PolicyClarity

Simplifier
Landing page
Simplified Health Policy

Inspiration

One of our teammates families was almost trapped in the scam. At the time, it was just a thought, but after researching the statistics of these frauds and after the announcement from the government to include a summary of policy, we decided to make PolicyClarity.

What it does

PolicyClarity is a free and easy-to-use platform. It basically helps users understand the policies by summarizing the legal and complex documents into a simple, understandable summary, Not only summary it eleminates the need of a lawyer to read the policy. It converts the whole policy into simple and easy to understandable words and phrases so that every one can go through it and understand .

This will help in 3 ways:

First, it’s making the government scheme easier, as it has Summary of the whole policy
Second, it reduces the time that a buyer takes to understand the policy.
Third, as we are providing a summary for each page in the policy, the buyer will easily understand the policy, hence reducing the chances of scams

Overall, it will prevent any false information and fraud that may be conveyed.

How we built it:

We started by setting up our front-end and back-end, and along with that, we started building and training our model. After that, we built the server and then integrated it with our trained model and front-end. The frontend is build using React and the backend was suppose to be built on Node but since we were facing problems to connect node and python's FastAPI . we had to switch to making the backend in Python's FastAPI.

Challenges

We were unable to do anything for 4 straight hours because of the wifi. We worked on Hugging Face to build a model but were not able to make one. Sending the pdf from node to FastAPI was a pain in ass since the data type changes on each platform was tough for us to figure out the best way possible to read the file and then rewrite the easier pdf file. another thing was hosting the FastAPI , we are still trying to figure out a way to host the backend.

Build With

We use Python's FastAPI at the backend and React on the frontend. the Ai Model was built in Python. Generative AI for training the model For frontend, we used React and Tailwind CSS. Libraries we used: easyocr, genai, tailwind, FPDF, pdfplumber, etc.

Accomplishments that we are proud of

It works!!! It does what it is supposed to. It gives the summary as expected, along with all the necessary points, in a very simple way., but this is not what we are proud of , even after been a team of beginners we were able to wake up for straight 27hrs and we dint wasted a single minute, thats what we are proud of :)

What's next for PolicyClarity?

We will be integrating diffrent language models so that every user can see the policy and understand it in there own language the plan is to turn this into a full fledged Non-Profit thing so that we can help out people on a mass scale