Inspiration

Around 90% of the allergies people have are food related. 4.7% of children under the age of 5 having a reported food allergies, and 3.7% of children between the ages of 5 to 17. Milk allergies are most common in children, 2.5% of them under the age of 3 have an allergic reaction to milk.

Allergies can be pinpointed with the help of a medical professional but it’s a hassle to find out if the dish contains any allergic ingredients, especially in the case of takeout's or ordering food online. It is even more hard for the younger generation to figure out if the food is safe for them to eat.

What it does

Our Web app helps to upload the picture of the dish and display all the ingredients with other essentials things to figure out if the food is safe or not.

How we built it

We started off by uploading the image using Django framework and stored the image using post request in Django. Then we passed the image to the machine learning model. After processing the image by the machine learning model with over 80% accuracy, we passed the name of the dish predicted to EDAMAM API. This API helped us to get all the necessary information about the predicted dish.

Challenges we ran into

Most challenging part was to upload image from mobile app and sending the image to the Django server using REST API. Due to time constraints we couldn't implement the project though app, so to cope up with the challenge we decided to make a web app to demonstrate our idea.

Accomplishments that we're proud of

Building a model with over 80% of accuracy which is good considering the number of classes.

What we learned

We learnt to face the challenges and come up with a solution in a short period of time.

What's next for Allertgy

  1. Making a mobile version of the web-app for easy use
  2. Improving the accuracy of machine-learning model
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