## Inspiration

There are a lot of text-to-image, image-to-text AI generators, only a few image-to-video AI generators, but from our research, we couldn't find an AI generator that converted live images to video (.gif). We wanted to fill this void, since it has a few useful applications, and so we created Fleurish!

## What it does

When the user inputs an image, selects a theme and clicks the "Imagine" button, an AI-generated gif based on the inputted image will be generated after a few seconds. Alternatively, if the user connects their phone to their computer, they can also use the "live image" option to get the application to read images from the phone camera video in real time.

## How we built it

We used a Convex template with a built-in Replicate integration as our backend, and then added on a Replicate [https://replicate.com/lucataco/dreamshaper7-img2img-lcm](model) in order to convert images to AI-generated images. For the live image option, we used RTC peer connections to send camera feed to a Python backend, which then compresses the image (to optimize the AI generation) and feeds to the front-end, built in Next.js with the WebSocket API.

## Challenges we ran into

We spent a lot of time testing and searching for a model that gave us satisfactory results, and even then, it took us time to learn how to integrate that with our application. Also, we did not have previous experience with a lot of the technologies used, namely generative AI (with images), WebSocket API, and using the RTC peer connection to allow the communication between a phone and our application.

## Accomplishments that we're proud of

Our application is highly optimized and generates the gif within 1-2 seconds. Also, the themes and the gifs produced give very accurate results.

## What we learned

We learned a lot of new technologies, such as Convex, WebSocket API and RTC peer connection. We also gained experience with image generative AI, which is vastly different than text generative AI. Also, learning to integrate all of the technologies used together was a very insightful learning experience.

## What's next for Fleurish

Better, faster models, user management (so there is no overlap between users).
## Inspiration

* As ardent content consumers, we relish exploring links from influencers and content creators. However, the sheer volume of links often makes finding the right one a daunting task.
* For example, a Sephora beauty product can be buried under a maze of 30+ links, each with vague and unhelpful titles.
* Our conversations with aspiring content creators echoed this frustration. They lamented the cookie-cutter sameness of everyone's link trees, which stifled their creativity and self-expression due to the lack of personalization.
* We aspired to elevate this experience, not only for influencers but also for their discerning audience.

## What it does

* Flink simplifies domain creation while preserving the freedom of personalization, just like other link platforms.
* How do we do it? We harness the power of AI to streamline the process and add a touch of uniqueness.
Creators can share a few key details, like their profession and brand colors, to craft a personalized site that truly represents them.
* But here's where Flink shines: We ask creators for three words that describe them, and we use this to create a one-of-a-kind background image. Imagine 'Gamer, Minecraft, Streamer' conjuring up a techno-inspired image with Minecraft blocks!
* Perhaps our most valuable feature is the ability to add MEANINGFUL links. Many creators need to showcase products, but navigating to them can be a hassle. With Flink, simply provide the product link, and we'll do the rest – image, accurate title, and a professional yet concise description that markets it effectively.
* After registration, creators have full control. They can add or remove links, tweak content, and toggle between creator and viewer perspectives on their dashboard. Plus, we provide analytics and data insights for creators really understand the trends in their audience.

## How we built it

* We used **React** and **Javascript** for the frontend
* **Node.js, Express, and MongoDB** for backend and storage
* We used **Cohere** for text generation and **OpenAI** for image generation

## Challenges we ran into

* Testing Cohere and OpenAI prompts posed an interesting challenge. We aimed for optimal product descriptions and images while using minimal tokens and context. For instance, generating a background image from just 3 words could result in bizarre, unsuitable images without a precise prompt. Our solution? Crafting prompts like 'Generate a very minimalistic background for...' to refine results.
* Designing the domain creation experience was another puzzle. We needed to strike a balance: collect enough user data for personalization without overwhelming creators. Prolonged surveys would mimic tedious and overwhelming website creation sites. So we really had to come to a consensus on what the necessary info we should collect from them was, and find innovative solutions.

## Accomplishments that we're proud of

* We are very proud of being able to succesfully use the Cohere and OpenAI APIs and use them effectively. We have never implemented AI technology in an application before, so this was a huge milestone for each of us, as this was a foot in the door to the future.
* Each of us had our own skills and weaknesses, and we were able to try new things that maybe we weren't the best at comfortably because of how helpful and resourceful we were.

## What's next for Flink

* Making the site more secure, such as two-factor authentication because of the sensitive nature of the information of the famous people that would potentially use it.
* Ideally, each generated page should have its own generated short link. We did not have enough time to implement this feature, but it would be a very useful feature to have.
* More personalization! With the growing power of AI, there is so much we can do and create with AI, and so there is a lot more features that Flink can implement in the future!
## **Inspiration:**

Our inspiration stemmed from the realization that the pinnacle of innovation occurs at the intersection of deep curiosity and an expansive space to explore one's imagination. Recognizing the barriers faced by learners—particularly their inability to gain real-time, personalized, and contextualized support—we envisioned a solution that would empower anyone, anywhere to seamlessly pursue their inherent curiosity and desire to learn.

## **What it does:**

Our platform is a revolutionary step forward in the realm of AI-assisted learning. It integrates advanced AI technologies with intuitive human-computer interactions to enhance the context a generative AI model can work within. By analyzing screen content—be it text, graphics, or diagrams—and amalgamating it with the user's audio explanation, our platform grasps a nuanced understanding of the user's specific pain points. Imagine a learner pointing at a perplexing diagram while voicing out their doubts; our system swiftly responds by offering immediate clarifications, both verbally and with on-screen annotations.

## **How we built it**:

We architected a Flask-based backend, creating RESTful APIs to seamlessly interface with user input and machine learning models. Integration of Google's Speech-to-Text enabled the transcription of users' learning preferences, and the incorporation of the Mathpix API facilitated image content extraction. Harnessing the prowess of the GPT-4 model, we've been able to produce contextually rich textual and audio feedback based on captured screen content and stored user data. For frontend fluidity, audio responses were encoded into base64 format, ensuring efficient playback without unnecessary re-renders.

## **Challenges we ran into**:

Scaling the model to accommodate diverse learning scenarios, especially in the broad fields of maths and chemistry, was a notable challenge. Ensuring the accuracy of content extraction and effectively translating that into meaningful AI feedback required meticulous fine-tuning.

## **Accomplishments that we're proud of**:

Successfully building a digital platform that not only deciphers image and audio content but also produces high utility, real-time feedback stands out as a paramount achievement. This platform has the potential to revolutionize how learners interact with digital content, breaking down barriers of confusion in real-time. One of the aspects of our implementation that separates us from other approaches is that we allow the user to perform ICL (In Context Learning), a feature that not many large language models don't allow the user to do seamlessly.

## **What we learned**:

We learned the immense value of integrating multiple AI technologies for a holistic user experience. The project also reinforced the importance of continuous feedback loops in learning and the transformative potential of merging generative AI models with real-time user input.