Quick rename to better identify extension util file.

Hotfixes for latest ComfyUI release

README.md

@@ -1,6 +1,6 @@
 ## ComfyUI-to-Python-Extension
 
-The `ComfyUI-to-Python-Extension` is a powerful tool that translates [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflows into executable Python code. Designed to bridge the gap between ComfyUI's visual interface and Python's programming environment, this script facilitates the seamless transition from design to code execution. Whether you're a data scientist, a software developer, or an AI enthusiast, this tool streamlines the process of implementing ComfyUI workflows in Python. The output makes it easy to queue a large amount of images for generation and provides a base script to easily modify for experimination.
+The `ComfyUI-to-Python-Extension` is a powerful tool that translates [ComfyUI](https://github.com/comfyanonymous/ComfyUI) workflows into executable Python code. Designed to bridge the gap between ComfyUI's visual interface and Python's programming environment, this script facilitates the seamless transition from design to code execution. Whether you're a data scientist, a software developer, or an AI enthusiast, this tool streamlines the process of implementing ComfyUI workflows in Python.
 
 **Convert this:**
 
@@ -109,6 +109,16 @@ def main():
 if __name__ == "__main__":
     main()
 ```
+## Potential Use Cases
+- Streamlining the process for creating a lean app or pipeline deployment that uses a ComfyUI workflow
+- Creating programmatic experiments for various prompt/parameter values
+- Creating large queues for image generation (For example, you could adjust the script to generate 1000 images without clicking ctrl+enter 1000 times)
+- Easily expanding or iterating on your architecture in Python once a foundational workflow is in place in the GUI
+
+## V1.0.0 Release Notes
+- **Use all the custom nodes!**
+    - Custom nodes are now supported. If you run into any issues with code execution, first ensure that the each node works as expected in the GUI. If it works in the GUI, but not in the generated script, please submit an issue.
+
 
 ## Usage
 
@@ -159,13 +169,13 @@ if __name__ == "__main__":
 
 6. Move the downloaded .json workflow file to your `ComfyUI/ComfyUI-to-Python-Extension` folder
 
-7. If needed, update the `input` variable at the bottom of `comfyui_to_python.py` to match the name of your .json workflow file. By default, the script will look for a file called `workflow_api.json`. You can also update the `queue_size` variable to your desired number of images that you want to generate in a single script execution. By default, the scripts will generate 10 images.
+7. If needed, update the `input_file` and `output_file` variables at the bottom of `comfyui_to_python.py` to match the name of your .json workflow file and desired .py file name. By default, the script will look for a file called `workflow_api.json`. You can also update the `queue_size` variable to your desired number of images that you want to generate in a single script execution. By default, the scripts will generate 10 images.
 
 8. Run the script:
    ```bash
    python comfyui_to_python.py
    ```
 
-9. After running `comfyui_to_python.py`, a new .py file will be created in the current working directory that contains the same name as the `input` variable. If you made no changes, look for `workflow_api.py`.
+9. After running `comfyui_to_python.py`, a new .py file will be created in the current working directory. If you made no changes, look for `workflow_api.py`.
 
 10. Now you can execute the newly created .py file to generate images without launching a server.

comfyui_to_python.py

@@ -1,91 +1,139 @@
+import copy
 import glob
 import inspect
 import json
-import logging
 import os
+import random
 import sys
+import re
+from typing import Dict, List, Any, Callable, Tuple
 
 import black
 
-from typing import Dict, List, Any, Callable
 
-sys.path.append('../')
+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,
+)
 
+sys.path.append("../")
 from nodes import NODE_CLASS_MAPPINGS
 
 
-logging.basicConfig(level=logging.INFO)
+class FileHandler:
+    """Handles reading and writing files.
 
-
-def read_json_file(file_path: str) -> 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.
+    This class provides methods to read JSON data from an input file and write code to an output file.
     """
 
-    try:
-        with open(file_path, 'r') as file:
-            data = json.load(file)
-        return data
+    @staticmethod
+    def read_json_file(file_path: str) -> dict:
+        """
+        Reads a JSON file and returns its contents as a dictionary.
 
-    except FileNotFoundError:
-        # Get the directory from the file_path
+        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.
+        """
+
+        try:
+            with open(file_path, "r") as file:
+                data = json.load(file)
+            return data
+
+        except FileNotFoundError:
+            # Get the directory from the file_path
+            directory = os.path.dirname(file_path)
+
+            # If the directory is an empty string (which means file is in the current directory),
+            # get the current working directory
+            if not directory:
+                directory = os.getcwd()
+
+            # Find all JSON files in the directory
+            json_files = glob.glob(f"{directory}/*.json")
+
+            # Format the list of JSON files as a string
+            json_files_str = "\n".join(json_files)
+
+            raise FileNotFoundError(
+                f"\n\nFile not found: {file_path}. JSON files in the directory:\n{json_files_str}"
+            )
+
+        except json.JSONDecodeError:
+            raise ValueError(f"Invalid JSON format in file: {file_path}")
+
+    @staticmethod
+    def write_code_to_file(file_path: str, 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
+        """
+        # Extract directory from the filename
         directory = os.path.dirname(file_path)
 
-        # If the directory is an empty string (which means file is in the current directory),
-        # get the current working directory
-        if not directory:
-            directory = os.getcwd()
+        # If the directory does not exist, create it
+        if directory and not os.path.exists(directory):
+            os.makedirs(directory)
 
-        # Find all JSON files in the directory
-        json_files = glob.glob(f"{directory}/*.json")
+        # Save the code to a .py file
+        with open(file_path, "w") as file:
+            file.write(code)
 
-        # Format the list of JSON files as a string
-        json_files_str = "\n".join(json_files)
 
-        raise FileNotFoundError(f"\n\nFile not found: {file_path}. JSON files in the directory:\n{json_files_str}")
+class LoadOrderDeterminer:
+    """Determine the load order of each key in the provided dictionary.
 
-    except json.JSONDecodeError:
-        raise ValueError(f"Invalid JSON format in file: {file_path}")
-    
+    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.
 
-def determine_load_order(data: Dict) -> List:
-    """
-    Determine the load order of each key in the provided dictionary. This code will place the 
-    nodes without node dependencies first, then ensure that any node whose result is used
-    in another node will be added to the list in the order it should be executed.
-
-    Args:
-        data (Dict): 
-            The dictionary for which to determine the load order.
-
-    Returns:
-        List: 
-            A list of tuples where each tuple contains a key, its corresponding dictionary, 
-            and a boolean indicating whether or not the function is dependent on the output of
-            a previous function, ordered by load order.
+    Attributes:
+        data (Dict): The dictionary for which to determine the load order.
+        node_class_mappings (Dict): Mappings of node classes.
     """
 
-    # Create a dictionary to keep track of visited nodes.
-    visited = {}
-    # Create a list to store the load order for functions
-    load_order = []
-    # Boolean to indicate whether or not the class is a loader class that should not be
-    # reloaded during every loop
-    is_loader = False
+    def __init__(self, data: Dict, node_class_mappings: Dict):
+        """Initialize the LoadOrderDeterminer with the given data and node class mappings.
 
-    def dfs(key: str) -> None:
+        Args:
+            data (Dict): The dictionary for which to determine the load order.
+            node_class_mappings (Dict): Mappings of node classes.
         """
-        Depth-First Search function.
+        self.data = data
+        self.node_class_mappings = node_class_mappings
+        self.visited = {}
+        self.load_order = []
+        self.is_special_function = False
+
+    def determine_load_order(self) -> List[Tuple[str, Dict, bool]]:
+        """Determine the load order for the given data.
+
+        Returns:
+            List[Tuple[str, Dict, bool]]: A list of tuples representing the load order.
+        """
+        self._load_special_functions_first()
+        self.is_special_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.
@@ -94,240 +142,423 @@ def determine_load_order(data: Dict) -> List:
             None
         """
         # Mark the node as visited.
-        visited[key] = True
-        inputs = data[key]['inputs']
-
+        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 (which should be a key) 
-            # has not been visited yet, then recursively apply dfs on the dependency.
-            if isinstance(val, list) and val[0] not in visited:
-                dfs(val[0])  
-
+            # 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.
-        load_order.append((key, data[key], is_loader))  
+        self.load_order.append((key, self.data[key], self.is_special_function))
 
-    # Load Loader keys first
-    for key in data:
-        class_def = NODE_CLASS_MAPPINGS[data[key]['class_type']]()
-        if class_def.CATEGORY == 'loaders' or class_def.FUNCTION in ['encode'] or not any(isinstance(val, list) for val in data[key]['inputs'].values()):
-            is_loader = True
-            # If the key has not been visited, perform a DFS from that key.
-            if key not in visited:
-                dfs(key)
+    def _load_special_functions_first(self) -> None:
+        """Load functions without dependencies, loaderes, and encoders first.
 
-    # Reset is_loader bool
-    is_loader = False
-    # Loop over each key in the data.
-    for key in data:
-        # If the key has not been visited, perform a DFS from that key.
-        if key not in visited:
-            dfs(key)
-
-    return load_order
+        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.
+            if (
+                class_def.CATEGORY == "loaders"
+                or class_def.FUNCTION in ["encode"]
+                or not any(
+                    isinstance(val, list) for val in self.data[key]["inputs"].values()
+                )
+            ):
+                self.is_special_function = True
+                # If the key has not been visited, perform a DFS from that key.
+                if key not in self.visited:
+                    self._dfs(key)
 
 
-def create_function_call_code(obj_name: str, func: str, variable_name: str, is_loader: bool, **kwargs) -> str:
-    """
-    This function generates Python code for a function call.
+class CodeGenerator:
+    """Generates Python code for a workflow based on the load order.
 
-    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_loader (bool): Determines the code indentation.
-        **kwargs: The keyword arguments for the function.
-
-    Returns:
-        str: The generated Python code.
+    Attributes:
+        node_class_mappings (Dict): Mappings of node classes.
+        base_node_class_mappings (Dict): Base mappings of node classes.
     """
 
-    def format_arg(key: str, value: any) -> str:
-        """Formats arguments based on key and value."""
-        if key == 'noise_seed':
-            return f'{key}=random.randint(1, 2**64)'
+    def __init__(self, node_class_mappings: Dict, base_node_class_mappings: Dict):
+        """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.
+        """
+        self.node_class_mappings = node_class_mappings
+        self.base_node_class_mappings = base_node_class_mappings
+
+    def generate_workflow(
+        self,
+        load_order: List,
+        filename: str = "generated_code_workflow.py",
+        queue_size: int = 10,
+    ) -> str:
+        """Generate the execution code based on the load order.
+
+        Args:
+            load_order (List): A list of tuples representing the load order.
+            filename (str): The name of the Python file to which the code should be saved.
+                Defaults to 'generated_code_workflow.py'.
+            queue_size (int): The number of photos that will be created by the script.
+
+        Returns:
+            str: Generated execution code as a string.
+        """
+        # Create the necessary data structures to hold imports and generated code
+        import_statements, executed_variables, special_functions_code, code = (
+            set(["NODE_CLASS_MAPPINGS"]),
+            {},
+            [],
+            [],
+        )
+        # This dictionary will store the names of the objects that we have already initialized
+        initialized_objects = {}
+
+        custom_nodes = False
+        # Loop over each dictionary in the load order list
+        for idx, data, is_special_function in load_order:
+
+            # Generate class definition and inputs from the data
+            inputs, class_type = data["inputs"], data["class_type"]
+            class_def = self.node_class_mappings[class_type]()
+
+            # 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)
+            )
+
+            # Remove any keyword arguments from **inputs if they are not in class_def_params
+            inputs = {
+                key: value for key, value in inputs.items() if key in class_def_params
+            }
+            # Deal with hidden variables
+            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)
+
+            if 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:
+                code.append(
+                    self.create_function_call_code(
+                        initialized_objects[class_type],
+                        class_def.FUNCTION,
+                        executed_variables[idx],
+                        is_special_function,
+                        **inputs,
+                    )
+                )
+
+        # Generate final code by combining imports and code, and wrap them in a main function
+        final_code = self.assemble_python_code(
+            import_statements, special_functions_code, code, queue_size, custom_nodes
+        )
+
+        return final_code
+
+    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):
-            value = value.replace("\n", "\\n")
+            value = value.replace("\n", "\\n").replace('"', "'")
             return f'{key}="{value}"'
-        elif isinstance(value, dict) and 'variable_name' in value:
+        elif isinstance(value, dict) and "variable_name" in value:
             return f'{key}={value["variable_name"]}'
-        return f'{key}={value}'
+        return f"{key}={value}"
 
-    args = ', '.join(format_arg(key, value) for key, value in kwargs.items())
+    def assemble_python_code(
+        self,
+        import_statements: set,
+        speical_functions_code: List[str],
+        code: List[str],
+        queue_size: int,
+        custom_nodes=False,
+    ) -> str:
+        """Generates the final code string.
 
-    # Generate the Python code
-    code = f'{variable_name} = {obj_name}.{func}({args})\n'
+        Args:
+            import_statements (set): A set of unique import statements.
+            speical_functions_code (List[str]): A list of special functions code strings.
+            code (List[str]): A list of code strings.
+            queue_size (int): Number of photos that will be generated by the script.
+            custom_nodes (bool): Whether to include custom nodes in the code.
 
-    # If the code contains dependencies, indent the code because it will be placed inside
-    # of a for loop
-    if not is_loader:
-        code = f'\t{code}'
-
-    return code
-
-
-def update_inputs(inputs: Dict, executed_variables: Dict) -> Dict:
-    """
-    Update inputs based on the executed variables.
-
-    Args:
-        inputs (Dict): Inputs dictionary to update.
-        executed_variables (Dict): Dictionary storing executed variable names.
-
-    Returns:
-        Dict: Updated inputs dictionary.
-    """
-    for key in inputs.keys():
-        if isinstance(inputs[key], list) and inputs[key][0] in executed_variables.keys():
-            inputs[key] = {'variable_name': f"{executed_variables[inputs[key][0]]}[{inputs[key][1]}]"}
-    return inputs
-
-
-def get_class_info(class_type: str) -> (str, str, str):
-    """
-    Generates and returns necessary information about class type.
-
-    Args:
-        class_type (str): Class type
-
-    Returns:
-        class_type (str): Updated class type
-        import_statement (str): Import statement string
-        class_code (str): Class initialization code
-    """
-    import_statement = class_type
-    class_code = f'{class_type.lower()} = {class_type}()'
-
-    return class_type, import_statement, class_code
-
-
-def assemble_python_code(import_statements: set, loader_code: List[str], code: List[str], queue_size: int) -> str:
-    """
-    Generates final code string.
-
-    Args:
-        import_statements (set): A set of unique import statements
-        code (List[str]): A list of code strings
-        queue_size (int): Number of photos that will be generated by the script.
-
-    Returns:
-        final_code (str): Generated final code as a string
-    """
-    static_imports = ['import random', 'import torch']
-    imports_code = [f"from nodes import {', '.join([class_name for class_name in import_statements])}" ]
-    main_function_code = f"def main():\n\t" + 'with torch.inference_mode():\n\t\t' + '\n\t\t'.join(loader_code) + f'\n\n\t\tfor q in range({queue_size}):\n\t\t' + '\n\t\t'.join(code)
-    final_code = '\n'.join(static_imports + ['import sys\nsys.path.append("../")'] + imports_code + ['', main_function_code, '', 'if __name__ == "__main__":', '\tmain()'])
-    final_code = black.format_str(final_code, mode=black.Mode())
-
-    return final_code
-
-
-def write_code_to_file(filename: str, code: str) -> None:
-    """
-    Writes given code to a .py file. If the directory does not exist, it creates it.
-
-    Args:
-        filename (str): The name of the Python file to save the code to.
-        code (str): The code to save.
-    """
-
-    # Extract directory from the filename
-    directory = os.path.dirname(filename)
-
-    # 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(filename, 'w') as file:
-        file.write(code)
-
-
-def get_function_parameters(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()}
-    return list(parameters.keys())
-
-
-def generate_workflow(load_order: List, filename: str = 'generated_code_workflow.py', queue_size: int = 10) -> str:
-    """
-    Generate the execution code based on the load order.
-
-    Args:
-        load_order (List): A list of tuples representing the load order.
-        filename (str): The name of the Python file to which the code should be saved.
-            Defaults to 'generated_code_workflow.py'.
-        queue_size (int): The number of photos that will be created by the script.
-
-    Returns:
-        str: Generated execution code as a string.
-    """
-
-    # Create the necessary data structures to hold imports and generated code
-    import_statements, executed_variables, loader_code, code = set(), {}, [], []
-    # This dictionary will store the names of the objects that we have already initialized
-    initialized_objects = {}
-
-    # Loop over each dictionary in the load order list
-    for idx, data, is_loader in load_order:
-
-        # Generate class definition and inputs from the data
-        inputs, class_type = data['inputs'], data['class_type']
-        class_def = NODE_CLASS_MAPPINGS[class_type]()
-
-        # If the class hasn't been initialized yet, initialize it and generate the import statements
-        if class_type not in initialized_objects:
-            class_type, import_statement, class_code = get_class_info(class_type)
-            initialized_objects[class_type] = class_type.lower()
-            import_statements.add(import_statement)
-            loader_code.append(class_code)
-
-        # Get all possible parameters for class_def
-        class_def_params = get_function_parameters(getattr(class_def, class_def.FUNCTION))
-
-        # Remove any keyword arguments from **inputs if they are not in class_def_params
-        inputs = {key: value for key, value in inputs.items() if key in class_def_params}
-
-        # Create executed variable and generate code
-        executed_variables[idx] = f'{class_type.lower()}_{idx}'
-        inputs = update_inputs(inputs, executed_variables)
-
-        if is_loader:
-            loader_code.append(create_function_call_code(initialized_objects[class_type], class_def.FUNCTION, executed_variables[idx], is_loader, **inputs))
+        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)}")
+        # Define static import statements required for the script
+        static_imports = (
+            [
+                "import os",
+                "import random",
+                "import sys",
+                "from typing import Sequence, Mapping, Any, Union",
+                "import torch",
+            ]
+            + 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\t"
         else:
-            code.append(create_function_call_code(initialized_objects[class_type], class_def.FUNCTION, executed_variables[idx], is_loader, **inputs))
+            custom_nodes = ""
+        # Create import statements for node classes
+        imports_code = [
+            f"from nodes import {', '.join([class_name for class_name in import_statements])}"
+        ]
+        # Assemble the main function code, including custom nodes if applicable
+        main_function_code = (
+            "def main():\n\t"
+            + f"{custom_nodes}with torch.inference_mode():\n\t\t"
+            + "\n\t\t".join(speical_functions_code)
+            + f"\n\n\t\tfor q in range({queue_size}):\n\t\t"
+            + "\n\t\t".join(code)
+        )
+        # Concatenate all parts to form the final code
+        final_code = "\n".join(
+            static_imports
+            + imports_code
+            + ["", main_function_code, "", 'if __name__ == "__main__":', "\tmain()"]
+        )
+        # Format the final code according to PEP 8 using the Black library
+        final_code = black.format_str(final_code, mode=black.Mode())
 
-    # Generate final code by combining imports and code, and wrap them in a main function
-    final_code = assemble_python_code(import_statements, loader_code, code, queue_size)
+        return final_code
 
-    # Save the code to a .py file
-    write_code_to_file(filename, final_code)
+    def get_class_info(self, class_type: str) -> Tuple[str, str, str]:
+        """Generates and returns necessary information about class type.
 
-    return final_code
+        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()
+        }
+        return list(parameters.keys())
+
+    def update_inputs(self, inputs: Dict, executed_variables: Dict) -> Dict:
+        """Update inputs based on the executed variables.
+
+        Args:
+            inputs (Dict): Inputs dictionary to update.
+            executed_variables (Dict): Dictionary storing executed variable names.
+
+        Returns:
+            Dict: Updated inputs dictionary.
+        """
+        for key in inputs.keys():
+            if (
+                isinstance(inputs[key], list)
+                and inputs[key][0] in executed_variables.keys()
+            ):
+                inputs[key] = {
+                    "variable_name": f"get_value_at_index({executed_variables[inputs[key][0]]}, {inputs[key][1]})"
+                }
+        return inputs
 
 
-def main(input, queue_size=10):
+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.
+        queue_size (int): The number of photos that will be created by the script.
+        node_class_mappings (Dict): Mappings of node classes.
+        base_node_class_mappings (Dict): Base mappings of node classes.
     """
-    Main function to be executed.
-    """
-    # Load JSON data from the input file
-    prompt = read_json_file(input)
-    load_order = determine_load_order(prompt)
-    output_file = input.replace('.json', '.py')
-    code = generate_workflow(load_order, filename=output_file, queue_size=queue_size)
-    logging.info(code)
+
+    def __init__(
+        self,
+        input_file: str,
+        output_file: str,
+        queue_size: int = 10,
+        node_class_mappings: Dict = NODE_CLASS_MAPPINGS,
+    ):
+        """Initialize the ComfyUItoPython class with the given parameters.
+
+        Args:
+            input_file (str): Path to the input JSON file.
+            output_file (str): Path to the output Python file.
+            queue_size (int): The number of times a workflow will be executed by the script. Defaults to 10.
+            node_class_mappings (Dict): Mappings of node classes. Defaults to NODE_CLASS_MAPPINGS.
+        """
+        self.input_file = input_file
+        self.output_file = output_file
+        self.queue_size = queue_size
+        self.node_class_mappings = node_class_mappings
+        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
+        import_custom_nodes()
+
+        # Step 2: Read JSON data from the input file
+        data = FileHandler.read_json_file(self.input_file)
+
+        # 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
+        )
+        generated_code = code_generator.generate_workflow(
+            load_order, filename=self.output_file, queue_size=self.queue_size
+        )
+
+        # Step 5: Write the generated code to a file
+        FileHandler.write_code_to_file(self.output_file, generated_code)
+
+        print(f"Code successfully generated and written to {self.output_file}")
 
 
-if __name__ == '__main__':
-    input = 'workflow_api.json'
+if __name__ == "__main__":
+    # Update class parameters here
+    input_file = "workflow_api.json"
+    output_file = "workflow_api.py"
     queue_size = 10
-    main(input, queue_size)
+
+    # Convert ComfyUI workflow to Python
+    ComfyUItoPython(
+        input_file=input_file, output_file=output_file, queue_size=queue_size
+    )

comfyui_to_python_utils.py

@@ -0,0 +1,108 @@
+import os
+from typing import Sequence, Mapping, Any, Union
+import sys
+
+sys.path.append("../")
+
+
+def import_custom_nodes() -> None:
+    """Find all custom nodes in the custom_nodes folder and add those node objects to NODE_CLASS_MAPPINGS
+
+    This function sets up a new asyncio event loop, initializes the PromptServer,
+    creates a PromptQueue, and initializes the custom nodes.
+    """
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    import server
+
+    # Creating a new event loop and setting it as the default loop
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+
+    # Creating an instance of PromptServer with the loop
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+
+    # Initializing custom nodes
+    init_extra_nodes()
+
+
+def find_path(name: str, path: str = None) -> str:
+    """
+    Recursively looks at parent folders starting from the given path until it finds the given name.
+    Returns the path as a Path object if found, or None otherwise.
+    """
+    # If no path is given, use the current working directory
+    if path is None:
+        path = os.getcwd()
+
+    # Check if the current directory contains the name
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+
+    # Get the parent directory
+    parent_directory = os.path.dirname(path)
+
+    # If the parent directory is the same as the current directory, we've reached the root and stop the search
+    if parent_directory == path:
+        return None
+
+    # Recursively call the function with the parent directory
+    return find_path(name, parent_directory)
+
+
+def add_comfyui_directory_to_sys_path() -> None:
+    """
+    Add 'ComfyUI' to the sys.path
+    """
+    comfyui_path = find_path("ComfyUI")
+    if comfyui_path is not None and os.path.isdir(comfyui_path):
+        sys.path.append(comfyui_path)
+        print(f"'{comfyui_path}' added to sys.path")
+
+
+def add_extra_model_paths() -> None:
+    """
+    Parse the optional extra_model_paths.yaml file and add the parsed paths to the sys.path.
+    """
+    try:
+        from main import load_extra_path_config
+    except ImportError:
+        print(
+            "Could not import load_extra_path_config from main.py. Looking in utils.extra_config instead."
+        )
+        from utils.extra_config import load_extra_path_config
+
+    extra_model_paths = find_path("extra_model_paths.yaml")
+
+    if extra_model_paths is not None:
+        load_extra_path_config(extra_model_paths)
+    else:
+        print("Could not find the extra_model_paths config file.")
+
+
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    """Returns the value at the given index of a sequence or mapping.
+
+    If the object is a sequence (like list or string), returns the value at the given index.
+    If the object is a mapping (like a dictionary), returns the value at the index-th key.
+
+    Some return a dictionary, in these cases, we look for the "results" key
+
+    Args:
+        obj (Union[Sequence, Mapping]): The object to retrieve the value from.
+        index (int): The index of the value to retrieve.
+
+    Returns:
+        Any: The value at the given index.
+
+    Raises:
+        IndexError: If the index is out of bounds for the object and the object is not a mapping.
+    """
+    try:
+        return obj[index]
+    except KeyError:
+        return obj["result"][index]