## Inspiration:

The inspiration for Kisan Mitra came from the realization that Indian farmers face a number of challenges in accessing information that can help them improve their productivity and incomes. These challenges include:

```
Limited reach of extension services
Lack of awareness of government schemes
Difficulty understanding complex information
Language barriers

```

Kisan Mitra is designed to address these challenges by providing farmers with timely and accurate information in a user-friendly and accessible manner.

## What it does :

Kisan Mitra is a chatbot that can answer farmers' questions on a wide range of topics, including:

```
Government schemes and eligibility criteria
Farming techniques and best practices
Crop selection and pest management
Irrigation and water management
Market prices and weather conditions

```

Kisan Mitra can also provide farmers with links to additional resources, such as government websites and agricultural research papers.

## How we built it:

Kisan Mitra is built using the PaLM API, which is a large language model from Google AI. PaLM is trained on a massive dataset of text and code, which allows it to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.

Kisan Mitra is also integrated with a number of government databases and agricultural knowledge bases. This ensures that the information that Kisan Mitra provides is accurate and up-to-date.

## Challenges we ran into:

One of the biggest challenges we faced in developing Kisan Mitra was making it accessible to farmers of all levels of literacy and technical expertise. We wanted to create a chatbot that was easy to use and understand, even for farmers who have never used a smartphone before.

Another challenge was ensuring that Kisan Mitra could provide accurate and up-to-date information on a wide range of topics. We worked closely with government agencies and agricultural experts to develop a knowledge base that is comprehensive and reliable.

## Accomplishments that we're proud of:

We are proud of the fact that Kisan Mitra is a first-of-its-kind chatbot that is designed to address the specific needs of Indian farmers. We are also proud of the fact that Kisan Mitra is user-friendly and accessible to farmers of all levels of literacy and technical expertise.

## What we learned:

We learned a lot while developing Kisan Mitra. We learned about the challenges that Indian farmers face in accessing information, and we learned how to develop a chatbot that is both user-friendly and informative. We also learned about the importance of working closely with domain experts to ensure that the information that we provide is accurate and up-to-date.

## What's next for Kisan Mitra:

We are committed to continuing to develop and improve Kisan Mitra. We plan to add new features and functionality, and we plan to expand the knowledge base to cover more topics. We also plan to work with more government agencies and agricultural experts to ensure that Kisan Mitra is the best possible resource for Indian farmers.

We hope that Kisan Mitra will make a positive impact on the lives of Indian farmers by helping them to improve their productivity and incomes.
## Inspiration

Partially inspired by the Smart Cities track, we wanted our app to have the direct utility of ordering food, while still being fun to interact with. We aimed to combine convenience with entertainment, making the experience more enjoyable than your typical drive-through order.

## What it does

You interact using only your voice. The app automatically detects when you start and stop talking, uses AI to transcribe what you say, figures out the food items (with modifications) you want to order, and adds them to your current order. It even handles details like size and flavor preferences. The AI then generates text-to-speech audio, which is played back to confirm your order in a humorous, engaging way. There is absolutely zero set-up or management necessary, as the program will completely ignore all background noises and conversation. Even then, it will still take your order with staggering precision.

## How we built it

The frontend of the app is built with React and TypeScript, while the backend uses Flask and Python. We containerized the app using Docker and deployed it using Defang. The design of the menu is also done in Canva with a dash of Harvard colors.

## Challenges we ran into

One major challenge was getting the different parts of the app—frontend, backend, and AI—to communicate effectively. From media file conversions to AI prompt engineering, we worked through each of the problems together. We struggled particularly with maintaining smooth communication once the app was deployed. Additionally, fine-tuning the AI to accurately extract order information from voice inputs while keeping the interaction natural was a big hurdle.

## Accomplishments that we're proud of

We're proud of building a fully functioning product that successfully integrates all the features we envisioned. We also managed to deploy the app, which was a huge achievement given the complexity of the project. Completing our initial feature set within the hackathon timeframe was a key success for us. Trying to work with Python data type was difficult to manage, and we were proud to navigate around that. We are also extremely proud to meet a bunch of new people and tackle new challenges that we were not previously comfortable with.

## What we learned

We honed our skills in React, TypeScript, Flask, and Python, especially in how to make these technologies work together. We also learned how to containerize and deploy applications using Docker and Docker Compose, as well as how to use Defang for cloud deployment.

## What's next for Harvard Burger

Moving forward, we want to add a business-facing interface, where restaurant staff would be able to view and fulfill customer orders. There will also be individual kiosk devices to handle order inputs. These features would allow *Harvard Burger* to move from a demo to a fully functional app that restaurants could actually use. Lastly, we can sell the product by designing marketing strategies for fast food chains.
## 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.