Skip to main content

Flet for Python - easily build interactive multi-platform apps in Python

Project description

Flet - quickly build interactive apps for web, desktop and mobile in Python

Flet is a rich User Interface (UI) framework to quickly build interactive web, desktop and mobile apps in Python without prior knowledge of web technologies like HTTP, HTML, CSS or JavaSscript. You build UI with controls based on Flutter widgets to ensure your programs look cool and professional.

Requirements

  • Python 3.7 or above on Windows, Linux or macOS

Installation

pip install flet

Create the app

Create main.py file with the following content:

import flet as ft

def main(page: ft.Page):
    page.title = "Flet counter example"
    page.vertical_alignment = ft.MainAxisAlignment.CENTER

    txt_number = ft.TextField(value="0", text_align=ft.TextAlign.RIGHT, width=100)

    def minus_click(e):
        txt_number.value = str(int(txt_number.value) - 1)
        page.update()

    def plus_click(e):
        txt_number.value = str(int(txt_number.value) + 1)
        page.update()

    page.add(
        ft.Row(
            [
                ft.IconButton(ft.icons.REMOVE, on_click=minus_click),
                txt_number,
                ft.IconButton(ft.icons.ADD, on_click=plus_click),
            ],
            alignment=ft.MainAxisAlignment.CENTER,
        )
    )

ft.app(main)

Run as a desktop app

The following command will start the app in a native OS window:

flet run main.py

Sample app in a native window

Run as a web app

The following command will start the app as a web app:

flet run --web main.py

Sample app in a browser

Learn more

Visit Flet website.

Continue with Python guide to learn how to make a real app.

Browse for more Flet examples.

Join to a conversation on Flet Discord server.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

flet-0.10.3.tar.gz (2.2 MB view details)

Uploaded Source

Built Distributions

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

flet-0.10.3-py3-none-win_amd64.whl (22.7 MB view details)

Uploaded Python 3Windows x86-64

flet-0.10.3-py3-none-win32.whl (22.6 MB view details)

Uploaded Python 3Windows x86

flet-0.10.3-py3-none-musllinux_1_2_x86_64.whl (8.9 MB view details)

Uploaded Python 3musllinux: musl 1.2+ x86-64

flet-0.10.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20.2 MB view details)

Uploaded Python 3manylinux: glibc 2.17+ x86-64

flet-0.10.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl (8.5 MB view details)

Uploaded Python 3manylinux: glibc 2.17+ ARMv7l

flet-0.10.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (19.2 MB view details)

Uploaded Python 3manylinux: glibc 2.17+ ARM64

flet-0.10.3-py3-none-macosx_12_0_arm64.whl (33.5 MB view details)

Uploaded Python 3macOS 12.0+ ARM64

flet-0.10.3-py3-none-macosx_10_14_x86_64.whl (33.7 MB view details)

Uploaded Python 3macOS 10.14+ x86-64

flet-0.10.3-py3-none-any.whl (2.2 MB view details)

Uploaded Python 3

File details

Details for the file flet-0.10.3.tar.gz.

File metadata

  • Download URL: flet-0.10.3.tar.gz
  • Upload date:
  • Size: 2.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for flet-0.10.3.tar.gz
Algorithm Hash digest
SHA256 ebef20f971945ddc3a5e874d9658f595107cbd1892f8dfb0503d76c7850b8234
MD5 28b567cf444b15b9df0eb8cb9f08e334
BLAKE2b-256 f53d4674ede8512630ca88b5fa668aeb234756a18f72c66a66e7fb4f928927a2

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-win_amd64.whl.

File metadata

  • Download URL: flet-0.10.3-py3-none-win_amd64.whl
  • Upload date:
  • Size: 22.7 MB
  • Tags: Python 3, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for flet-0.10.3-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 3064f627398271bcb2f38b604eb1c747e0ea458bf7f363ee9bd8cec1a6abc001
MD5 cd75cc78d73bec735b4736b568850982
BLAKE2b-256 18c1b5b802eb31690e1a77c0e8a71db21d5f1570949f8f52cbdd65f91462bd7c

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-win32.whl.

File metadata

  • Download URL: flet-0.10.3-py3-none-win32.whl
  • Upload date:
  • Size: 22.6 MB
  • Tags: Python 3, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for flet-0.10.3-py3-none-win32.whl
Algorithm Hash digest
SHA256 c3e37b1f385529d081143f32302253c5b9b8b01de77b42a44a1552e1beb3f2ce
MD5 de0ebb849ee73dea2ef54bd1d1c2f79a
BLAKE2b-256 afa3d4a6b00ca33130853fcf1f4882cdc83462f6013c36226baebe85a32958b2

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for flet-0.10.3-py3-none-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 fdfd033179cb1e96dde710af465b4d7ec43a54b55503879d25bb8b15a7f9c149
MD5 6ef26ebc007b931c4925118d6b0c7ee0
BLAKE2b-256 1c58d27e840b118d7b1b0e3b6e0168aaa40814ef007e356b0ad7568a273abe47

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for flet-0.10.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e9c09799d727e3521295ca2568c80cfeb7c94530490c957dab95a83d8dc6f3bd
MD5 5a6927f599e68947ebcf464d46c1cdc2
BLAKE2b-256 a1c691712aa22b0b25727febd2b94444bf246852338d31df675a0c17aaee9894

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl.

File metadata

File hashes

Hashes for flet-0.10.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl
Algorithm Hash digest
SHA256 dfe08dc9defdcbd147beda73a75ce2fd1d89ee952c1d6473e5b6c51fdd1bf04f
MD5 dae27f0eae42daf77114050f0e61905b
BLAKE2b-256 b3c8952c8626c2daa7f4b9fd09fde6211554c9bd7accce9889d4e10add0c4b8a

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for flet-0.10.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c49d8aa6c078aa6b0e380b26ae75f8c02301c7ea632f06df9c7a49946b378224
MD5 a0695140ffd9a760bed9115dcebb67d6
BLAKE2b-256 a36e32b9a70c205a60b06aab2eb102bffcc2bb090dd72d847d140e7a4b5b2ee9

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-macosx_12_0_arm64.whl.

File metadata

  • Download URL: flet-0.10.3-py3-none-macosx_12_0_arm64.whl
  • Upload date:
  • Size: 33.5 MB
  • Tags: Python 3, macOS 12.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for flet-0.10.3-py3-none-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 273c0dcab3d612c549fbdd013dd9fc767d8beda163cde8b5c6ef86cd2903c676
MD5 4ed60b8d1d5842e8dde87b9617a15555
BLAKE2b-256 22704d679cbf861465f63a2e957dbbe490722cbff26e6bd267f7d41b0fe62556

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-macosx_10_14_x86_64.whl.

File metadata

File hashes

Hashes for flet-0.10.3-py3-none-macosx_10_14_x86_64.whl
Algorithm Hash digest
SHA256 3ca2bb41bf1da77ce135ee8dd34a0764414680987ce189cabae95f5936cf7b85
MD5 857a99fdfc4278703a139fd85127ba7a
BLAKE2b-256 b103d0ee4f315750faa8ba8c867296d760e812f8ab23449ef0edd5b468c73e93

See more details on using hashes here.

File details

Details for the file flet-0.10.3-py3-none-any.whl.

File metadata

  • Download URL: flet-0.10.3-py3-none-any.whl
  • Upload date:
  • Size: 2.2 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for flet-0.10.3-py3-none-any.whl
Algorithm Hash digest
SHA256 81f22eda3080e335fac3dbef5533a30030d0c52849db9acb3d39b1ff6190cc6e
MD5 f64e53adb1b9e9934ac81cb592b29f10
BLAKE2b-256 f6617042427d6852c6fe6bed2b56ccc34a20e357364d5671f1b8870155d5fada

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