The problem VetAce solves

Problem
Imagine your pet is suffering from any kind of disease. What are the biggest challenges you think you'll face at this time of COVID-19 being spread all over the world? 1.) Taking a risk to take your pet outside to a VETfor consultation.

2.) Waiting for your turn for longer duration of time.

3.) Stressing your pet all our the journey to take to VET to home.

And Even if the COVID-19 is gone, still many would be having time constraints or hate to wait in the long long queues.

These are the most common challenges that would be faced during this time even if your pet is suffering from any small kind of disease. So what if we give you a solution to solve all the majors' challenges. Sitting just at your place you can identify your pet's problem in just a few seconds with all the details on how it can be cured.

Solution:
VetAce is a web-based app solution in which a user can:
1.) Click the pic of the pet and upload it to the disease predicted over our algorithm.

2.) Our Web App will also suggest initial curing.

3.) Can explore any disease by visiting the description page or clicking over hyperlinks.

4.) Can get advice from VET experts over our site.

In this project, we provide users with features to examine their pets just by uploading a picture of their pet, that too at the ease of your bed and get the result for the cure, and also expert advice if subscription mode is on.

All you need to do is simply click a picture of your pet if it's suffering from any disease and just upload it. We will provide you with the best-related output.
With just on single click users can upload the image of their pet and our application will use machine learning model to generate the report of the disease from which your pet is suffering and will also tell the required cure that can be taken.
You can easily have a pre-treatment and knowledge about the disease and can easily consult the VET for further information.

Challenges we ran into

1.) No particular dataset we found so, we had to make our own
2.) Converting the TensorFlow model to the tensorflow.js model was quite tricky
3.) There was difficulty in integrating tensorflow.js with HTML and javascript files
4.) Deployment of ml model was a big difficulty