;3h;dZddlmZddlZddlZddlZddlZddlZddlm Z m Z ddl m Z ddl mZmZmZddlmZddlmZmZmZdd lmZdd lmZmZmZmZmZmZm Z m!Z!dd l"m#Z#dd l$m%Z%m&Z&m'Z'dd l(m)Z)ddl*m+Z+m,Z,m-Z-ddl.m/Z/ddl0m1Z1m2Z2m3Z3m4Z4m5Z5ddl6m7Z7m8Z8ddl9m:Z;er ddlm?Z?edddddGddeZ@eGddeZAdd dZBy)!zk This file defines two useful high-level abstractions to build Gradio apps: Interface and TabbedInterface. ) annotationsN)CallableSequence)Path) TYPE_CHECKINGAnyLiteral)document)Examplesutils wasm_utils)Blocks)Button ClearButton ComponentDeepLinkButtonDuplicateButtonMarkdownStateget_component_instance)InterfaceTypes) DependencyEventson) RenderError) CSVLoggerFlaggingCallback FlagMethod)I18nData) AccordionColumnRowTabTabs)load_from_js_pipelineload_from_pipeline) ThemeClass)DiffusionPipeline)Pipelinelaunchload from_pipeline integratequeuec'eZdZUdZej Zded<ed dZ e d!dZ d"dddddddddddd dddd d ddd dddddddd dddddddddd% d#fdZ d$dZ d%dZ d&dZ d'dZdZ d(fd Z d)dZ d*dZdZdZdZxZS)+ Interfaceaq Interface is Gradio's main high-level class, and allows you to create a web-based GUI / demo around a machine learning model (or any Python function) in a few lines of code. You must specify three parameters: (1) the function to create a GUI for (2) the desired input components and (3) the desired output components. Additional parameters can be used to control the appearance and behavior of the demo. Example: import gradio as gr def image_classifier(inp): return {'cat': 0.3, 'dog': 0.7} demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label") demo.launch() Demos: hello_world, hello_world_2, hello_world_3 Guides: quickstart, key-features, sharing-your-app, interface-state, reactive-interfaces, advanced-interface-features, setting-up-a-gradio-demo-for-maximum-performance zweakref.WeakSet instancesc4ttjS)z9 :return: list of all current instances. )listr0r1)clss P/var/www/html/audio-gradio/venv/lib/python3.12/site-packages/gradio/interface.py get_instanceszInterface.get_instancesDs I''((c |tjr t|}n t|}t |fi|}|di|}|S)aB Class method that constructs an Interface from a Hugging Face transformers.Pipeline or diffusers.DiffusionPipeline object. The input and output components are automatically determined from the pipeline. Parameters: pipeline: the pipeline object to use. Returns: a Gradio Interface object from the given Pipeline Example: import gradio as gr from transformers import pipeline pipe = pipeline("image-classification") gr.Interface.from_pipeline(pipe).launch() )r IS_WASMr%r&dict)r4pipelinekwargsinterface_info interfaces r5r,zInterface.from_pipelineKsA"   28ngt|"tvr%|"jK|"jM|_>n2t|"tr |"d"d|_>nt!d$tO|"d|_?|&tCjDd%|&}|tjd&d'|_Bn|d(vr||_Bn t!d)| d*g|_Cnmt|ts t!d+tSd,|Dr|D6cgc] }6d-|6|6f c}6|_Cn%tSd.|Dr||_Cn t!d+| t}||_F||_G|$|_H||_I||_J||_K||_Ld|_Md|_Nd|_Od|_Pd|_Qtjj|t j|jZ}7g}8 tj|jZd}8t?|8dkDr$tj|jZr|8dd}8|8jD]*}9t j|9|7s|8j|9, tQ|j6|8dD]@\}4}9t|4t,st/d/tO|4|4j:|9|4_]Bt;|j8D]c\}+}4t|4t,st/dtO|4|4j:t?|j8dk(rd0|4_]Zd1|+|4_]e|jd2k7r|jtjTk(r1|jj|j6|jn[|jtj"k(rn=|jj|j6|j8z|j|5|jr|jj|jd3\}:};}<}=}>d}?t5|jtjtj"tjTfvr|j\}:};}<}=}?|jtjtj$fvr/|j|:\}@}A}>}B}C|:xs@}:|;xsA};||>j|j|=|;D|Dr.|jr"Djd5d|jgdd6|j|jddd|j|_lycc}+wcc}+wcc},wcc}+}-wcc},}-wcc},}+wcc}6w#t.t f$rt j}8YwxYw#1swYxYw#1swY|xYw)8a* Parameters: fn: the function to wrap an interface around. 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: a single Gradio component, or list of Gradio components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of input components should match the number of parameters in fn. If set to None, then only the output components will be displayed. outputs: a single Gradio component, or list of Gradio components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of output components should match the number of values returned by fn. If set to None, then only the input components will be displayed. examples: sample inputs for the function; if provided, appear below the UI components and can be clicked to populate the interface. 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. 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 defaults to True (as long as `fn` and `outputs` are also provided). 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". In HuggingFace Spaces, this parameter defaults to "eager" except for ZeroGPU Spaces, in which case it defaults to "lazy". examples_per_page: if examples are provided, how many to display per page. live: whether the interface should automatically rerun if any of the inputs change. title: a title for the interface; if provided, appears above the input and output components in large font. Also used as the tab title when opened in a browser window. description: a description for the interface; if provided, appears above the input and output components and beneath the title in regular font. Accepts Markdown and HTML content. article: an expanded article explaining the interface; if provided, appears below the input and output components in regular font. Accepts Markdown and HTML content. If it is an HTTP(S) link to a downloadable remote file, the content of this file is displayed. theme: a Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the Hugging Face Hub (e.g. "gradio/monochrome"). If None, will use the Default theme. flagging_mode: one of "never", "auto", or "manual". If "never" or "auto", users will not see a button to flag an input and output. If "manual", users will see a button to flag. If "auto", every input the user submits will be automatically flagged, along with the generated output. If "manual", both the input and outputs are flagged when the user clicks flag button. This parameter can be set with environmental variable GRADIO_FLAGGING_MODE; otherwise defaults to "manual". flagging_options: if provided, allows user to select from the list of options when flagging. Only applies if flagging_mode is "manual". Can either be a list of tuples of the form (label, value), where label is the string that will be displayed on the button and value is the string that will be stored in the flagging CSV; or it can be a list of strings ["X", "Y"], in which case the values will be the list of strings and the labels will ["Flag as X", "Flag as Y"], etc. flagging_dir: path to the the directory where flagged data is stored. If the directory does not exist, it will be created. flagging_callback: either None or an instance of a subclass of FlaggingCallback which will be called when a sample is flagged. If set to None, an instance of gradio.flagging.CSVLogger will be created and logs will be saved to a local CSV file in flagging_dir. Default to None. analytics_enabled: whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable if defined, or default to True. 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: the maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True) api_name: defines how the endpoint appears in the API docs. Can be a string, None, or False. If set to a string, the endpoint will be exposed in the API docs with the given name. If None, the name of the prediction function will be used as the API endpoint. 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 this event. allow_duplication: if True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces. 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 `.queue()`, which itself is 1 by default). css: Custom css as a code string. This css will be included in the demo webpage. css_paths: Custom css as a pathlib.Path to a css file or a list of such paths. This css files will be read, concatenated, and included in the demo webpage. If the `css` parameter is also set, the css from `css` will be included first. js: Custom js as a code string. The custom js should be in the form of a single js function. This function will automatically be executed when the page loads. For more flexibility, use the head parameter to insert js inside