Skip to main content

Python library for easily interacting with trained machine learning models

Project description

gradio_freddytb

Static Badge

Python library for easily interacting with trained machine learning models

Installation

pip install gradio_freddytb

Usage

import gradio as gr
from gradio_freddytb import FreddyTb


example = FreddyTb().example_inputs()

demo = gr.Interface(
    lambda x:x,
    FreddyTb(),  # interactive version of your component
    FreddyTb(),  # static version of your component
    # examples=[[example]],  # uncomment this line to view the "example version" of your component
)


if __name__ == "__main__":
    demo.launch()

FreddyTb

Initialization

name type default description
value
str | None
None A path or URL for the default value that FreddyTb component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.
label
str | None
None The label for this component. Appears above the component and is also used as the header if there are a table of examples for this component. If None and used in a `gr.Interface`, the label will be the name of the parameter this component is assigned to.
every
float | None
None If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
show_label
bool | None
None if True, will display label.
show_download_button
bool
True If True, will display button to download image.
container
bool
True If True, will place the component in a container - providing some extra padding around the border.
scale
int | None
None relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
min_width
int
160 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
bool | None
None if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output.
visible
bool
True If False, component will be hidden.
elem_id
str | None
None 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
list[str] | str | None
None 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
bool
True 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.

Events

name description
clear This listener is triggered when the user clears the FreddyTb using the X button for the component.
change Triggered when the value of the FreddyTb changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See .input() for a listener that is only triggered by user input.
upload This listener is triggered when the user uploads a file into the FreddyTb.

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

  • When used as an Input, the component only impacts the input signature of the user function.
  • When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

  • As output: Is passed, a string containing the path to the image.
  • As input: Should return, a string or pathlib.Path object containing the path to the image.
def predict(
    value: str | None
) -> str | Path | None:
    return value

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gradio_freddytb-0.0.1.tar.gz (99.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gradio_freddytb-0.0.1-py3-none-any.whl (87.4 kB view details)

Uploaded Python 3

File details

Details for the file gradio_freddytb-0.0.1.tar.gz.

File metadata

  • Download URL: gradio_freddytb-0.0.1.tar.gz
  • Upload date:
  • Size: 99.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for gradio_freddytb-0.0.1.tar.gz
Algorithm Hash digest
SHA256 449836a2c07ebafc2183183aa54511172ce1237075fc10ac4834c8a488d1a908
MD5 65fbd650cc0354508a0baec8698a05e8
BLAKE2b-256 e7cdaf184f8b5c62c3e9a13d9a3524c2ffede214e17a7c32014ba6c429b3c3a8

See more details on using hashes here.

File details

Details for the file gradio_freddytb-0.0.1-py3-none-any.whl.

File metadata

File hashes

Hashes for gradio_freddytb-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 cfe50a7e21c47beab8dce1fdea007a5207920c2d0c73c8dc13f2cf317b002856
MD5 32538bcd34bb04b2fcc3fe93f90fabfa
BLAKE2b-256 883608e0d189a453d6eef5b5c320d1cc1cbf348158cc560018882829a517a85b

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page