Best AI tools for< Try Hairstyles >

Hairstyle AI is a virtual AI hairstyle try-on tool that allows users to experiment with new haircuts virtually before making a real-life change. With over 155,760 AI-generated hairstyles created for 1,298 happy customers, Hairstyle AI offers a wide variety of styles for both male and female users. The platform uses AI technology to provide users with personalized hairstyle recommendations, helping them feel more confident in their appearance without the need for any actual haircut. Users can upload their selfies, explore different hairstyles, and download their favorites to try out different looks effortlessly.

My Perfect Hairstyle is an AI-powered tool that helps users find their perfect hairstyle. By utilizing advanced artificial intelligence algorithms, the application analyzes facial features and suggests hairstyles that best suit the user's unique characteristics. Users can experiment with different styles virtually before making a decision, saving time and effort. Whether you're looking for a new haircut, color, or style, My Perfect Hairstyle provides personalized recommendations tailored to your preferences.

Try On Hairstyles is a website that allows users to try on different hairstyles using artificial intelligence. Users can upload a photo of themselves and then choose from a variety of hairstyles to see how they would look. The website also offers a variety of hair care tips and advice.

AI Hairstyles is a website that allows users to try on different hairstyles and colors using artificial intelligence. Users can upload a selfie and choose from a variety of styles and colors to see how they would look with a new hairstyle. The website also offers a privacy-first approach, deleting user images after 30 days of inactivity.

AI Hairstyle is an AI-powered online platform that offers a virtual hairstyle transformation experience. Users can explore various hairstyles and hair colors using the AI hairstyle generator and color changer. The platform provides personalized hairstyle suggestions and allows users to try on different looks virtually. With features like free storage, users can save their generated images in the cloud for easy access. AI Hairstyle aims to make experimenting with new hairstyles effortless and fun.

Facetune is an AI-powered photo and video editing application that offers a wide range of tools for enhancing, reshaping, and creatively transforming digital content. It provides one-tap editing features to bring out the best in photos and videos, making it a go-to choice for influencers, creators, and anyone looking to elevate their social media presence. With intuitive tools for beginners and professionals alike, Facetune allows users to edit photos and videos on-the-go, ensuring dynamic and varied content for every creative journey.

BrideLook AI is an AI-powered application designed to help users explore and design their dream bridal hairstyles instantly. By analyzing the user's facial features, the app suggests unique bridal hairstyles that accentuate their natural beauty, eliminating the need for endless salon trials. Users can upload or take a selfie, choose from a selection of hairstyles, view them from multiple angles, and download their favorite in high resolution. The app simplifies the process of finding the perfect bridal hairstyle, making wedding preparation fun and stress-free.

Curlyhair AI is an AI-powered tool that allows users to see how they would look with curly hair. It uses advanced AI algorithms to generate realistic images of users with different curly hairstyles. The tool is easy to use and can be accessed from any device with an internet connection. Users simply need to upload a photo of themselves and select the desired hairstyle. The tool will then generate four variations of the user's photo with different curly hairstyles. The results can be downloaded or shared on social media.

Perfect365 is an AI makeup application that allows users to virtually try on makeup and hairstyles through advanced augmented reality technology. With over 100 million users, the app offers a seamless way to experiment with different looks, acting as a personal beauty assistant. Users can adjust every aspect of their appearance, from skin tone to eye color, all while maintaining a natural and realistic look. The app employs artificial intelligence algorithms to let users experiment with different makeup looks virtually, without the need for physical products. Perfect365 is a pioneer in the beauty apps sector, providing users with a transformative experience in exploring e-cosmetics.

Photify AI is an AI-powered application that generates unique and creative photos using artificial intelligence technology. Users can create stunning images by simply uploading a selfie, and the app transforms it into various styles such as artwork, games, cartoons, anime, retro, movies, and more. With a wide range of packs to explore, Photify AI offers a fun and easy way to enhance photos and unleash creativity.

VirtualFit is an AI application that allows users to change their style within seconds using advanced AI technology. Users can upload their photo, specify what they want to change (e.g., clothes, hairstyle), and let the powerful AI algorithm do the rest. VirtualFit offers a range of features such as outfit replacement, image restoration, generative fill, object recoloring, and background removal. The application is designed to provide users with an affordable and user-friendly solution for enhancing their photos without the need for complex editing software like Photoshop.

Pyxer is an AI fashion application that helps users find the right clothes, jewelry, hairstyles, and portraits effortlessly. It acts as a personal stylist, fashion photographer, and shopping assistant, utilizing artificial intelligence to generate outfits and enhance photos. Pyxer values user privacy and offers personalized recommendations based on individual preferences. Users can explore the latest trends, experiment with different looks, and create stunning portraits with ease. The application aims to simplify the fashion discovery process and provide a unique shopping experience tailored to each user's style.

VOLV is an AI application that enhances the shopping experience by providing personalized product suggestions based on individual's facial/body features. It offers recommendations for eyewear, jewelry, makeup, personal grooming, and apparel, transforming the online shopping experience across various industries. Additionally, VOLV introduces Spatial Technology, allowing customers to engage with products in hyper-realistic 3D interactive lines and try products virtually before buying. The application prioritizes privacy and security, ensuring encrypted end-to-end experiences.

Online AI Baby Generator is an AI application that predicts the appearance of a future child based on the facial features of the parents. It uses advanced algorithms to analyze parental photos, extract facial features, and combine them statistically to render the future child's appearance. The tool respects genetic inheritance, ensures privacy by encrypting and erasing user photos, and offers continuous innovation with features like changing hairstyles and clothes for future children. Users can upload parental photos, select gender and photo dimensions, and receive the prediction within minutes. The tool is designed for entertainment purposes and not for medical or genetic analysis.

Metapix is a leading company in AI-powered image editing solutions, helping businesses enhance visuals efficiently and maintain quality with innovative technology. Metapix offers a suite of AI-powered tools for the beauty industry, including virtual makeup try-on, virtual nail try-on, virtual try-on glasses, hair color and style try-on, and men's beard dye try-on. These tools allow businesses to provide their customers with a more engaging and personalized shopping experience.

Try it on AI is a professional AI photography studio that offers stunning AI headshots and portraits for various purposes such as LinkedIn, resume, acting & modeling portfolios, and social media. The platform provides high-quality AI headshots tailored to individual needs, with features like Express AI for quick headshots, Creative Studio for custom portraits, and Edit Your Own Tools for image enhancement. Try it on AI is widely used by professionals worldwide to meet their portrait needs, offering a seamless experience for creating on-brand portraits for teams and individuals across different industries.

Try it on AI is an online platform that uses artificial intelligence to generate professional-quality headshots and portraits. The platform offers a variety of tools to meet all your portrait needs, including Express AI for quick AI Headshots, Creative Studio to create custom portraits, and Edit Your Own Tools to enhance your own images. Try it on AI is perfect for individuals, teams, and businesses of all sizes. With over 500,000+ professionals around the world using Try it on AI, you can be sure that you're getting the best possible results.

Kolors Virtual Try On is an AI-powered fashion fitting tool that allows users to virtually try on clothing and accessories. By uploading images of themselves and the garments they want to try, users can quickly generate realistic virtual try-on results. The cutting-edge AI technology behind Kolors Virtual Try On ensures fast and accurate processing, providing users with a diverse range of clothing options to experiment with. The tool offers a secure platform with advanced encryption to protect user privacy, making it a convenient and reliable solution for exploring different fashion styles.

Why Try AI is a hands-on, no-hype guide to generative AI, providing valuable insights and practical tips for individuals interested in understanding and utilizing AI technologies. With over 6,000 subscribers, the newsletter offers a well-written and hype-free approach to AI, catering to a wide audience of normal folk looking to explore the world of artificial intelligence. The content covers various AI tools, offering readers the opportunity to learn and apply new techniques in their projects and endeavors. By registering on the website, users gain access to a wealth of information and resources related to AI, presented in an engaging and accessible format.

QuillBot is an AI-powered paraphrasing tool that helps students and professionals rewrite, edit, and change the tone of their text to improve clarity and comprehension. It offers various features such as customization, synonym suggestions, and integrations with other writing tools. QuillBot's paraphrasing tool is designed to enhance writing fluency, vocabulary, tone, and style, making it suitable for a wide range of writing tasks.

ai-collective-tools is an open-source community dedicated to creating a comprehensive collection of AI tools for developers, researchers, and enthusiasts. The repository provides a curated selection of AI tools and resources across various categories such as 3D, Agriculture, Art, Audio Editing, Avatars, Chatbots, Code Assistant, Cooking, Copywriting, Crypto, Customer Support, Dating, Design Assistant, Design Generator, Developer, E-Commerce, Education, Email Assistant, Experiments, Fashion, Finance, Fitness, Fun Tools, Gaming, General Writing, Gift Ideas, HealthCare, Human Resources, Image Classification, Image Editing, Image Generator, Interior Designing, Legal Assistant, Logo Generator, Low Code, Models, Music, Paraphraser, Personal Assistant, Presentations, Productivity, Prompt Generator, Psychology, Real Estate, Religion, Research, Resume, Sales, Search Engine, SEO, Shopping, Social Media, Spreadsheets, SQL, Startup Tools, Story Teller, Summarizer, Testing, Text to Speech, Text to Image, Transcriber, Travel, Video Editing, Video Generator, Weather, Writing Generator, and Other Resources.

llamafile is a tool that enables users to distribute and run Large Language Models (LLMs) with a single file. It combines llama.cpp with Cosmopolitan Libc to create a framework that simplifies the complexity of LLMs into a single-file executable called a 'llamafile'. Users can run these executable files locally on most computers without the need for installation, making open LLMs more accessible to developers and end users. llamafile also provides example llamafiles for various LLM models, allowing users to try out different LLMs locally. The tool supports multiple CPU microarchitectures, CPU architectures, and operating systems, making it versatile and easy to use.

Haystack is an open-source framework for building production-ready LLM applications, retrieval-augmented generative pipelines, and state-of-the-art search systems that work intelligently over large document collections. It lets you quickly try out the latest models in natural language processing (NLP) while being flexible and easy to use.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

The Latent Browser is a desktop application designed like a web browser, which hallucinates web search results (the resultds are fictious and are generated by a LLM) and web pages. It is a web application designed to run locally on your machine and is 99% React, Tailwind, TypeScript, and NextJS. The runtime is Tauri, which is written in Rust. The Latent Browser is still under development and some things may be broken when you try it.

Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple. Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to: make integration of hardware and software easy by supporting Python, C, C++, and also ROS2. make communication low latency by using zero-copy Arrow messages. dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval. The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

HuixiangDou is a **group chat** assistant based on LLM (Large Language Model). Advantages: 1. Design a two-stage pipeline of rejection and response to cope with group chat scenario, answer user questions without message flooding, see arxiv2401.08772 2. Low cost, requiring only 1.5GB memory and no need for training 3. Offers a complete suite of Web, Android, and pipeline source code, which is industrial-grade and commercially viable Check out the scenes in which HuixiangDou are running and join WeChat Group to try AI assistant inside. If this helps you, please give it a star ⭐

Vespa documentation is served using GitHub Project pages with Jekyll. To edit documentation, check out and work off the master branch in this repository. Documentation is written in HTML or Markdown. Use a single Jekyll template _layouts/default.html to add header, footer and layout. Install bundler, then $ bundle install $ bundle exec jekyll serve --incremental --drafts --trace to set up a local server at localhost:4000 to see the pages as they will look when served. If you get strange errors on bundle install try $ export PATH=“/usr/local/opt/[email protected]/bin:$PATH” $ export LDFLAGS=“-L/usr/local/opt/[email protected]/lib” $ export CPPFLAGS=“-I/usr/local/opt/[email protected]/include” $ export PKG_CONFIG_PATH=“/usr/local/opt/[email protected]/lib/pkgconfig” The output will highlight rendering/other problems when starting serving. Alternatively, use the docker image `jekyll/jekyll` to run the local server on Mac $ docker run -ti --rm --name doc \ --publish 4000:4000 -e JEKYLL_UID=$UID -v $(pwd):/srv/jekyll \ jekyll/jekyll jekyll serve or RHEL 8 $ podman run -it --rm --name doc -p 4000:4000 -e JEKYLL_ROOTLESS=true \ -v "$PWD":/srv/jekyll:Z docker.io/jekyll/jekyll jekyll serve The layout is written in denali.design, see _layouts/default.html for usage. Please do not add custom style sheets, as it is harder to maintain.

STORM is a LLM system that writes Wikipedia-like articles from scratch based on Internet search. While the system cannot produce publication-ready articles that often require a significant number of edits, experienced Wikipedia editors have found it helpful in their pre-writing stage. **Try out our [live research preview](https://storm.genie.stanford.edu/) to see how STORM can help your knowledge exploration journey and please provide feedback to help us improve the system 🙏!**

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

Databend is an open-source cloud data warehouse built in Rust, offering fast query execution and data ingestion for complex analysis of large datasets. It integrates with major cloud platforms, provides high performance with AI-powered analytics, supports multiple data formats, ensures data integrity with ACID transactions, offers flexible indexing options, and features community-driven development. Users can try Databend through a serverless cloud or Docker installation, and perform tasks such as data import/export, querying semi-structured data, managing users/databases/tables, and utilizing AI functions.

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=