thedatatribune / dypixa Goto Github PK
View Code? Open in Web Editor NEWTurning words into lively shades!
Home Page: https://thedatatribune.github.io/dyPixa/
License: MIT License
Turning words into lively shades!
Home Page: https://thedatatribune.github.io/dyPixa/
License: MIT License
We need an NLP model to process English short poems and extract sentiments, emphasizing metaphor recognition for accurate sentiment analysis.
This issue involves enhancing error handling in the dyPixa
project's code (primarily Python). Effective error handling is essential for application stability and user experience.
Contributors are invited to improve dyPixa's error management, enhancing application reliability. Your contributions will elevate the project's quality. ππ οΈ
This project requires an NLP model trained on a poetry dataset, encompassing different languages with a current focus on English and Hindi. The dataset should meet the following constraints:
For longer poems, consider the following contributions:
Note:
Issue Title: Documentation
Description:
π Overview
It's crucial to ensure that the documentation of dyPixa remains comprehensive, up-to-date, and user-friendly. Documentation serves as the gateway for contributors and users to understand and utilize the full potential of dyPixa. This issue is dedicated to improving our project's documentation across various aspects.
π Tasks
User Guide Enhancement: Expand and refine the user guide to provide clear, step-by-step instructions for using dyPixa. Include examples and best practices for generating abstract images, utilizing the color harmony model, and best practices to provide the inputs.
Contributor Guide Update: Ensure our contributor guide is concise and offers a smooth onboarding experience for new contributors. Explain our development workflow, coding standards, and how to submit contributions effectively.
API Documentation: We aim to develop APIs for each of the four main sub-parts/features of dyPixa as we progress. Simultaneously, we will document the project's API, detailing functions and integration endpoints, along with usage examples for developers implementing dyPixa in their applications.
Installation Instructions: Improve the installation guide to cover different platforms and deployment scenarios. Ensure it is beginner-friendly and includes troubleshooting tips.
FAQ Section: Create a Frequently Asked Questions (FAQ) section to address common queries and issues users and contributors may encounter.
π οΈ Expected Outcome
By addressing this documentation issue, we aim to provide a more accessible and informative resource for the community. This will empower both users and contributors to make the most of dyPixa's capabilities, fostering a stronger and more vibrant project ecosystem.
π Note
If you choose to work on this issue, please fork the repository, create a new branch for your changes, and submit a pull request once your updates are ready. Your contributions to improving our documentation are highly valued and appreciated! π
Let's make dyPixa even more accessible and user-friendly together! ππ
It's time to ensure our project's dependencies are up-to-date. Let's update the requirements.txt
file to keep our codebase fresh and reliable. Your contribution to this essential maintenance task is greatly appreciated! π
Note: Do ensure that you take into account the requirements of all available Python code, which can be found in the code/
directory across various subdirectories.
This issue is one of the great starting point for the beginners in opensource community, here you can:
In the context of the dyPixa
project, this task revolves around the crucial need to gather and comprehensively document datasets for training and testing the machine learning models. This issue addresses the following key aspects:
python
(preferred) code for sourcing diverse datasets. This may include acquiring text data from social media, product reviews, and news articles, and images with associated sentiments from public image repositories.Types of Data Needed:
For the NLP and color suggestion models to be highly usable and effective, the following types of data should be considered:
Text Data:
Image Data:
By addressing these components and collecting the appropriate types of data, this issue will lay the foundation for robust machine learning model development and further enhancements in the dyPixa
project. Your contributions here will greatly advance the project's capabilities. ππ
dyPixa
, our innovative project at the intersection of computer vision, machine learning, and natural language processing, is looking for a unique and captivating logo to represent its identity. We believe that a visually appealing logo can truly capture the essence of our project and make a lasting impression.
π― What We're Looking For
We're open to your creative genius! Whether it's abstract art, a clever combination of letters, or a symbol that represents our project's mission, we're excited to see your ideas. Here are some key guidelines:
dyPixa
.π‘ How to Participate
logo/
.π Let's Get Creative
Together, we can make dyPixa visually unforgettable. We're excited to see your designs and grateful for your contributions to our project's identity. π
Thank you for being part of the dyPixa community! π
Note: π If you have any questions or need further guidance on this issue, join us at Discord.
Developing an open-source web app, dyPixa, powered by ML and LLMs. APIs enable color scheme recommendations and dynamic image generation (Issue #4).
We aim to address various UI needs:
Text-Based Color Recommendation π
Text-to-Image Conversion ππΌοΈ
Color-to-Image Generation π¨πΌοΈ
dyPixa
and ML Models π€This project relies heavily on creating, deploying, and utilizing various machine learning models, primarily for:
This issue focuses on developing new ML and NLP models and associated code for:
Contributors are encouraged to tackle related sub-problems while addressing issue #4. π‘π§
Note: π The necessary information related to the data to build/test these models could be found in issue #6. It is appreciated to contribute with data, as well.
Our Quest: Let's supercharge dyPixa's creative potential! We're on a mission to build a vibrant dataset that connects emotions to colors, and we need your help to make it happen. π
Emotional Imagery: We're looking for images or thumbnails that express each of the 28 unique emotions. These visuals will breathe life into our dataset.
Color Splash: For each emotion, we want to collect a colorful array of at least 5 hues. These will be the building blocks of our dynamic color palettes.
Shades of Emotion: Remember, there's a world of color sets that can convey the same emotion, so feel free to explore the possibilities.
Organized Magic: Treat each color set that aligns with a specific emotion as a distinct data entry.
Effortless Extraction: To make your contribution smoother, you can use this handy script by @thevectorguy to automatically fetch colors from your provided images or thumbnails.
Let's Paint the World with Emotions!
By contributing to this dataset, you're not only adding color to dyPixa's palette but also unleashing a world of creative emotions. Join us in this colorful journey and be a part of the dyPixa magic! π¨β¨
Ready to dive in? Contribute to the dyPixa Emotion Dataset and let's make emotions burst with life through colors! π
Note: Questions or need guidance? You can join dyPixa's lively community at Discord for further discussions and queries. Your contributions mean the world to us! π
dyPixa's project goals is to enable the dynamic overlay of text onto generated images, fostering the creation of emotionally expressive compositions. This task is a crucial element within our broader project.
As you work on this issue, please consider the following constraints to ensure the best results:
This issue is a key step in bringing emotion and creativity to life through text and images. Your contributions are greatly appreciated as we work together to make dyPixa truly extraordinary. ππ
This issue aims to create a Docker container for the solution provided in #30 within the dyPixa
project. It addresses the requirement of developing a user-friendly front-end interface for users to input text, transmitting this input to the backend/API, and receiving color shade recommendations in hexadecimal form (#FFFFFF for white) as the response.
π³ Create Docker Container:
π¦ Dependency Management:
π CI/CD Integration:
Dockerfile
and docker-compose.yml
.π§ͺ Testing and Documentation:
We need a simple Python program to extract dominant colors from an input image (provided as a file path) and present them in hex format (e.g., white = #FFFFFF).
Note: π Solving this issue will contribute to the advancement of #6.
We require a user-friendly front-end interface π₯οΈ to facilitate the user with a form for inputting text. This text input will be transmitted to the backend/API, and the received response should consist of a set of color shades π¨ (in hexadecimal form, e.g., #FFFFFF for white).
Your contributions to this feature will enhance the user experience by providing dynamic color recommendations, enriching the functionality of the dyPixa
project. ππ
NOTE: Refer to Issue #5 for more details.
The following changes are requested on #59 to integrate the solution with UI:
models
to the code
directory.models
directory.Convert both train and test data in a uniform format (either in Excel or CVS only).
A UI is required to integrate the emotion classification model based on go-emotions
, which is provided in the ml_nlp
directory.
The UI should include the following components:
findEmotions()
function.Our mission is clear: we need to build a comprehensive dataset that harmonizes dominant colors with various emotions and feelings. To achieve this, we've outlined a set of reference steps:
ColorExtraction.py
script available within dyPixa's resources to list the vibrant colors.The colors of each image could be stored keeping the MD5 hash of the image as its key. The JSON data can the following structure:
{
"emotions": {
"ImageHash": {
"HEX": [
"shade1",
"shade2",
"....",
"shadeN"
],
"RGB": {
"shade1": ["R", "G", "B"],
"shade2": ["R", "G", "B"],
"...": ["R", "G", "B"],
"shadeN": ["R", "G", "B"]
}
}
}
}
This dataset holds immense potential. We'll harness it to train machine learning models, enabling us to recommend the perfect palette of colors for any given sentiment. π¨π
Let's paint the world with emotions! Your contributions are a stroke of genius. π
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