from __future__ import annotations

from typing import Any, Callable

from gradio.components.base import FormComponent
from gradio.events import Events
from ase import Atoms, Atom
from ase.io import read, write
from ase.spacegroup import get_spacegroup
import io
import json
import numpy as np
import random
import string

def generate_random_string(length):
    characters = string.ascii_letters + string.digits
    return ''.join(random.choices(characters, k=length))
from gradio.events import Dependency

class MaterialViewer(FormComponent):
    """
    Creates a very simple textbox for user to enter string input or display string output.
    """

    EVENTS = [
        Events.change,
        Events.input,
        Events.submit,
        'update',
    ]

    # def get_info(self, file, format='POSCAR'):
    #     # 创建一个 StringIO 对象
    #     file_obj = io.StringIO()
    #     # 向文件对象中写入数据
    #     file_obj.write(file)
    #     # 将文件对象的指针移动到文件开始位置
    #     file_obj.seek(0)
    #     ase_format = self.format_to_ase(format)
    #     for structure in read(file_obj, index=":", format=ase_format):
    #         formulas = structure.get_chemical_symbols()
    #         cartesian_coords = structure.get_positions()
    #         if structure.pbc.all():
    #             lattice = structure.cell
    #             matrix = lattice.array
    #             length = lattice.lengths()
    #             angle = lattice.angles()
    #             spacegroup = [get_spacegroup(structure).no, get_spacegroup(structure).symbol]
    #             fractional_coords = structure.get_scaled_positions()
    #             atoms = [{"id": index + 1, "formula": formula, "cart_coord": cart, "frac_coord": frac} for
    #                     index, (formula, cart, frac) in enumerate(zip(formulas, cartesian_coords, fractional_coords))]
    #             results = {"atoms": atoms, "matrix": matrix, "length": length, "angle": angle, "spacegroup": spacegroup}
    #         else:
    #             atoms = [{"id": index + 1, "formula": formula, "cart_coord": cart} for
    #                     index, (formula, cart) in enumerate(zip(formulas, cartesian_coords))]
    #             results = {"atoms": atoms}
    #     return results

    def __init__(
        self,
        value: str | Callable | None = None,
        *,
        placeholder: str | None = None,
        label: str | None = None,
        every: float | None = None,
        show_label: bool | None = None,
        scale: int | None = None,
        min_width: int = 160,
        interactive: bool | None = None,
        visible: bool = True,
        rtl: bool = False,
        elem_id: str | None = None,
        elem_classes: list[str] | str | None = None,
        render: bool = True,
        key: int | str | None = None,
        materialFile: str | None = None,
        format: str | None = None,
        height: int = 240,
        style: str | None = 'BallAndStick',
        latticeInfoVisible: bool = False
    ):
        """
        Parameters:
            value: default text to provide in textbox. If callable, the function will be called whenever the app loads to set the initial value of the component.
            placeholder: placeholder hint to provide behind textbox.
            label: component name in interface.
            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
            show_label: if True, will display label.
            scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
            interactive: if True, will be rendered as an editable textbox; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
            visible: If False, component will be hidden.
            rtl: If True and `type` is "text", sets the direction of the text to right-to-left (cursor appears on the left of the text). Default is False, which renders cursor on the right.
            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
            render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
            key: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.
        """
        def convert_numpy(obj):
            if isinstance(obj, np.ndarray):
                return obj.tolist()
            if isinstance(obj, dict):
                return {k: convert_numpy(v) for k, v in obj.items()}
            if isinstance(obj, list):
                return [convert_numpy(v) for v in obj]
            return obj

        def format_to_ase(format):
            if format == 'POSCAR':
                ase_format = 'vasp'
            elif format == 'dump':
                ase_format = 'lammps-dump-text'
            else:
                ase_format = format
            return ase_format

        def get_info(file, format='POSCAR'):
            # 创建一个 StringIO 对象
            file_obj = io.StringIO()
            # 向文件对象中写入数据
            file_obj.write(file)
            # 将文件对象的指针移动到文件开始位置
            file_obj.seek(0)
            def structure_generator():
                ase_format = format_to_ase(format)
                for structure in read(file_obj, index=":", format=ase_format):
                    formulas = structure.get_chemical_symbols()
                    cartesian_coords = structure.get_positions()
                    if structure.pbc.all():
                        lattice = structure.cell
                        matrix = lattice.array
                        length = lattice.lengths()
                        angle = lattice.angles()
                        spacegroup = [get_spacegroup(structure).no, get_spacegroup(structure).symbol]
                        fractional_coords = structure.get_scaled_positions()
                        atoms = [{"id": index + 1, "formula": formula, "cart_coord": cart, "frac_coord": frac} for
                                index, (formula, cart, frac) in enumerate(zip(formulas, cartesian_coords, fractional_coords))]
                        results = {"atoms": atoms, "matrix": matrix, "length": length, "angle": angle, "spacegroup": spacegroup}
                    else:
                        atoms = [{"id": index + 1, "formula": formula, "cart_coord": cart} for
                                index, (formula, cart) in enumerate(zip(formulas, cartesian_coords))]
                        results = {"atoms": atoms}
                    yield results
            yield from structure_generator()
        materialFile = json.dumps(convert_numpy(list(get_info(materialFile, format))))
        self.materialFile = materialFile
        self.format = format
        self.height = height
        self.style = style
        self.latticeInfoVisible = latticeInfoVisible
        super().__init__(
            label=label,
            every=every,
            show_label=show_label,
            scale=scale,
            min_width=min_width,
            interactive=interactive,
            visible=visible,
            elem_id=elem_id,
            elem_classes=elem_classes,
            value=value,
            render=render,
            key=key,
        )


    # def convert_numpy(self, obj):
    #     if isinstance(obj, np.ndarray):
    #         return obj.tolist()
    #     if isinstance(obj, dict):
    #         return {k: self.convert_numpy(v) for k, v in obj.items()}
    #     if isinstance(obj, list):
    #         return [self.convert_numpy(v) for v in obj]
    #     return obj

    # def format_to_ase(self, format):
    #     if format == 'POSCAR':
    #         ase_format = 'vasp'
    #     elif format == 'dump':
    #         ase_format = 'lammps-dump-text'
    #     else:
    #         ase_format = format
    #     return ase_format

    # def get_info(self, file, format='POSCAR'):
    #     # 创建一个 StringIO 对象
    #     file_obj = io.StringIO()
    #     # 向文件对象中写入数据
    #     file_obj.write(file)
    #     # 将文件对象的指针移动到文件开始位置
    #     file_obj.seek(0)
    #     def structure_generator():
    #         ase_format = self.format_to_ase(format)
    #         for structure in read(file_obj, index=":", format=ase_format):
    #             formulas = structure.get_chemical_symbols()
    #             cartesian_coords = structure.get_positions()
    #             if structure.pbc.all():
    #                 lattice = structure.cell
    #                 matrix = lattice.array
    #                 length = lattice.lengths()
    #                 angle = lattice.angles()
    #                 spacegroup = [get_spacegroup(structure).no, get_spacegroup(structure).symbol]
    #                 fractional_coords = structure.get_scaled_positions()
    #                 atoms = [{"id": index + 1, "formula": formula, "cart_coord": cart, "frac_coord": frac} for
    #                         index, (formula, cart, frac) in enumerate(zip(formulas, cartesian_coords, fractional_coords))]
    #                 results = {"atoms": atoms, "matrix": matrix, "length": length, "angle": angle, "spacegroup": spacegroup}
    #             else:
    #                 atoms = [{"id": index + 1, "formula": formula, "cart_coord": cart} for
    #                         index, (formula, cart) in enumerate(zip(formulas, cartesian_coords))]
    #                 results = {"atoms": atoms}
    #             yield results
    #     yield from structure_generator()

    def preprocess(self, payload: str | None) -> str | None:
        """
        Parameters:
            payload: the text entered in the textarea.
        Returns:
            Passes text value as a {str} into the function.
        """
        return None if payload is None else str(payload)

    def postprocess(self, value: str | None) -> str | None:
        """
        Parameters:
            value: Expects a {str} returned from function and sets textarea value to it.
        Returns:
            The value to display in the textarea.
        """
        return None if value is None else str(value)

    def api_info(self) -> dict[str, Any]:
        return {"type": "string"}

    def example_payload(self) -> Any:
        return "Hello!!"

    def example_value(self) -> Any:
        return "Hello!!"
    from typing import Callable, Literal, Sequence, Any, TYPE_CHECKING
    from gradio.blocks import Block
    if TYPE_CHECKING:
        from gradio.components import Timer

    
    def change(self,
        fn: Callable[..., Any] | None = None,
        inputs: Block | Sequence[Block] | set[Block] | None = None,
        outputs: Block | Sequence[Block] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: Timer | float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True,
    
        ) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: if True, will scroll to output component on completion
            show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
            queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        
        """
        ...
    
    def input(self,
        fn: Callable[..., Any] | None = None,
        inputs: Block | Sequence[Block] | set[Block] | None = None,
        outputs: Block | Sequence[Block] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: Timer | float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True,
    
        ) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: if True, will scroll to output component on completion
            show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
            queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        
        """
        ...
    
    def submit(self,
        fn: Callable[..., Any] | None = None,
        inputs: Block | Sequence[Block] | set[Block] | None = None,
        outputs: Block | Sequence[Block] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: Timer | float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True,
    
        ) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: if True, will scroll to output component on completion
            show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
            queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        
        """
        ...
    
    def update(self,
        fn: Callable[..., Any] | None = None,
        inputs: Block | Sequence[Block] | set[Block] | None = None,
        outputs: Block | Sequence[Block] | None = None,
        api_name: str | None | Literal[False] = None,
        scroll_to_output: bool = False,
        show_progress: Literal["full", "minimal", "hidden"] = "full",
        queue: bool | None = None,
        batch: bool = False,
        max_batch_size: int = 4,
        preprocess: bool = True,
        postprocess: bool = True,
        cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
        every: Timer | float | None = None,
        trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
        js: str | None = None,
        concurrency_limit: int | None | Literal["default"] = "default",
        concurrency_id: str | None = None,
        show_api: bool = True,
    
        ) -> Dependency:
        """
        Parameters:
            fn: the function to call when this event is triggered. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
            inputs: list of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
            outputs: list of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
            api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If False, the endpoint will not be exposed in the api docs. If set to None, the endpoint will be exposed in the api docs as an unnamed endpoint, although this behavior will be changed in Gradio 4.0. If set to a string, the endpoint will be exposed in the api docs with the given name.
            scroll_to_output: if True, will scroll to output component on completion
            show_progress: how to show the progress animation while event is running: "full" shows a spinner which covers the output component area as well as a runtime display in the upper right corner, "minimal" only shows the runtime display, "hidden" shows no progress animation at all
            queue: if True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
            batch: if True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then *required* to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
            max_batch_size: maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
            preprocess: if False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
            postprocess: if False, will not run postprocessing of component data before returning 'fn' output to the browser.
            cancels: a list of other events to cancel when this listener is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
            every: continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
            trigger_mode: if "once" (default for all events except `.change()`) would not allow any submissions while an event is pending. If set to "multiple", unlimited submissions are allowed while pending, and "always_last" (default for `.change()` and `.key_up()` events) would allow a second submission after the pending event is complete.
            js: optional frontend js method to run before running 'fn'. Input arguments for js method are values of 'inputs' and 'outputs', return should be a list of values for output components.
            concurrency_limit: if set, this is the maximum number of this event that can be running simultaneously. Can be set to None to mean no concurrency_limit (any number of this event can be running simultaneously). Set to "default" to use the default concurrency limit (defined by the `default_concurrency_limit` parameter in `Blocks.queue()`, which itself is 1 by default).
            concurrency_id: if set, this is the id of the concurrency group. Events with the same concurrency_id will be limited by the lowest set concurrency_limit.
            show_api: whether to show this event in the "view API" page of the Gradio app, or in the ".view_api()" method of the Gradio clients. Unlike setting api_name to False, setting show_api to False will still allow downstream apps as well as the Clients to use this event. If fn is None, show_api will automatically be set to False.
        
        """
        ...    

    # def update(self, materialFile: str, format: str) -> any:
    #     self.materialFile = json.dumps(self.convert_numpy(list(self.get_info(materialFile, format))))
    #     res = {}
    #     res['materialFile'] = self.materialFile
    #     print('Update')
    #     return res