python_utils import (
    import_custom_nodes,
    find_path,
    add_comfyui_directory_to_sys_path,
    add_extra_model_paths,
    get_value_at_index
)

add_comfyui_directory_to_sys_path()
from nodes import NODE_CLASS_MAPPINGS

class FileHandler:
    """Handles reading and writing files.

    This class provides methods to read JSON data from an input file and write code to an output file.
    """

    @staticmethod
    def read_json_file(file_path: str | TextIO, encoding: str = "utf-8") -> dict:
        """
        Reads a JSON file and returns its contents as a dictionary.

        Args:
            file_path (str): The path to the JSON file.

        Returns:
            dict: The contents of the JSON file as a dictionary.

        Raises:
            FileNotFoundError: If the file is not found, it lists all JSON files in the directory of the file path.
            ValueError: If the file is not a valid JSON.
        """

        if hasattr(file_path, "read"):
            return json.load(file_path)
        with open(file_path, "r", encoding="utf-8") as file:
            data = json.load(file)
        return data

    @staticmethod
    def write_code_to_file(file_path: str | TextIO, code: str) -> None:
        """Write the specified code to a Python file.

        Args:
            file_path (str): The path to the Python file.
            code (str): The code to write to the file.

        Returns:
            None
        """
        if isinstance(file_path, str):
            # Extract directory from the filename
            directory = os.path.dirname(file_path)

            # If the directory does not exist, create it
            if directory and not os.path.exists(directory):
                os.makedirs(directory)

            # Save the code to a .py file
            with open(file_path, "w", encoding="utf-8") as file:
                file.write(code)
        else:
            file_path.write(code)


class LoadOrderDeterminer:
    """Determine the load order of each key in the provided dictionary.

    This class places the nodes without node dependencies first, then ensures that any node whose
    result is used in another node will be added to the list in the order it should be executed.

    Attributes:
        data (Dict): The dictionary for which to determine the load order.
        node_class_mappings (Dict): Mappings of node classes.
    """

    def __init__(self, data: Dict, node_class_mappings: Dict):
        """Initialize the LoadOrderDeterminer with the given data and node class mappings.

        Args:
            data (Dict): The dictionary for which to determine the load order.
            node_class_mappings (Dict): Mappings of node classes.
        """
        self.data = data
        self.node_class_mappings = node_class_mappings
        self.visited = {}
        self.load_order = []
        self.is_special_function = False
        self.is_loader_function = False

    def determine_load_order(self) -> List[Tuple[str, Dict, bool, bool]]:
        """Determine the load order for the given data.

        Returns:
            List[Tuple[str, Dict, bool, bool]]: A list of tuples representing the load order.
        """
        self._load_special_functions_first()
        self.is_special_function = False
        self.is_loader_function = False
        for key in self.data:
            if key not in self.visited:
                self._dfs(key)
        return self.load_order

    def _dfs(self, key: str) -> None:
        """Depth-First Search function to determine the load order.

        Args:
            key (str): The key from which to start the DFS.

        Returns:
            None
        """
        # Mark the node as visited.
        self.visited[key] = True
        inputs = self.data[key]["inputs"]
        # Loop over each input key.
        for input_key, val in inputs.items():
            # If the value is a list and the first item in the list has not been visited yet,
            # then recursively apply DFS on the dependency.
            if isinstance(val, list) and val[0] not in self.visited:
                self._dfs(val[0])
        # Add the key and its corresponding data to the load order list.
        self.load_order.append((key, self.data[key], self.is_special_function, self.is_loader_function))

    def _load_special_functions_first(self) -> None:
        """Load functions without dependencies, loaders, and encoders first.

        Returns:
            None
        """
        # Iterate over each key in the data to check for loader keys.
        for key in self.data:
            class_def = self.node_class_mappings[self.data[key]["class_type"]]()
            # Check if the class is a loader class or meets specific conditions.
            self.is_special_function = (
                class_def.CATEGORY == "loaders"
                # or class_def.FUNCTION in ["encode"]
                or not any(
                    isinstance(val, list) for val in self.data[key]["inputs"].values()
                )
            ) and class_def.CATEGORY != "FramerComfy"
            # Track if this is specifically a loader function
            self.is_loader_function = class_def.CATEGORY == "loaders"
            
            if self.is_special_function:
                # If the key has not been visited, perform a DFS from that key.
                if key not in self.visited:
                    self._dfs(key)


class CodeGenerator:
    """Generates Python code for a workflow based on the load order.

    Attributes:
        node_class_mappings (Dict): Mappings of node classes.
        base_node_class_mappings (Dict): Base mappings of node classes.
    """

    def __init__(self, node_class_mappings: Dict, base_node_class_mappings: Dict, workflow_models: List = None):
        """Initialize the CodeGenerator with given node class mappings.

        Args:
            node_class_mappings (Dict): Mappings of node classes.
            base_node_class_mappings (Dict): Base mappings of node classes.
            workflow_models (List): List of models to download from huggingface.
        """
        self.node_class_mappings = node_class_mappings
        self.base_node_class_mappings = base_node_class_mappings
        self.workflow_models = workflow_models or []
        self.input_nodes = {}  # Store input nodes and their variable names
        self.output_nodes = []  # Store output node variable names

    def collect_framer_nodes(self, load_order: List) -> Tuple[Dict, List]:
        """Collect FramerComfy input and output nodes from the load order.
        
        Args:
            load_order (List): List of tuples containing node information.
            
        Returns:
            Tuple[Dict, List]: Dictionary of input parameters and list of output variables.
        """
        for idx, data, _, _ in load_order:
            class_type = data["class_type"]
            if class_type.startswith("FramerComfy") and "Input" in class_type:
                # Extract input parameter name and default value from the node
                class_def = self.node_class_mappings[class_type]()
                param_name = data.get("inputs", {}).get("name", f"param_{idx}")
                # default_value = data.get("inputs", {}).get("default_value", None)
                default_value = None

                param_type = class_def.__class__.__name__.replace("FramerComfyInput", "").lower()
                self.input_nodes[param_name] = {
                    "var_name": f"{self.clean_variable_name(class_type)}_{idx}",
                    "default": default_value,
                    "type": param_type
                }
            elif class_type.startswith("FramerComfy") and "Save" in class_type:
                var_name = f"{self.clean_variable_name(class_type)}_{idx}"
                self.output_nodes.append({
                    "var_name": var_name,
                    "type": class_type
                })

    def generate_function_signature(self) -> str:
        """Generate the function signature based on collected input nodes.
        
        Returns:
            str: The function signature string.
        """
        params = []
        for param_name, info in self.input_nodes.items():
            default = f"={info['default']}" if info['default'] is not None else ""
            params.append(f"{param_name}{default}")
        
        return f"@spaces.GPU\ndef run_workflow({', '.join(params)}) -> Tuple[Any, ...]:"

    def generate_workflow(
        self,
        load_order: List,
    ) -> str:
        """Generate the execution code based on the load order.

        Args:
            load_order (List): A list of tuples representing the load order.

        Returns:
            str: Generated execution code as a string.
        """
        # Create the necessary data structures to hold imports and generated code
        import_statements, executed_variables = set(["NODE_CLASS_MAPPINGS"]), {}
        loader_code, loader_execution_code, special_functions_code, main_code = [], [], [], []
        
        # This dictionary will store the names of the objects that we have already initialized
        initialized_objects = {}

        # Collect FramerComfy input and output nodes
        self.collect_framer_nodes(load_order)

        # Create a mapping of FramerComfy input node IDs to their parameter names
        input_node_mapping = {}
        for param_name, info in self.input_nodes.items():
            for idx, data, _, _ in load_order:
                if f"{self.clean_variable_name(data['class_type'])}_{idx}" == info['var_name']:
                    input_node_mapping[idx] = param_name
                    break

        custom_nodes = False
        # Loop over each dictionary in the load order list
        for idx, data, is_special_function, is_loader_function in load_order:
            # Skip FramerComfy input nodes entirely
            if data["class_type"].startswith("FramerComfy") and "Input" in data["class_type"]:
                continue

            # Generate class definition and inputs from the data
            inputs, class_type = data["inputs"], data["class_type"]
            input_types = self.node_class_mappings[class_type].INPUT_TYPES()
            class_def = self.node_class_mappings[class_type]()

            # If required inputs are not present, skip the node as it will break the code if passed through to the script
            missing_required_variable = False
            if "required" in input_types.keys():
                for required in input_types["required"]:
                    if required not in inputs.keys():
                        missing_required_variable = True
            if missing_required_variable:
                continue

            # If the class hasn't been initialized yet, initialize it and generate the import statements
            if class_type not in initialized_objects:
                # No need to use preview image nodes since we are executing the script in a terminal
                if class_type == "PreviewImage":
                    continue

                class_type, import_statement, class_code = self.get_class_info(
                    class_type
                )
                initialized_objects[class_type] = self.clean_variable_name(class_type)
                if class_type in self.base_node_class_mappings.keys():
                    import_statements.add(import_statement)
                if class_type not in self.base_node_class_mappings.keys():
                    custom_nodes = True
                special_functions_code.append(class_code)

            # Get all possible parameters for class_def
            class_def_params = self.get_function_parameters(
                getattr(class_def, class_def.FUNCTION)
            )
            no_params = class_def_params is None

            # Remove any keyword arguments from **inputs if they are not in class_def_params
            inputs = {
                key: value
                for key, value in inputs.items()
                if no_params or key in class_def_params
            }
            # Deal with hidden variables
            if (
                "hidden" in input_types.keys()
                and "unique_id" in input_types["hidden"].keys()
            ):
                inputs["unique_id"] = random.randint(1, 2**64)
            elif class_def_params is not None:
                if "unique_id" in class_def_params:
                    inputs["unique_id"] = random.randint(1, 2**64)

            # Create executed variable and generate code
            executed_variables[idx] = f"{self.clean_variable_name(class_type)}_{idx}"
            inputs = self.update_inputs(inputs, executed_variables, input_node_mapping)

            if is_loader_function:
                loader_execution_code.append(
                    self.create_function_call_code(
                        initialized_objects[class_type],
                        class_def.FUNCTION,
                        executed_variables[idx],
                        True,  # Always unindented since it's outside main
                        **inputs,
                    )
                )
            elif is_special_function:
                special_functions_code.append(
                    self.create_function_call_code(
                        initialized_objects[class_type],
                        class_def.FUNCTION,
                        executed_variables[idx],
                        is_special_function,
                        **inputs,
                    )
                )
            else:
                main_code.append(
                    self.create_function_call_code(
                        initialized_objects[class_type],
                        class_def.FUNCTION,
                        executed_variables[idx],
                        is_special_function,
                        **inputs,
                    )
                )

        return self.assemble_python_code(
            import_statements,
            loader_code,
            loader_execution_code,
            special_functions_code,
            main_code,
            custom_nodes,
        )

    def create_function_call_code(
        self,
        obj_name: str,
        func: str,
        variable_name: str,
        is_special_function: bool,
        **kwargs,
    ) -> str:
        """Generate Python code for a function call.

        Args:
            obj_name (str): The name of the initialized object.
            func (str): The function to be called.
            variable_name (str): The name of the variable that the function result should be assigned to.
            is_special_function (bool): Determines the code indentation.
            **kwargs: The keyword arguments for the function.

        Returns:
            str: The generated Python code.
        """
        args = ", ".join(self.format_arg(key, value) for key, value in kwargs.items())

        # Generate the Python code
        code = f"{variable_name} = {obj_name}.{func}({args})\n"

        # If the code contains dependencies and is not a loader or encoder, indent the code because it will be placed inside
        # of a for loop
        if not is_special_function:
            code = f"\t{code}"

        return code

    def format_arg(self, key: str, value: any) -> str:
        """Formats arguments based on key and value.

        Args:
            key (str): Argument key.
            value (any): Argument value.

        Returns:
            str: Formatted argument as a string.
        """
        if key == "noise_seed" or key == "seed":
            return f"{key}=random.randint(1, 2**64)"
        elif isinstance(value, str):
            # Check if this value is one of our input parameters
            if value in self.input_nodes:
                return f"{key}={value}"
            value = value.replace("\n", "\\n").replace('"', "'")
            return f'{key}="{value}"'
        elif isinstance(value, dict) and "variable_name" in value:
            return f'{key}={value["variable_name"]}'
        return f"{key}={value}"

    def assemble_python_code(
        self,
        import_statements: set,
        loader_code: List[str],
        loader_execution_code: List[str],
        special_functions_code: List[str],
        main_code: List[str],
        custom_nodes=False,
    ) -> str:
        """Generates the final code string.

        Args:
            import_statements (set): A set of unique import statements.
            loader_code (List[str]): A list of loader functions code strings.
            loader_execution_code (List[str]): A list of loader function execution code strings.
            special_functions_code (List[str]): A list of special functions code strings.
            main_code (List[str]): A list of code strings.
            custom_nodes (bool): Whether to include custom nodes in the code.

        Returns:
            str: Generated final code as a string.
        """
        # Get the source code of the utils functions as a string
        func_strings = []
        for func in [
            get_value_at_index,
            find_path,
            add_comfyui_directory_to_sys_path,
            add_extra_model_paths
        ]:
            func_strings.append(f"\n{inspect.getsource(func)}")

        # Generate model download code if we have workflow_models
        model_download_code = []
        if self.workflow_models:
            for model in self.workflow_models:
                model_download_code.append(
                    f'hf_hub_download(repo_id="{model["repo_id"]}", filename="{model["file_name"]}", local_dir="{model["model_local_path"]}", token=hf_token)'
                )
            model_download_code = ["\n# Download required models from huggingface"] + ['hf_token = os.environ.get("HF_TOKEN")'] + model_download_code + [""]
        
        # Define static import statements required for the script
        static_imports = (
            [
                "import os",
                "import random",
                "import sys",
                "from typing import Sequence, Mapping, Any, Union, Tuple",
                "import torch",
                "from PIL import Image",
                "import spaces",
                "import gradio as gr",
                "from huggingface_hub import hf_hub_download",
                "from comfy import model_management",
            ]
            + model_download_code
            + func_strings
            + ["\n\nadd_comfyui_directory_to_sys_path()\nadd_extra_model_paths()\n"]
        )

        # Check if custom nodes should be included
        if custom_nodes:
            static_imports.append(f"\n{inspect.getsource(import_custom_nodes)}\n")
            custom_nodes = "import_custom_nodes()\n"
        else:
            custom_nodes = ""
        # Create import statements for node classes
        imports_code = [
            f"from nodes import {', '.join([class_name for class_name in import_statements])}\n"
            + f"{custom_nodes}"
        ]
        # Place loader class initialization first
        loader_init = "\n".join(loader_code) + "\n" if loader_code else ""
        
        # Place loader execution code after initialization
        loader_execution = "\n".join(loader_execution_code) + "\n" if loader_execution_code else ""
        
        # Extract variable names from loader execution code to build model_loaders list
        model_vars = []
        if loader_execution_code:
            for line in loader_execution_code:
                if "=" in line:
                    var_name = line.split("=")[0].strip()
                    model_vars.append(var_name)
        
        # Add model management code if we have loaders
        if model_vars:
            model_management_code = f"\nmodel_loaders = [{', '.join(model_vars)}]\n\n"
            model_management_code += "model_management.load_models_gpu([\n"
            model_management_code += "    loader[0].patcher if hasattr(loader[0], 'patcher') else loader[0] for loader in model_loaders\n"
            model_management_code += "])\n\n"
        else:
            model_management_code = ""
        
        # Place other special functions after loaders
        initialization_code = "\n".join(special_functions_code) + "\n" if special_functions_code else ""

        # Generate the workflow function
        workflow_code = []
        
        # Add main workflow code
        workflow_code.extend([f"\twith torch.inference_mode():", f"\t\t" + "\n\t\t".join(main_code)])
        
        # Add return statement for outputs
        if self.output_nodes:
            # Process each output node
            output_vars = []
            for output in self.output_nodes:
                var_name = output["var_name"]
                if "SaveImage" in output["type"]:
                    # For image outputs, extract the path
                    path_var = f"{var_name}_path"
                    workflow_code.append(f"\t{path_var} = \"output/\" + {var_name}['ui']['images'][0]['filename']")
                    output_vars.append(path_var)
                else:
                    output_vars.append(var_name)
            
            # Generate return statement
            if len(output_vars) == 1:
                workflow_code.append(f"\treturn {output_vars[0]}")
            else:
                workflow_code.append(f"\treturn ({', '.join(output_vars)})")
        else:
            workflow_code.append("\treturn None")

        # Generate Gradio components
        gradio_components = []
        input_components = []
        output_components = []

        # Create output components first (outside the block)
        output_declarations = []
        for output in self.output_nodes:
            var_name = output["var_name"]
            output_name = var_name.replace("framercomfysave", "").replace("node_", "")
            if "SaveImage" in output["type"]:
                output_declarations.append(
                    f"{output_name}_output = gr.Image(label=\"{output.get('label', 'Generated ' + output_name.title())}\")"
                )
                output_components.append(f"{output_name}_output")

        # Generate Gradio app code
        gradio_code = [
            "# Create Gradio interface",
            *output_declarations,
            "",
            "with gr.Blocks() as app:",
            "\twith gr.Row():",
            "\t\twith gr.Column():"
        ]

        # Add input components inside the block
        input_declarations = []
        for param_name, info in self.input_nodes.items():
            if info["type"] == "stringnode":
                input_declarations.append(
                    f"\t\t\t{param_name}_input = gr.Textbox("
                    f"label=\"{info.get('label', param_name.title())}\", "
                    f"value=\"{info['default']}\" if \"{info['default']}\" else None, "
                    f"placeholder=f\"Enter {param_name} here...\")"
                )
                input_components.append(f"{param_name}_input")
            elif info["type"] == "image":
                input_declarations.append(
                    f"\t\t\t{param_name}_input = gr.Image("
                    f"label=\"{info.get('label', param_name.title())}\", "
                    f"type=\"filepath\")"
                )
                input_components.append(f"{param_name}_input")
            elif info["type"] == "float" or info["type"] == "integer":
                min_val = f", minimum={info['min']}" if info.get('min') is not None else ""
                max_val = f", maximum={info['max']}" if info.get('max') is not None else ""
                step = f", step={info['step']}" if info.get('step') is not None else ""
                input_declarations.append(
                    f"\t\t\t{param_name}_input = gr.{'Number' if info['type'] == 'float' else 'Slider'}("
                    f"label=\"{info.get('label', param_name.title())}\""
                    f"{min_val}{max_val}{step}, "
                    f"value={info['default'] if info['default'] is not None else 'None'})"
                )
                input_components.append(f"{param_name}_input")

        gradio_code.extend([
            *input_declarations,
            "\t\t\tgenerate_btn = gr.Button(\"Generate\")",
            "\t\twith gr.Column():",
            *[f"\t\t\t{comp}.render()" for comp in output_components],
            "\tgenerate_btn.click(",
            "\t\tfn=run_workflow,",
            f"\t\tinputs=[{', '.join(input_components)}],",
            f"\t\toutputs=[{', '.join(output_components)}]",
            "\t)",
            "",
            'if __name__ == "__main__":',
            "\tapp.launch(share=True)"
        ])

        # Concatenate all parts to form the final code
        final_code = "\n".join(
            static_imports 
            # + model_download_code
            + imports_code
            + ["", initialization_code, loader_init, loader_execution, model_management_code, 
               self.generate_function_signature(), "\n".join(workflow_code),
               "", "\n".join(gradio_code)]
        )

        # Format the final code according to PEP 8 using the Black library
        final_code = black.format_str(final_code, mode=black.Mode())

        return final_code

    def get_class_info(self, class_type: str) -> Tuple[str, str, str]:
        """Generates and returns necessary information about class type.

        Args:
            class_type (str): Class type.

        Returns:
            Tuple[str, str, str]: Updated class type, import statement string, class initialization code.
        """
        import_statement = class_type
        variable_name = self.clean_variable_name(class_type)
        if class_type in self.base_node_class_mappings.keys():
            class_code = f"{variable_name} = {class_type.strip()}()"
        else:
            class_code = f'{variable_name} = NODE_CLASS_MAPPINGS["{class_type}"]()'

        return class_type, import_statement, class_code

    @staticmethod
    def clean_variable_name(class_type: str) -> str:
        """
        Remove any characters from variable name that could cause errors running the Python script.

        Args:
            class_type (str): Class type.

        Returns:
            str: Cleaned variable name with no special characters or spaces
        """
        # Convert to lowercase and replace spaces with underscores
        clean_name = class_type.lower().strip().replace("-", "_").replace(" ", "_")

        # Remove characters that are not letters, numbers, or underscores
        clean_name = re.sub(r"[^a-z0-9_]", "", clean_name)

        # Ensure that it doesn't start with a number
        if clean_name[0].isdigit():
            clean_name = "_" + clean_name

        return clean_name

    def get_function_parameters(self, func: Callable) -> List:
        """Get the names of a function's parameters.

        Args:
            func (Callable): The function whose parameters we want to inspect.

        Returns:
            List: A list containing the names of the function's parameters.
        """
        signature = inspect.signature(func)
        parameters = {
            name: param.default if param.default != param.empty else None
            for name, param in signature.parameters.items()
        }
        catch_all = any(
            param.kind == inspect.Parameter.VAR_KEYWORD
            for param in signature.parameters.values()
        )
        return list(parameters.keys()) if not catch_all else None

    def update_inputs(self, inputs: Dict, executed_variables: Dict, input_node_mapping: Dict) -> Dict:
        """Update inputs based on the executed variables and input node mapping.

        Args:
            inputs (Dict): Inputs dictionary to update.
            executed_variables (Dict): Dictionary storing executed variable names.
            input_node_mapping (Dict): Mapping of input node IDs to parameter names.

        Returns:
            Dict: Updated inputs dictionary.
        """
        for key in inputs.keys():
            if isinstance(inputs[key], list):
                node_id = inputs[key][0]
                if node_id in input_node_mapping:
                    # Directly use the parameter name instead of get_value_at_index
                    inputs[key] = input_node_mapping[node_id]
                elif node_id in executed_variables:
                    inputs[key] = {
                        "variable_name": f"get_value_at_index({executed_variables[node_id]}, {inputs[key][1]})"
                    }
        return inputs


class ComfyUItoPython:
    """Main workflow to generate Python code from a workflow_api.json file.

    Attributes:
        input_file (str): Path to the input JSON file.
        output_file (str): Path to the output Python file.
        node_class_mappings (Dict): Mappings of node classes.
        base_node_class_mappings (Dict): Base mappings of node classes.
    """

    def __init__(
        self,
        workflow: str = "",
        input_file: str = "",
        workflow_models: str | List = "",
        output_file: str | TextIO = "",
        node_class_mappings: Dict = NODE_CLASS_MAPPINGS,
        needs_init_custom_nodes: bool = False,
    ):
        """Initialize the ComfyUItoPython class with the given parameters. Exactly one of workflow or input_file must be specified.
        Args:
            workflow (str): The workflow's JSON.
            input_file (str): Path to the input JSON file.
            workflow_models (str | List): JSON string or list containing models to download.
            output_file (str | TextIO): Path to the output file or a file-like object.
            node_class_mappings (Dict): Mappings of node classes. Defaults to NODE_CLASS_MAPPINGS.
            needs_init_custom_nodes (bool): Whether to initialize custom nodes. Defaults to False.
        """
        if input_file and workflow:
            raise ValueError("Can't provide both input_file and workflow")
        elif not input_file and not workflow:
            raise ValueError("Needs input_file or workflow")

        if not output_file:
            raise ValueError("Needs output_file")

        self.workflow = workflow
        self.input_file = input_file
        self.output_file = output_file
        self.node_class_mappings = node_class_mappings
        self.needs_init_custom_nodes = needs_init_custom_nodes
        
        # Handle workflow_models that can be either a JSON string or a list
        if isinstance(workflow_models, str):
            self.workflow_models = json.loads(workflow_models) if workflow_models else []
        else:
            self.workflow_models = workflow_models or []

        self.base_node_class_mappings = copy.deepcopy(self.node_class_mappings)
        # self.execute()

    def execute(self):
        """Execute the main workflow to generate Python code.

        Returns:
            None
        """
        # Step 1: Import all custom nodes if we need to
        if self.needs_init_custom_nodes:
            import_custom_nodes()
        else:
            # If they're already imported, we don't know which nodes are custom nodes, so we need to import all of them
            self.base_node_class_mappings = {}

        # Step 2: Read JSON data from the input file
        if self.input_file:
            data = FileHandler.read_json_file(self.input_file)
        else:
            data = json.loads(self.workflow)

        # Step 3: Determine the load order
        load_order_determiner = LoadOrderDeterminer(data, self.node_class_mappings)
        load_order = load_order_determiner.determine_load_order()

        # Step 4: Generate the workflow code
        code_generator = CodeGenerator(self.node_class_mappings, self.base_node_class_mappings, self.workflow_models)

        generated_code = code_generator.generate_workflow(load_order)

        # Step 5: Write the generated code to a file
        FileHandler.write_code_to_file(self.output_file, generated_code)
        return generated_code