## Inspiration

We believe the best way to learn a new language is to immerse yourself in it. That’s why we created LinguaScan. Through learning translations of everyday objects and places, you’ll pick up a new language in no time!

## What it does

Lingua Scan translates virtually anything around you! Scan anything with the app’s camera and it’ll use trained machine learning models to recognize the object, provide a translation, and even provide a short description of it.

But what’s learning a new language without being able to reinforce your learning? In the future, LinguaScan will also record every object you’ve seen the translation of and test your knowledge by chatting with a personalized chatbot that crafts conversations based on your knowledge of the language so far. You can also upload pictures of your travels and learn even more through interactive learning.

## How we built it

We built this app using a frontend in Flutter and the services of Authentication and Firestore from Google Firebase. We implemented OpenCV library with real time translation integration.

## Challenges we ran into

One of the major challenges that we ran into was Authentication with Firebase and Flutter. Much of the documentation was not in sync with the most recent versions of Flutter and Firebase, causing a great deal of confusion. At the end, we were able to use basic email authentication. In the future, we would like to seamlessly integrate Google authentication and other methods as well. Additionally, we had issues trying to generalize translation to specific objects.

## Accomplishments that we're proud of

We are all proud of learning about Flutter and using new technologies. We are also proud of being able to submit a project amidst our busy schedules and the stresses of the school year.

## What we learned

Since most of us didn’t have experience in Firebase or Flutter, we spent most of the time perusing documentation and guides on how the 2 frameworks and technologies were intertwined and integrated together. We really enjoyed the experience at HackMIT and would definitely do this experience again. We also learned about the architecture for a computer vision based project.

## What's next for LinguaScan

We hope to bring LinguaScan past the MVP stage and revamp the feature set that LinguaScan offers as well. Future feature updates are planned to include a speech-to-text feature that tests the user’s proficiency in a certain language. We are excited about the future of LinguaScan!
## Inspiration

Our initiative confronts the urgent issue of food waste with a dual approach involving consumers and farmers. We implement a unique and creative solution using AI, computer vision, and drone technology.

“Ugly” vegetables are a major cause of food waste. Every year, over [20%](https://foodwastefeast.com/why-we-waste-ugly-food-expiration-dates-and-more#:%7E:text=It's%20estimated%20that%20approximately%2020,nutritious%20and%20delicious%20to%20eat.) of wasted fruit and vegetables were attributed to “ugly” or “wonky” produce, that is **six billion** pounds. This produce was discarded for failing to meet retailer appearance standards

On the other hand, Low-income families have a high awareness of healthy diets, but cannot afford good-quality food according to this [article](https://www.news-medical.net/news/20210224/Low-income-families-have-high-awareness-of-healthy-diets-but-cant-afford-good-quality-food.aspx). [This](https://www.healthaffairs.org/do/10.1377/hpb20180817.901935/) creates a lot of health problems due to bad nutrition.

## What it does

For Farmers: We provide a dedicated platform where farmers can market their 'imperfect produce,' which, while entirely edible, may bear minor cosmetic imperfections that render them unsellable in conventional markets.

For Users: Users simply photograph the contents of their fridge, and our sophisticated algorithm takes over. It crafts personalized recipes based on available ingredients and accesses data on surplus produce from local farms.

## How we built it

### Backend Development:

**Flask**: Our web application is built on the Flask framework, providing us the flexibility and tools necessary to create a dynamic and interactive user experience.
**SQLite**: Serving as our database, SQLite efficiently manages data and ensures smooth operations even as the user base grows.

### Data Pre-population:

**Faker**: To simulate a realistic environment for testing, we utilized Faker to pre-populate our database with synthetic yet believable data.

### Image Analysis:

**Google Cloud Vision API (vision\_v1)**: Post-segmentation, each segmented object is analyzed using the Google Cloud Vision API to identify and catalog the items present.

**DeepLabV3**: To dissect images of refrigerated items into distinct objects, we employed the DeepLabV3 model. This segmentation stage is crucial for accurate item identification.

### Drone Prototyping:

Drone delivery is an eco-friendly and swift technological method that eliminates the inconvenience of farmers relying on other transportation while also actively contributing significantly to the sustainability of the environment.

**CAD Modeling**: Conceptualizing and iterating our drone design was made feasible and efficient through Computer-Aided Design (CAD) modeling.

**Autodesk Fusion 360**: For a precise and detailed design of our drone prototype, we used Autodesk Fusion 360, taking advantage of its comprehensive set of tools for 3D design, engineering, and simulation.

## Challenges we ran into

1. One of the challenges we faced was breaking down our project ideas into smaller, manageable tasks and then figuring out which tasks to work on first.
2. When dealing with Google Cloud Computer Vision, it only does image recognition of one whole image and does not break it down. We discussed and came up with a creative solution where we used a different ML model to separate the original image and send each segment to Google Cloud.
3. Our API key says "too many requests" at the end when we are trying to record a demo. We used too many requests in a short period of time while developing it

## Accomplishments that we're proud of

1. All of us came from different countries and we were able to match each other's energy very well. We have very different ideas which made us work so well together as we allow our thoughts to complement and amplify each other.
2. Team member, Omari Emmanuel learned how to do HTML for the first time with everyone's help and successfully deployed the help web page in the main designed online platform for our project
3. We researched deeply into a pressing issue that affects 25% of the US population, and we were able to come up with a comprehensive idea to mitigate that issue in a short amount of time.

## What's next for Misfit Munch

1. We want to continue working by trying to validate the market and see if there is a strong demand.
2. Understand the local laws of drones and the viability of transporting goods and services using drones, figuring out how to engage with regulatory bodies.
3. Secure funding by applying to grants, which also validates our ideas as well as giving us a head start in purchasing delivery drones
# LangUR: Linguistic Mastery at Your Fingertips

## Learning languages is one thing, but what does it take to be fluent in them?

**LangUR** is a groundbreaking project which bridges the difficult phases of learning languages. In a diverse business world with hundreds of languages being used in high commerical settings, modern applications serve to teach proficiency in langauges, but per a fluency study of Duolingo patrons learning French and Spanish, only 52.94% of 102 French learners reached a pre-intermediate level, and 66.03% of 156 Spanish speakers reached a similar level. (Jiang X, 2021)

![image](https://cdn.discordapp.com/attachments/906212540021895178/1208541042333454357/image.png?ex=65e3a88e&is=65d1338e&hm=d4eda74145d711657f12a1d2503ff8dad36b73efaa44294fdc51be3e499d2b48&)
![image](https://cdn.discordapp.com/attachments/906212540021895178/1208541095605309461/image.png?ex=65e3a89a&is=65d1339a&hm=6b2d5407681f1a568841cdec7fc972ea2c33df6d24568de47a04009c68a29d2d&)

## So the question stands: what helps set apart LangUR from language learning apps that help learners breach a beginner level of linguistic competence?

## **Efficiency. Scalability. Practicality.**

# Key Features:

1. Uses a grounded evaluation metric to measure your performance (LIX scoring algorithm)
2. Application learns and adapts to individual learner's abilities
3. Learning of language conveniently incorporated into daily workflow
4. Reinforced learning of queries words through review quizzes

# Development Tools:

1. PostmanAPI: Testing and Integrating API Endpoints

	* Between Frontend and Backend-> enabled synchronous development by different teams
	* Testing of MonsterAPI's inference engine-> enable quick iteration and testing of various baseline models and their associated hyperparameters
	* Facilitated collaboration between Frontend and Backend Team with a shareable link detailing communication format
2. Bun: Leveraging Bun as a JavaScript ecosystem for web deployment

	* ultra fast and all in one bundler, runtime and package manager that saved us a lot of time
3. MonsterAPI: Fine tuning the baseline models and using the inference engines

	* easy to access deployment and fine-tuning platform greatly reduced the learning curve
	* round-clock support by staff made development easier
4. Chroma Vector DB: Quick and reliable retrieval of semantic lists and articles for rapid processing with the LLM Engines.

# Tech Stack:

![image](https://cdn.discordapp.com/attachments/906212540021895178/1208809771969548318/image.png?ex=65e4a2d4&is=65d22dd4&hm=eed8c1ee801ca095d3ced02556b9588e3d839cea0238873b598a050c9a488457&)

## Sample on a webpage

![image](https://cdn.discordapp.com/attachments/906212540021895178/1208799395672494151/image.png?ex=65e4992a&is=65d2242a&hm=25af027e439ae07aaa69a885152cb600da6d5374ead0717b527f4e1545abeea8&)

## Research

The project's premise and continuity relies very heavily on social research. When Robin was learning his languages, he found that maintaining continuous performance on a daily basis aided his performance in learning a language, and such a trend is commonly correlated with higher testing proportions among students that are fed information on a consistent daily basis when studying, as demonstrated by the American Psychological Society (Mawhinney et al., 1971).

![image](https://cdn.discordapp.com/attachments/1201235197425889452/1208784092938633266/image.png?ex=65e48ae9&is=65d215e9&hm=996c2f55e975ec4db33be0032d5253f5c894641b4295a0c5b1968ec056bf7f1e&)

Moreover, the concept of integrating language learning seamlessly into daily routines aligns with principles of habit formation and behavioral psychology. By embedding language practice within the natural flow of a user's day, LangUR capitalizes on the psychological phenomenon of habit stacking. This approach leverages existing habits as anchors for new behaviors, making language learning feel less like a burdensome task and more like an integrated aspect of daily life. Stacking habits is quintessential as evidenced for learning, so, how would one be able to consider a new approach to structuring a language learning app based off of this? Let's look back at the presented graph.

In any instance, Distributed Practice and Practice Testing appear to be the largest factors associated with higher testing in general study areas, where we based our project idea off of: a gradual but slow streamline of language implementation, albeit slowly and consistently. By being passive and seamless, LangUR has the capability to gradually streamline language learning modes into a user's daily workflow, providing an excellent UI, with a diverse array of features such as translation, suggested articles based off of past history, and progressively improving the user's ability to take in the language with the readability algorithm.

We often encounter the issue that learning a language is daunting, requiring continuous effort, where a lot of people simply don't have that time to invest, whereas the practicality of being fluent in languages has a high yield in business returns by eliminating barriers between multinational individuals and corporations. The idea behind LangUR had to critically emphasize the parallel and efficient nature of learning, which other applications failed to consider. Giving users a comfortable experience, automatically determining skill level through a customized LIX Algorithm, and structuring the application to cater to their learning pace was imperative during the building phase.

In conclusion, taking into account human study patterns, the demanding needs of learning a language and daily time constraints, LangUR was built with the mindfulness that dedication is always on the users side, but we can do our job to make it as seamless as possible, accessible, and catered to the users.

## What's next for LangUR

LangUR's potential is unbelievable. It has the ability to drive a stake in the educational world, collectivizing language learners, teachers, students, and anyone through simplicity. As the project's development continues, more functions will be added such as social integrations, connecting learners across the world, and more advanced algorithms will be put in place to create more personalized suggestions, helping people learn their new languages.