The Book Reading Bot (brb) programmatically flips through physical books, and using TTS reads the pages aloud. There are also options to download the pdf or audiobook.

I read an article on [The Spectator](http://columbiaspectator.com/) how some low-income students cannot afford textbooks, and actually spend time at the library manually scanning the books on their phones. I realized this was a perfect opportunity for technology to help people and eliminate repetitive tasks. All you do is click start on the web app and the software and hardware do the rest!

Another use case is for young children who do not know how to read yet. Using brb, they can read Dr. Seuss alone! As kids nowadays spend too much time on the television, I hope this might lure kids back to children books.

On a high level technical overview, the web app (bootstrap) sends an image to a flask server which uses ocr and tts.
## Inspiration

As international students, we often have to navigate around a lot of roadblocks when it comes to receiving money from back home for our tuition.
 Cross-border payments are gaining momentum with so many emerging markets. In 2021, the top five recipient countries for remittance inflows in current USD were India (89 billion), Mexico (54 billion), China (53 billion), the Philippines (37 billion), and Egypt (32 billion). The United States was the largest source country for remittances in 2020, followed by the United Arab Emirates, Saudi Arabia, and Switzerland.

However, Cross-border payments face 5 main challenges: cost, security, time, liquidity & transparency.

* Cost: Cross-border payments are typically costly due to costs involved such as currency exchange costs, intermediary charges, and regulatory costs.
-Time: most international payments take anything between 2-5 days.
-Security: The rate of fraud in cross-border payments is comparatively higher than in domestic payments because it's much more difficult to track once it crosses the border.
* Standardization: Different countries tend to follow a different set of rules & formats which make cross-border payments even more difficult & complicated at times.
* Liquidity: Most cross-border payments work on the pre-funding of accounts to settle payments; hence it becomes important to ensure adequate liquidity in correspondent bank accounts to meet payment obligations within cut-off deadlines.

## What it does

Cashflow is a solution to all of the problems above. It provides a secure method to transfer money overseas. It uses the checkbook.io API to verify users' bank information, and check for liquidity, and with features such as KYC, it ensures security in enabling instant payments. Further, it uses another API to convert the currencies using accurate, non-inflated rates.

Sending money:

Our system requests a few pieces of information from you, which pertain to the recipient. After having added your bank details to your profile, you will be able to send money through the platform.

The recipient will receive an email message, through which they can deposit into their account in multiple ways.

Requesting money:
By requesting money from a sender, an invoice is generated to them. They can choose to send money back through multiple methods, which include credit and debit card payments.

## How we built it

We built it using HTML, CSS, and JavaScript. We also used the Checkbook.io API and exchange rate API.

## Challenges we ran into

Neither of us is familiar with backend technologies or react. Mihir has never worked with JS before and I haven't worked on many web dev projects in the last 2 years, so we had to engage in a lot of learning and refreshing of knowledge as we built the project which took a lot of time.

## Accomplishments that we're proud of

We learned a lot and built the whole web app as we were continuously learning. Mihir learned JavaScript from scratch and coded in it for the whole project all under 36 hours.

## What we learned

We learned how to integrate APIs in building web apps, JavaScript, and a lot of web dev.

## What's next for CashFlow

We were having a couple of bugs that we couldn't fix, we plan to work on those in the near future.
## Inspiration

The inspiration for the project was our desire to make studying and learning more efficient and accessible for students and educators. Utilizing advancements in technology, like the increased availability and lower cost of text embeddings, to make the process of finding answers within educational materials more seamless and convenient.

## What it does

Wise Up is a website that takes many different types of file format, as well as plain text, and separates the information into "pages". Using text embeddings, it can then quickly search through all the pages in a text and figure out which ones are most likely to contain the answer to a question that the user sends. It can also recursively summarize the file at different levels of compression.

## How we built it

With blood, sweat and tears! We used many tools offered to us throughout the challenge to simplify our life. We used Javascript, HTML and CSS for the website, and used it to communicate to a Flask backend that can run our python scripts involving API calls and such. We have API calls to openAI text embeddings, to cohere's xlarge model, to GPT-3's API, OpenAI's Whisper Speech-to-Text model, and several modules for getting an mp4 from a youtube link, a text from a pdf, and so on.

## Challenges we ran into

We had problems getting the backend on Flask to run on a Ubuntu server, and later had to instead run it on a Windows machine. Moreover, getting the backend to communicate effectively with the frontend in real time was a real challenge. Extracting text and page data from files and links ended up taking more time than expected, and finally, since the latency of sending information back and forth from the front end to the backend would lead to a worse user experience, we attempted to implement some features of our semantic search algorithm in the frontend, which led to a lot of difficulties in transferring code from Python to Javascript.

## Accomplishments that we're proud of

Since OpenAI's text embeddings are very good and very new, and we use GPT-3.5 based on extracted information to formulate the answer, we believe we likely equal the state of the art in the task of quickly analyzing text and answer complex questions on it, and the ease of use for many different file formats makes us proud that this project and website can be useful for so many people so often. To understand a textbook and answer questions about its content, or to find specific information without knowing any relevant keywords, this product is simply incredibly good, and costs pennies to run. Moreover, we have added an identification system (users signing up with a username and password) to ensure that a specific account is capped at a certain usage of the API, which is at our own costs (pennies, but we wish to avoid it becoming many dollars without our awareness of it.

## What we learned

As time goes on, not only do LLMs get better, but new methods are developed to use them more efficiently and for greater results. Web development is quite unintuitive for beginners, especially when different programming languages need to interact. One tool that has saved us a few different times is using json for data transfer, and aws services to store Mbs of data very cheaply. Another thing we learned is that, unfortunately, as time goes on, LLMs get bigger and so sometimes much, much slower; api calls to GPT3 and to Whisper are often slow, taking minutes for 1000+ page textbooks.

## What's next for Wise Up

What's next for Wise Up is to make our product faster and more user-friendly. A feature we could add is to summarize text with a fine-tuned model rather than zero-shot learning with GPT-3. Additionally, a next step is to explore partnerships with educational institutions and companies to bring Wise Up to a wider audience and help even more students and educators in their learning journey, or attempt for the website to go viral on social media by advertising its usefulness. Moreover, adding a financial component to the account system could let our users cover the low costs of the APIs, aws and CPU running Whisper.