""" Defines helper methods useful for loading and caching Interface examples. """ from __future__ import annotations import ast import copy import csv import inspect import os import shutil import warnings from collections.abc import Callable, Iterable, Sequence from functools import partial from pathlib import Path from typing import TYPE_CHECKING, Any, Literal, Optional from gradio_client import utils as client_utils from gradio_client.documentation import document from gradio import components, oauth, processing_utils, routes, utils, wasm_utils from gradio.context import Context, LocalContext, get_blocks_context from gradio.data_classes import GradioModel, GradioRootModel from gradio.events import Dependency, EventData from gradio.exceptions import Error from gradio.flagging import CSVLogger from gradio.i18n import I18nData from gradio.utils import UnhashableKeyDict if TYPE_CHECKING: # Only import for type checking (to avoid circular imports). from gradio.components import Component LOG_FILE = "log.csv" def create_examples( examples: list[Any] | list[list[Any]] | str, inputs: Component | Sequence[Component], outputs: Component | Sequence[Component] | None = None, fn: Callable | None = None, cache_examples: bool | None = None, cache_mode: Literal["eager", "lazy"] | None = None, examples_per_page: int = 10, _api_mode: bool = False, label: str | I18nData | None = None, elem_id: str | None = None, run_on_click: bool = False, preprocess: bool = True, postprocess: bool = True, api_name: str | Literal[False] = "load_example", batch: bool = False, *, example_labels: list[str] | None = None, visible: bool = True, ): """Top-level synchronous function that creates Examples. Provided for backwards compatibility, i.e. so that gr.Examples(...) can be used to create the Examples component.""" examples_obj = Examples( examples=examples, inputs=inputs, outputs=outputs, fn=fn, cache_examples=cache_examples, cache_mode=cache_mode, examples_per_page=examples_per_page, _api_mode=_api_mode, label=label, elem_id=elem_id, run_on_click=run_on_click, preprocess=preprocess, postprocess=postprocess, api_name=api_name, batch=batch, example_labels=example_labels, visible=visible, _initiated_directly=False, ) examples_obj.create() return examples_obj @document() class Examples: """ This class is a wrapper over the Dataset component and can be used to create Examples for Blocks / Interfaces. Populates the Dataset component with examples and assigns event listener so that clicking on an example populates the input/output components. Optionally handles example caching for fast inference. Demos: calculator_blocks Guides: more-on-examples-and-flagging, using-hugging-face-integrations, image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, create-your-own-friends-with-a-gan """ def __init__( self, examples: list[Any] | list[list[Any]] | str, inputs: Component | Sequence[Component], outputs: Component | Sequence[Component] | None = None, fn: Callable | None = None, cache_examples: bool | None = None, cache_mode: Literal["eager", "lazy"] | None = None, examples_per_page: int = 10, _api_mode: bool = False, label: str | I18nData | None = "Examples", elem_id: str | None = None, run_on_click: bool = False, preprocess: bool = True, postprocess: bool = True, api_name: str | Literal[False] = "load_example", batch: bool = False, *, example_labels: list[str] | None = None, visible: bool = True, _initiated_directly: bool = True, ): """ Parameters: examples: example inputs that can be clicked to populate specific components. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided but it should be within the directory with the python file running the gradio app. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs. inputs: the component or list of components corresponding to the examples outputs: optionally, provide the component or list of components corresponding to the output of the examples. Required if `cache_examples` is not False. fn: optionally, provide the function to run to generate the outputs corresponding to the examples. Required if `cache_examples` is not False. Also required if `run_on_click` is True. cache_examples: If True, caches examples in the server for fast runtime in examples. If "lazy", then examples are cached (for all users of the app) after their first use (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES environment variable, which should be either "true" or "false". In HuggingFace Spaces, this parameter is True (as long as `fn` and `outputs` are also provided). The default option otherwise is False. cache_mode: if "lazy", examples are cached after their first use. If "eager", all examples are cached at app launch. If None, will use the GRADIO_CACHE_MODE environment variable if defined, or default to "eager". examples_per_page: how many examples to show per page. label: the label to use for the examples component (by default, "Examples") elem_id: an optional string that is assigned as the id of this component in the HTML DOM. run_on_click: if cache_examples is False, clicking on an example does not run the function when an example is clicked. Set this to True to run the function when an example is clicked. Has no effect if cache_examples is True. preprocess: if True, preprocesses the example input before running the prediction function and caching the output. Only applies if `cache_examples` is not False. postprocess: if True, postprocesses the example output after running the prediction function and before caching. Only applies if `cache_examples` is not False. api_name: Defines how the event associated with clicking on the examples appears in the API docs. Can be a string or False. If set to a string, the endpoint will be exposed in the API docs with the given name. If False, the endpoint will not be exposed in the API docs and downstream apps (including those that `gr.load` this app) will not be able to use the example function. 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. Used only if cache_examples is not False. example_labels: A list of labels for each example. If provided, the length of this list should be the same as the number of examples, and these labels will be used in the UI instead of rendering the example values. visible: If False, the examples component will be hidden in the UI. """ if _initiated_directly: warnings.warn( "Please use gr.Examples(...) instead of gr.examples.Examples(...) to create the Examples.", ) self.cache_examples = False if cache_examples is None: if ( os.getenv("GRADIO_CACHE_EXAMPLES", "").lower() in ["true", "lazy"] and fn is not None and outputs is not None ): self.cache_examples = True elif cache_examples == "lazy": warnings.warn( "In future versions of Gradio, the `cache_examples` parameter will no longer accept a value of 'lazy'. To enable lazy caching in " "Gradio, you should set `cache_examples=True`, and `cache_mode='lazy'` instead." ) self.cache_examples = "lazy" elif cache_examples in [True, False]: self.cache_examples = cache_examples else: raise ValueError( f"The `cache_examples` parameter should be either True or False, not {cache_examples}" ) if self.cache_examples and (fn is None or outputs is None): raise ValueError("If caching examples, `fn` and `outputs` must be provided") if (cache_mode_env := os.getenv("GRADIO_CACHE_MODE")) and cache_mode is None: if cache_mode_env.lower() == "eager": cache_mode = "eager" elif cache_mode_env.lower() == "lazy": cache_mode = "lazy" else: cache_mode = "eager" warnings.warn( "The `GRADIO_CACHE_MODE` environment variable must be either 'eager' or 'lazy'. " "Defaulting to 'eager'." ) if self.cache_examples and cache_mode == "lazy": self.cache_examples = "lazy" if not isinstance(inputs, Sequence): inputs = [inputs] if outputs and not isinstance(outputs, Sequence): outputs = [outputs] working_directory = Path().absolute() if examples is None: raise ValueError("The parameter `examples` cannot be None") elif isinstance(examples, list) and ( len(examples) == 0 or isinstance(examples[0], list) ): pass elif ( isinstance(examples, list) and len(inputs) == 1 ): # If there is only one input component, examples can be provided as a regular list instead of a list of lists examples = [[e] for e in examples] elif isinstance(examples, str): if not Path(examples).exists(): raise FileNotFoundError( f"Could not find examples directory: {examples}" ) working_directory = examples if not (Path(examples) / LOG_FILE).exists(): if len(inputs) == 1: examples = [[e] for e in os.listdir(examples)] else: raise FileNotFoundError( "Could not find log file (required for multiple inputs): " + LOG_FILE ) else: with open(Path(examples) / LOG_FILE) as logs: examples = list(csv.reader(logs)) examples = [ examples[i][: len(inputs)] for i in range(1, len(examples)) ] # remove header and unnecessary columns else: raise ValueError( "The parameter `examples` must either be a string directory or a list" "(if there is only 1 input component) or (more generally), a nested " "list, where each sublist represents a set of inputs." ) input_has_examples = [False] * len(inputs) for example in examples: for idx, example_for_input in enumerate(example): if example_for_input is not None: try: input_has_examples[idx] = True except IndexError: pass # If there are more example components than inputs, ignore. This can sometimes be intentional (e.g. loading from a log file where outputs and timestamps are also logged) inputs_with_examples = [ inp for (inp, keep) in zip(inputs, input_has_examples, strict=False) if keep ] non_none_examples = [ [ ex for (ex, keep) in zip(example, input_has_examples, strict=False) if keep ] for example in examples ] if example_labels is not None and len(example_labels) != len(examples): raise ValueError( "If `example_labels` are provided, the length of `example_labels` must be the same as the number of examples." ) self.examples = examples self.non_none_examples = non_none_examples self.inputs = inputs self.input_has_examples = input_has_examples self.inputs_with_examples = inputs_with_examples self.outputs = outputs or [] self.fn = fn self._api_mode = _api_mode self.preprocess = preprocess self.postprocess = postprocess self.api_name: str | Literal[False] = api_name self.batch = batch self.example_labels = example_labels self.working_directory = working_directory from gradio import components with utils.set_directory(working_directory): self.dataset = components.Dataset( components=inputs_with_examples, samples=copy.deepcopy(non_none_examples), type="tuple", label=label, samples_per_page=examples_per_page, elem_id=elem_id, visible=visible, sample_labels=example_labels, ) self.cache_logger = CSVLogger( simplify_file_data=False, verbose=False, dataset_file_name="log.csv" ) self.cached_folder = utils.get_cache_folder() / str(self.dataset._id) if ( os.environ.get("GRADIO_RESET_EXAMPLES_CACHE") == "True" and self.cached_folder.exists() ): shutil.rmtree(self.cached_folder) self.cached_file = Path(self.cached_folder) / "log.csv" self.cached_indices_file = Path(self.cached_folder) / "indices.csv" self.run_on_click = run_on_click self.cache_event: Dependency | None = None self.non_none_processed_examples = UnhashableKeyDict() if self.dataset.samples: for index, example in enumerate(self.non_none_examples): self.non_none_processed_examples[self.dataset.samples[index]] = ( self._get_processed_example(example) ) if self.cache_examples == "lazy": print( f"Will cache examples in '{utils.abspath(self.cached_folder)}' directory at first use.", end="", ) if Path(self.cached_file).exists(): print( "If method or examples have changed since last caching, delete this folder to reset cache." ) print("\n") def _get_processed_example(self, example): """ This function is used to get the post-processed example values, ready to be used in the frontend for each input component. For example, if the input components are image components, the post-processed example values will be the a list of ImageData dictionaries with the path, url, size, mime_type, orig_name, and is_stream keys. For any input components that should be skipped (b/c they are None for all samples), they will simply be absent from the returned list Parameters: example: a list of example values for each input component, excluding those components that have all None values """ if example in self.non_none_processed_examples: return self.non_none_processed_examples[example] with utils.set_directory(self.working_directory): sub = [] for component, sample in zip( self.inputs_with_examples, example, strict=False ): prediction_value = component.postprocess(sample) if isinstance(prediction_value, (GradioRootModel, GradioModel)): prediction_value = prediction_value.model_dump() prediction_value = processing_utils.move_files_to_cache( prediction_value, component, postprocess=True, ) sub.append(prediction_value) return sub def create(self) -> None: """Creates the Dataset component to hold the examples""" blocks_config = get_blocks_context() self.root_block = Context.root_block or ( blocks_config.root_block if blocks_config else None ) if blocks_config: if self.root_block: self.root_block.extra_startup_events.append(self._start_caching) if self.cache_examples: def load_example_input(example_tuple): _, example_value = example_tuple processed_example = self._get_processed_example(example_value) return utils.resolve_singleton(processed_example) def load_example_output(example_tuple): example_id, _ = example_tuple cached_outputs = self.load_from_cache(example_id) return utils.resolve_singleton(cached_outputs) self.cache_event = self.load_input_event = self.dataset.click( load_example_input, inputs=[self.dataset], outputs=self.inputs, show_progress="hidden", postprocess=False, queue=False, show_api=False, ).then( load_example_output, inputs=[self.dataset], outputs=self.outputs, postprocess=False, api_name=self.api_name, show_api=False, ) else: def load_example(example_tuple): _, example_value = example_tuple processed_example = self._get_processed_example(example_value) if len(self.inputs_with_examples) == 1: return update( value=processed_example[0], **self.dataset.component_props[0], # type: ignore ) return [ update( value=processed_example[i], **self.dataset.component_props[i], # type: ignore ) for i in range(len(self.inputs_with_examples)) ] self.load_input_event = self.dataset.click( load_example, inputs=[self.dataset], outputs=self.inputs_with_examples, show_progress="hidden", postprocess=False, queue=False, api_name=self.api_name, show_api=False, ) if self.run_on_click: if self.fn is None: raise ValueError( "Cannot run_on_click if no function is provided" ) self.load_input_event.then( self.fn, inputs=self.inputs, outputs=self.outputs, show_api=False, ) else: warnings.warn( f"If an Examples object is created outside a Blocks Context, make sure to call `examples.dataset.render()`{'and `examples.create()`' if self.cache_examples else ''} to render the examples in the interface." ) async def _postprocess_output(self, output) -> list: """ This is a way that we can postprocess the data manually, since we set postprocess=False in the lazy_cache event handler. The reason we did that is because we don't want to postprocess data if we are loading from the cache, since that has already been postprocessed. We postprocess this data manually if we are calling the function using the _handle_callable_as_generator() method. """ import gradio as gr with gr.Blocks() as demo: [output.render() for output in self.outputs] demo.load(self.fn, self.inputs, self.outputs) demo.unrender() return await demo.postprocess_data(demo.default_config.fns[0], output, None) def _get_cached_index_if_cached(self, example_index) -> int | None: if Path(self.cached_indices_file).exists(): with open(self.cached_indices_file) as f: cached_indices = [int(line.strip()) for line in f] if example_index in cached_indices: cached_index = cached_indices.index(example_index) return cached_index return None async def _start_caching(self): if self.cache_examples: for example in self.examples: if len([ex for ex in example if ex is not None]) != len(self.inputs): warnings.warn( "Examples will be cached but not all input components have " "example values. This may result in an exception being thrown by " "your function. If you do get an error while caching examples, make " "sure all of your inputs have example values for all of your examples " "or you provide default values for those particular parameters in your function." ) break if self.cache_examples is True: if wasm_utils.IS_WASM: # In the Wasm mode, the `threading` module is not supported, # so `client_utils.synchronize_async` is also not available. # And `self.cache()` should be waited for to complete before this method returns, # (otherwise, an error "Cannot cache examples if not in a Blocks context" will be raised anyway) # so `eventloop.create_task(self.cache())` is also not an option. warnings.warn( "Setting `cache_examples=True` is not supported in the Wasm mode. You can set `cache_examples='lazy'` to cache examples after first use." ) else: await self.cache() async def cache(self, example_id: int | None = None) -> None: """ Caches examples so that their predictions can be shown immediately. Parameters: example_id: The id of the example to process (zero-indexed). If None, all examples are cached. """ if self.root_block is None: raise Error("Cannot cache examples if not in a Blocks context.") if Path(self.cached_file).exists() and example_id is None: print( f"Using cache from '{utils.abspath(self.cached_folder)}' directory. If method or examples have changed since last caching, delete this folder to clear cache.\n" ) else: print(f"Caching examples at: '{utils.abspath(self.cached_folder)}'") self.cache_logger.setup(self.outputs, self.cached_folder) generated_values = [] if inspect.isgeneratorfunction(self.fn): def get_final_item(*args): # type: ignore x = None generated_values.clear() for x in self.fn(*args): # noqa: B007 # type: ignore generated_values.append(x) return x fn = get_final_item elif inspect.isasyncgenfunction(self.fn): async def get_final_item(*args): x = None generated_values.clear() async for x in self.fn(*args): # noqa: B007 # type: ignore generated_values.append(x) return x fn = get_final_item else: fn = self.fn # create a fake dependency to process the examples and get the predictions from gradio.events import EventListenerMethod _, fn_index = self.root_block.default_config.set_event_trigger( [EventListenerMethod(Context.root_block, "load")], fn=fn, inputs=self.inputs, outputs=self.outputs, preprocess=self.preprocess and not self._api_mode, postprocess=self.postprocess and not self._api_mode, batch=self.batch, ) if self.outputs is None: raise ValueError("self.outputs is missing") for i, example in enumerate(self.non_none_examples): if example_id is not None and i != example_id: continue processed_input = self._get_processed_example(example) for index, keep in enumerate(self.input_has_examples): if not keep: processed_input.insert(index, None) if self.batch: processed_input = [[value] for value in processed_input] with utils.MatplotlibBackendMananger(): prediction = await self.root_block.process_api( block_fn=self.root_block.default_config.fns[fn_index], inputs=processed_input, request=None, ) output = prediction["data"] if generated_values: output = await merge_generated_values_into_output( self.outputs, generated_values, output ) if self.batch: output = [value[0] for value in output] self.cache_logger.flag(output) # Remove the "fake_event" to prevent bugs in loading interfaces from spaces self.root_block.default_config.fns.pop(fn_index) def load_from_cache(self, example_id: int) -> list[Any]: """Loads a particular cached example for the interface. Parameters: example_id: The id of the example to process (zero-indexed). """ if self.cache_examples == "lazy": if (cached_index := self._get_cached_index_if_cached(example_id)) is None: client_utils.synchronize_async(self.cache, example_id) with open(self.cached_indices_file, "a") as f: f.write(f"{example_id}\n") with open(self.cached_indices_file) as f: example_id = len(f.readlines()) - 1 else: example_id = cached_index with open(self.cached_file, encoding="utf-8") as cache: examples = list(csv.reader(cache)) example = examples[example_id + 1] # +1 to adjust for header output = [] if self.outputs is None: raise ValueError("self.outputs is missing") for component, value in zip(self.outputs, example, strict=False): value_to_use = value try: value_as_dict = ast.literal_eval(value) # File components that output multiple files get saved as a python list # need to pass the parsed list to serialize # TODO: Better file serialization in 4.0 if isinstance(value_as_dict, list) and isinstance( component, components.File ): value_to_use = value_as_dict if not utils.is_prop_update(value_as_dict): raise TypeError("value wasn't an update") # caught below output.append(value_as_dict) except (ValueError, TypeError, SyntaxError): output.append(component.read_from_flag(value_to_use)) return output async def merge_generated_values_into_output( components: Sequence[Component], generated_values: list, output: list ): from gradio.components.base import StreamingOutput for output_index, output_component in enumerate(components): if isinstance(output_component, StreamingOutput) and output_component.streaming: binary_chunks = [] desired_output_format = None for i, chunk in enumerate(generated_values): if len(components) > 1: chunk = chunk[output_index] processed_chunk = output_component.postprocess(chunk) if isinstance(processed_chunk, (GradioModel, GradioRootModel)): processed_chunk = processed_chunk.model_dump() stream_chunk = await output_component.stream_output( processed_chunk, "", i == 0 ) if i == 0 and (orig_name := stream_chunk[1].get("orig_name")): desired_output_format = Path(orig_name).suffix[1:] if stream_chunk[0]: binary_chunks.append(stream_chunk[0]["data"]) combined_output = await output_component.combine_stream( binary_chunks, desired_output_format=desired_output_format ) output[output_index] = combined_output.model_dump() return output class TrackedIterable: def __init__( self, iterable: Iterable | None, index: int | None, length: int | None, desc: str | None, unit: str | None, _tqdm=None, progress: float | None = None, ) -> None: self.iterable = iterable self.index = index self.length = length self.desc = desc self.unit = unit self._tqdm = _tqdm self.progress = progress @document("__call__", "tqdm") class Progress(Iterable): """ The Progress class provides a custom progress tracker that is used in a function signature. To attach a Progress tracker to a function, simply add a parameter right after the input parameters that has a default value set to a `gradio.Progress()` instance. The Progress tracker can then be updated in the function by calling the Progress object or using the `tqdm` method on an Iterable. The Progress tracker is currently only available with `queue()`. Example: import gradio as gr import time def my_function(x, progress=gr.Progress()): progress(0, desc="Starting...") time.sleep(1) for i in progress.tqdm(range(100)): time.sleep(0.1) return x gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch() """ def __init__( self, track_tqdm: bool = False, ): """ Parameters: track_tqdm: If True, the Progress object will track any tqdm.tqdm iterations with the tqdm library in the function. """ if track_tqdm: patch_tqdm() self.track_tqdm = track_tqdm self.iterables: list[TrackedIterable] = [] def __len__(self): return self.iterables[-1].length def __iter__(self): return self def __next__(self): """ Updates progress tracker with next item in iterable. """ callback = self._progress_callback() if callback: current_iterable = self.iterables[-1] while ( not hasattr(current_iterable.iterable, "__next__") and len(self.iterables) > 0 ): current_iterable = self.iterables.pop() callback(self.iterables) if current_iterable.index is None: raise IndexError("Index not set.") current_iterable.index += 1 try: return next(current_iterable.iterable) # type: ignore except StopIteration: self.iterables.pop() raise else: return self def __call__( self, progress: float | tuple[int, int | None] | None, desc: str | None = None, total: int | None = None, unit: str = "steps", _tqdm=None, ): """ Updates progress tracker with progress and message text. Parameters: progress: If float, should be between 0 and 1 representing completion. If Tuple, first number represents steps completed, and second value represents total steps or None if unknown. If None, hides progress bar. desc: description to display. total: estimated total number of steps. unit: unit of iterations. """ callback = self._progress_callback() if callback: if isinstance(progress, tuple): index, total = progress progress = None else: index = None callback( self.iterables + [TrackedIterable(None, index, total, desc, unit, _tqdm, progress)] ) else: return progress def tqdm( self, iterable: Iterable | None, desc: str | None = None, total: int | None = None, unit: str = "steps", _tqdm=None, ): """ Attaches progress tracker to iterable, like tqdm. Parameters: iterable: iterable to attach progress tracker to. desc: description to display. total: estimated total number of steps. unit: unit of iterations. """ callback = self._progress_callback() if callback: if iterable is None: new_iterable = TrackedIterable(None, 0, total, desc, unit, _tqdm) self.iterables.append(new_iterable) callback(self.iterables) return self length = len(iterable) if hasattr(iterable, "__len__") else total # type: ignore self.iterables.append( TrackedIterable(iter(iterable), 0, length, desc, unit, _tqdm) ) return self def update(self, n=1): """ Increases latest iterable with specified number of steps. Parameters: n: number of steps completed. """ callback = self._progress_callback() if callback and len(self.iterables) > 0: current_iterable = self.iterables[-1] if current_iterable.index is None: raise IndexError("Index not set.") current_iterable.index += n callback(self.iterables) else: return def close(self, _tqdm): """ Removes iterable with given _tqdm. """ callback = self._progress_callback() if callback: for i in range(len(self.iterables)): if id(self.iterables[i]._tqdm) == id(_tqdm): self.iterables.pop(i) break callback(self.iterables) else: return @staticmethod def _progress_callback(): blocks = LocalContext.blocks.get() event_id = LocalContext.event_id.get() if not (blocks and event_id): return None return partial(blocks._queue.set_progress, event_id) def patch_tqdm() -> None: try: _tqdm = __import__("tqdm") except ModuleNotFoundError: return def init_tqdm( self, iterable=None, desc=None, total=None, unit="steps", *args, **kwargs ): self._progress = LocalContext.progress.get() if self._progress is not None: self._progress.tqdm(iterable, desc, total, unit, _tqdm=self) kwargs["file"] = open(os.devnull, "w") # noqa: SIM115 self.__init__orig__(iterable, desc, total, *args, unit=unit, **kwargs) def iter_tqdm(self): if self._progress is not None: return self._progress return self.__iter__orig__() def update_tqdm(self, n=1): if self._progress is not None: self._progress.update(n) return self.__update__orig__(n) def close_tqdm(self): if self._progress is not None: self._progress.close(self) return self.__close__orig__() def exit_tqdm(self, exc_type, exc_value, traceback): if self._progress is not None: self._progress.close(self) return self.__exit__orig__(exc_type, exc_value, traceback) # Backup if not hasattr(_tqdm.tqdm, "__init__orig__"): _tqdm.tqdm.__init__orig__ = _tqdm.tqdm.__init__ if not hasattr(_tqdm.tqdm, "__update__orig__"): _tqdm.tqdm.__update__orig__ = _tqdm.tqdm.update if not hasattr(_tqdm.tqdm, "__close__orig__"): _tqdm.tqdm.__close__orig__ = _tqdm.tqdm.close if not hasattr(_tqdm.tqdm, "__exit__orig__"): _tqdm.tqdm.__exit__orig__ = _tqdm.tqdm.__exit__ if not hasattr(_tqdm.tqdm, "__iter__orig__"): _tqdm.tqdm.__iter__orig__ = _tqdm.tqdm.__iter__ # Patch _tqdm.tqdm.__init__ = init_tqdm _tqdm.tqdm.update = update_tqdm _tqdm.tqdm.close = close_tqdm _tqdm.tqdm.__exit__ = exit_tqdm _tqdm.tqdm.__iter__ = iter_tqdm if hasattr(_tqdm, "auto") and hasattr(_tqdm.auto, "tqdm"): _tqdm.auto.tqdm = _tqdm.tqdm def create_tracker(fn, track_tqdm): progress = Progress(track_tqdm=track_tqdm) if not track_tqdm: return progress, fn return progress, utils.function_wrapper( f=fn, before_fn=LocalContext.progress.set, before_args=(progress,), after_fn=LocalContext.progress.set, after_args=(None,), ) def special_args( fn: Callable, inputs: list[Any] | None = None, request: routes.Request | None = None, event_data: EventData | None = None, ) -> tuple[list, int | None, int | None]: """ Checks if function has special arguments Request or EventData (via annotation) or Progress (via default value). If inputs is provided, these values will be loaded into the inputs array. Parameters: fn: function to check. inputs: array to load special arguments into. request: request to load into inputs. event_data: event-related data to load into inputs. Returns: updated inputs, progress index, event data index. """ try: signature = inspect.signature(fn) except ValueError: return inputs or [], None, None type_hints = utils.get_type_hints(fn) positional_args = [] for param in signature.parameters.values(): if param.kind not in (param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD): break positional_args.append(param) progress_index = None event_data_index = None for i, param in enumerate(positional_args): type_hint = type_hints.get(param.name) if isinstance(param.default, Progress): progress_index = i if inputs is not None: inputs.insert(i, param.default) elif type_hint in (routes.Request, Optional[routes.Request]): if inputs is not None: inputs.insert(i, request) elif type_hint in ( oauth.OAuthProfile | None, oauth.OAuthToken | None, oauth.OAuthProfile, oauth.OAuthToken, ): if inputs is not None: # Retrieve session from gr.Request, if it exists (i.e. if user is logged in) session = ( # request.session (if fastapi.Request obj i.e. direct call) getattr(request, "session", {}) or # or request.request.session (if gr.Request obj i.e. websocket call) getattr(getattr(request, "request", None), "session", {}) ) # Inject user profile if type_hint in (Optional[oauth.OAuthProfile], oauth.OAuthProfile): oauth_profile = ( session["oauth_info"]["userinfo"] if "oauth_info" in session else None ) if oauth_profile is not None: oauth_profile = oauth.OAuthProfile(oauth_profile) elif type_hint == oauth.OAuthProfile: raise Error( "This action requires a logged in user. Please sign in and retry." ) inputs.insert(i, oauth_profile) # Inject user token elif type_hint in (Optional[oauth.OAuthToken], oauth.OAuthToken): oauth_info = session.get("oauth_info", None) oauth_token = ( oauth.OAuthToken( token=oauth_info["access_token"], scope=oauth_info["scope"], expires_at=oauth_info["expires_at"], ) if oauth_info is not None else None ) if oauth_token is None and type_hint == oauth.OAuthToken: raise Error( "This action requires a logged in user. Please sign in and retry." ) inputs.insert(i, oauth_token) elif ( type_hint and inspect.isclass(type_hint) and issubclass(type_hint, EventData) ): event_data_index = i if inputs is not None and event_data is not None: processing_utils.check_all_files_in_cache(event_data._data) inputs.insert(i, type_hint(event_data.target, event_data._data)) elif ( param.default is not param.empty and inputs is not None and len(inputs) <= i ): inputs.insert(i, param.default) if inputs is not None: while len(inputs) < len(positional_args): i = len(inputs) param = positional_args[i] if param.default == param.empty: warnings.warn("Unexpected argument. Filling with None.") inputs.append(None) else: inputs.append(param.default) return inputs or [], progress_index, event_data_index def update( elem_id: str | None = None, elem_classes: list[str] | str | None = None, visible: bool | None = None, **kwargs: Any, ) -> dict[str, Any]: """ Updates a component's properties. When a function passed into a Gradio Interface or a Blocks events returns a value, it typically updates the value of the output component. But it is also possible to update the *properties* of an output component (such as the number of lines of a `Textbox` or the visibility of an `Row`) by returning a component and passing in the parameters to update in the constructor of the component. Alternatively, you can return `gr.update(...)` with any arbitrary parameters to update. (This is useful as a shorthand or if the same function can be called with different components to update.) This method does not work with the `gr.State` component. Parameters: elem_id: Use this to update the id of the component in the HTML DOM elem_classes: Use this to update the classes of the component in the HTML DOM visible: Use this to update the visibility of the component kwargs: Any other keyword arguments to update the component's properties. Example: import gradio as gr with gr.Blocks() as demo: radio = gr.Radio([1, 2, 4], label="Set the value of the number") number = gr.Number(value=2, interactive=True) radio.change(fn=lambda value: gr.update(value=value), inputs=radio, outputs=number) demo.launch() """ kwargs["__type__"] = "update" if elem_id is not None: kwargs["elem_id"] = elem_id if elem_classes is not None: kwargs["elem_classes"] = elem_classes if visible is not None: kwargs["visible"] = visible return kwargs @document() def skip() -> dict: """ A special function that can be returned from a Gradio function to skip updating the output component. This may be useful when you want to update the output component conditionally, and in some cases, you want to skip updating the output component. If you have multiple output components, you can return `gr.skip()` as part of a tuple to skip updating a specific output component, or you can return a single `gr.skip()` to skip updating all output components. """ return {"__type__": "update"} def log_message( message: str, title: str, level: Literal["info", "warning", "success"] = "info", duration: float | None = 10, visible: bool = True, ): from gradio.context import LocalContext blocks = LocalContext.blocks.get() event_id = LocalContext.event_id.get() if blocks is None or event_id is None: # Function called outside of Gradio if blocks is None # Or from /api/predict if event_id is None if level in ("info", "success"): print(message) elif level == "warning": warnings.warn(message) return blocks._queue.log_message( event_id=event_id, log=message, title=title, level=level, duration=duration, visible=visible, ) @document(documentation_group="modals") def Warning( # noqa: N802 message: str = "Warning issued.", duration: float | None = 10, visible: bool = True, title: str = "Warning", ): """ This function allows you to pass custom warning messages to the user. You can do so simply by writing `gr.Warning('message here')` in your function, and when that line is executed the custom message will appear in a modal on the demo. The modal is yellow by default and has the heading: "Warning." Queue must be enabled for this behavior; otherwise, the warning will be printed to the console using the `warnings` library. Demos: blocks_chained_events Parameters: message: The warning message to be displayed to the user. Can be HTML, which will be rendered in the modal. duration: The duration in seconds that the warning message should be displayed for. If None or 0, the message will be displayed indefinitely until the user closes it. visible: Whether the error message should be displayed in the UI. title: The title to be displayed to the user at the top of the modal. Example: import gradio as gr def hello_world(): gr.Warning('This is a warning message.') return "hello world" with gr.Blocks() as demo: md = gr.Markdown() demo.load(hello_world, inputs=None, outputs=[md]) demo.launch() """ log_message( message, title=title, level="warning", duration=duration, visible=visible ) @document(documentation_group="modals") def Info( # noqa: N802 message: str = "Info issued.", duration: float | None = 10, visible: bool = True, title: str = "Info", ): """ This function allows you to pass custom info messages to the user. You can do so simply by writing `gr.Info('message here')` in your function, and when that line is executed the custom message will appear in a modal on the demo. The modal is gray by default and has the heading: "Info." Queue must be enabled for this behavior; otherwise, the message will be printed to the console. Demos: blocks_chained_events Parameters: message: The info message to be displayed to the user. Can be HTML, which will be rendered in the modal. duration: The duration in seconds that the info message should be displayed for. If None or 0, the message will be displayed indefinitely until the user closes it. visible: Whether the error message should be displayed in the UI. title: The title to be displayed to the user at the top of the modal. Example: import gradio as gr def hello_world(): gr.Info('This is some info.') return "hello world" with gr.Blocks() as demo: md = gr.Markdown() demo.load(hello_world, inputs=None, outputs=[md]) demo.launch() """ log_message(message, title=title, level="info", duration=duration, visible=visible) @document(documentation_group="modals") def Success( # noqa: N802 message: str = "Success.", duration: float | None = 10, visible: bool = True, title: str = "Success", ): """ This function allows you to pass custom success messages to the user. You can do so simply by writing `gr.Success('message here')` in your function, and when that line is executed the custom message will appear in a modal on the demo. The modal is green by default and has the heading: "Success." Queue must be enabled for this behavior; otherwise, the message will be printed to the console. Parameters: message: The success message to be displayed to the user. Can be HTML, which will be rendered in the modal. duration: The duration in seconds that the success message should be displayed for. If None or 0, the message will be displayed indefinitely until the user closes it. visible: Whether the error message should be displayed in the UI. title: The title to be displayed to the user at the top of the modal. Example: def hello_world(): gr.Success('Operation completed successfully!') return "hello world" with gr.Blocks() as demo: md = gr.Markdown() demo.load(hello_world, inputs=None, outputs=[md]) demo.launch() """ log_message( message, title=title, level="success", duration=duration, visible=visible )