## Inspiration

Our inspiration was that we wanted to build something that we would want use, and could use immediately after the event ended. The two core principles behind Verify are that we want information fast, and we want to know if it is biased. This means we don't want to leave our browser, don't want to scroll over ads and don't want to lookup every site we go to. We knew that if we could create a project that could fit these parameters, we would (and others as well) would use it daily.

## What it does

Verify is a chrome extension that analysis the current article you are looking at and produces the sites political leaning/bias, article sentiment and a concise summary of the article, only a click away. Previously analyzed site results are cached in chrome to save precious seconds when you visit again (clearable in settings).

## How we built it

Our back end was a **Node** server deployed via **Standard Library** that integrated with the **Google Learning API**, **Article Outliner API** from Diffbot.com, a **NLP summarizer API** from DeepAI.org and <https://mediabiasfactcheck.com/> for the bias analysis by scraping their website (please add an API mediabiasfactcheck!).

Our front end was a classic **Chrome extension** built using **JavaScript**, **CSS**, **HTML** and some non reciprocated love.

The saving grace of our project was **Standard Library**. the smart code editor, automated package imports and installations, and one click deploy saved hours in creating and hosting our analysis endpoint and allowed us to reach our stretch goal of sentiment analysis.

## Challenges we ran into

The biggest challenge we faced was finding a good source for checking website bias. No API's were available, but we found a great, reputable website with thousands of entries so we ended up scraping that site for our results. The usual challenge of setting up and deploying a server without being forced to use certain libraries was removed by **Standard Library's** no hassle deployment

## Accomplishments that we're proud of

We are proud that our project has immediate, practical uses, and that we know we will use this project for months or years to come.

## What we learned

We learned the power of good prototyping tools (**Standard Library**) and gained more experience with **Google Cloud Services** and our first look into **Chrome Extension** development

## What's next for Verify

We plan on using our project ourselves. We will look into a wide release of the extension. As the server costs and API calls are only pennies per user per month, revenue models could include advertisement based, subscription based, or based on a small up front fee (or based on burning VC funding). We can also add quality of life features such as the full article in an easily copyable format and connectivity features.
## Inspiration

There are very small but impactful ways to be eco-conscious 🌱 in your daily life, like using reusable bags, shopping at thrift stores, or carpooling. We know one thing for certain; people love rewards ✨. So we thought, how can we reward people for eco-conscious behaviour such as taking the bus or shopping at sustainable businesses?
We wanted a way to make eco-consciousness simple, cost-effective, rewarding, and accessible to everyone.

## What it does

Ecodes rewards you for every sustainable decision you make. Some examples are: shopping at sustainable partner businesses, taking the local transit, and eating at sustainable restaurants. Simply scanning an Ecode at these locations will allow you to claim EcoPoints that can be converted into discounts, coupons or gift cards to eco-conscious businesses. Ecodes also sends users text-based reminders when acting sustainably is especially convenient (ex. take the bus when the weather is unsafe for driving). Furthermore, sustainable businesses also get free advertising, so it's a win-win for both parties! See the demo [here](https://drive.google.com/file/d/1suT7tPila3rz4PSmoyl42G5gyAwrC_vu/view?usp=sharing).

## How we built it

We initially prototyped UI/UX using Figma, then built onto a React-Native frontend and a Flask backend. QR codes were generated for each business via python and detected using a camera access feature created in React-Native. We then moved on to use the OpenWeatherMaps API and the Twilio API in the backend to send users text-based eco-friendly reminders.

## Challenges we ran into

Implementing camera access into the app and actually scanning specific QR codes that corresponded to a unique business and number of EcoPoints was a challenge. We had to add these technical features to the front-end seamlessly without much effort from the user but also have it function correctly. But after all, there's nothing a little documentation can't solve! In the end, we were able to debug our code and successfully implement this key feature.

## Accomplishments that we're proud of

**Kemi** is proud that she learned how to implement new features such as camera access in React Native. 😙
**Akanksha** is proud that she learnt Flask and interfacing with Google Maps APIs in python. 😁
**Vaisnavi** is proud that she was able to generate multiple QR codes in python, each with a unique function. 😝
**Anna** is proud to create the logistics behind the project and learnt about frontend and backend development. 😎
Everyone was super open to working together as a team and helping one another out. As as a team, we learnt a lot from each other in a short amount of time, and the effort was worth it!

## What we learned

We took the challenge to learn new skills outside of our comfort zone, learning how to add impressive features to an app such as camera access, QR code scanning, counter updates, and aesthetic UI. Our final hack turned out to be better than we anticipated, and inspired us to develop impactful and immensely capable apps in the future :)

## What's next for Ecodes

Probably adding a location feature to send users text-based reminders to the user, informing them that an Ecode is nearby. We can use the Geolocation Google Maps API and Twilio API to implement this. Additionally, we hope to add a carpooling feature which enables users to earn points together by carpooling with one another!!
## Inspiration

Misinformation has become more and more widespread, posing numerous societal and ethical concerns. We have seen these effects first hand, and hoped to help address this issue.

## What it does

It's a chrome extension; you open a sidebar, and it determines if the site contains misinformation.

## How we built it

To initially build the chrome extension, we used a framework called Plasmo. We built the front end in TypeScript and used react, and we built the backend in Python. We trained a logistic regression model using a data set with over 40000 lines of news text classified as true or fake, and then grabbed the URL from the current website and passed it through the Flask backend. Then, we scraped the text from the website and passed it through our machine learning model for analysis, finishing by displaying the results of the model on the sidebar for the user to see. Finally, we sent a request to the OpenAI API to display additional resources for a user to look into if they want more context or information about the topic that they are currently reading about.

## Challenges we ran into

The onSubmit function of the html form was using defaults, which refreshed the page, so we spent a long time figuring out why the information was not going through to the model. We also found it a bit challenging initially because none of us had experience using react or flask.

## Accomplishments that we're proud of

Our model is accurate and the user interface looks very good.

## What we learned

We learned how to make a chrome extension, and connect the backend to the frontend with Flask.

## What's next for Clarif.ai

Give a more accurate description of what exactly is false in the page, and allow users to give feedback to improve our model.