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Avaiga / taipy

Turns Data and AI algorithms into production-ready web applications in no time.
https://www.taipy.io
Apache License 2.0
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[DOCS] Adding All Contributors' Profile Photos #1926

Closed say-het closed 1 month ago

say-het commented 1 month ago

Issue Description

Screenshots or Examples (if applicable)

I will add this type of Live Contributor Profile Photos in it image

Proposed Solution (optional)

No response

Code of Conduct

heysolomon commented 1 month ago

Hi @say-het , are you working on this? I would like to work on the issue.

arnab825 commented 1 month ago

Hacktoberfest_clickable

Taipy

Build Python Data & AI web applications

From simple pilots to production-ready web applications in no time.
No more compromise on performance, customization, and scalability.


**Go beyond existing libraries**


📚 Explore the docs
🫱🏼‍🫲🏼 Discord support
👀 Demos & Examples

 

⭐️ What's Taipy?

Taipy is designed for data scientists and machine learning engineers to build data & AI web applications.  

⭐️ Enables building production-ready web applications.
⭐️ No need to learn new languages. Only Python is needed.
⭐️ Concentrate on Data and AI algorithms without development and deployment complexities.

 

Taipy is a Two-in-One Tool for UI Generation and Scenario/Data Management


User Interface Generation Scenario and Data Management
Interface Animation Back-End Animation

 

✨ Key Features

taipy_github_scenario taipy_github_scenarios_video

taipy-github-optimized

 

Our Valueable Contributors 🌟❤️

⚙️ Quickstart

To install the Taipy stable release run:

pip install taipy

To install Taipy on a Conda Environment or from a source, please refer to the Installation Guide.
To get started with Taipy, please refer to the Getting Started Guide.

 

🔌 Scenario and Data Management

Let's create a scenario in Taipy that allows you to filter movie data based on your chosen genre.
This scenario is designed as a straightforward pipeline.
Every time you change your genre selection, the scenario runs to process your request.
It then displays the top seven most popular movies in that genre.


⚠️ Keep in mind, in this example, we're using a very basic pipeline that consists of just one task. However,
Taipy is capable of handling much more complex pipelines 🚀


Below is our filter function. This is a typical Python function and it's the only task used in this scenario.

def filter_genre(initial_dataset: pd.DataFrame, selected_genre):
    filtered_dataset = initial_dataset[initial_dataset['genres'].str.contains(selected_genre)]
    filtered_data = filtered_dataset.nlargest(7, 'Popularity %')
    return filtered_data

This is the execution graph of the scenario we are implementing

Taipy Studio

You can use the Taipy Studio extension in Visual Studio Code to configure your scenario with no code
Your configuration is automatically saved as a TOML file.
Check out Taipy Studio Documentation

For more advanced use cases or if you prefer coding your configurations instead of using Taipy Studio,
Check out the movie genre demo scenario creation with this Demo.

TaipyStudio

 

User Interface Generation and Scenario & Data Management

This simple Taipy application demonstrates how to create a basic film recommendation system using Taipy.
The application filters a dataset of films based on the user's selected genre and displays the top seven films in that genre by popularity. Here is the full code for both the front-end and back-end of the application.

import taipy as tp
import pandas as pd
from taipy import Config, Scope, Gui

# Defining the helper functions

# Callback definition - submits scenario with genre selection
def on_genre_selected(state):
    scenario.selected_genre_node.write(state.selected_genre)
    tp.submit(scenario)
    state.df = scenario.filtered_data.read()

## Set initial value to Action
def on_init(state):
    on_genre_selected(state)

# Filtering function - task
def filter_genre(initial_dataset: pd.DataFrame, selected_genre):
    filtered_dataset = initial_dataset[initial_dataset["genres"].str.contains(selected_genre)]
    filtered_data = filtered_dataset.nlargest(7, "Popularity %")
    return filtered_data

# The main script
if __name__ == "__main__":
    # Taipy Scenario & Data Management

    # Load the configuration made with Taipy Studio
    Config.load("config.toml")
    scenario_cfg = Config.scenarios["scenario"]

    # Start Taipy Orchestrator
    tp.Orchestrator().run()

    # Create a scenario
    scenario = tp.create_scenario(scenario_cfg)

    # Taipy User Interface
    # Let's add a GUI to our Scenario Management for a full application

    # Get the list of genres
    genres = [
        "Action", "Adventure", "Animation", "Children", "Comedy", "Fantasy", "IMAX"
        "Romance", "Sci-FI", "Western", "Crime", "Mystery", "Drama", "Horror", "Thriller", "Film-Noir", "War", "Musical", "Documentary"
    ]

    # Initialization of variables
    df = pd.DataFrame(columns=["Title", "Popularity %"])
    selected_genre = "Action"

    # User interface definition
    my_page = """
# Film recommendation

## Choose your favorite genre
<|{selected_genre}|selector|lov={genres}|on_change=on_genre_selected|dropdown|>

## Here are the top seven picks by popularity
<|{df}|chart|x=Title|y=Popularity %|type=bar|title=Film Popularity|>
    """

    Gui(page=my_page).run()

And the final result:

 

⚒️ Contributing

Want to help build Taipy? Check out our Contributing Guide.

🪄 Code of Conduct

Want to be part of the Taipy community? Check out our Code of Conduct.

Contribution Guidelines

🪪 License

Copyright 2021-2024 Avaiga Private Limited

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at (Apache License)http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

jrobinAV commented 1 month ago

The contributors are already displayed on the GitHub home page, so I don't see any reason to overload the README with them.

I would close this issue as not planned. @RymMichaut @FabienLelaquais Do you agree?

RymMichaut commented 1 month ago

I totally agree, the list is already displayed. The réadmets aim is to explain the product.

arnab825 commented 1 month ago

Member

ok

arnab825 commented 1 month ago

Just tell me which issue are you facing right now ? Can you provide me the link and screenshot?

FabienLelaquais commented 1 month ago

The contributors are already displayed on the GitHub home page, so I don't see any reason to overload the README with them.

I would close this issue as not planned. @RymMichaut @FabienLelaquais Do you agree?

Totally. Thanks