This week I focused on my work for the recommendation part as well as supporting tests for image recognition. I collected a set of images of the candidate items posted in the previous status report, and my teammate will use the image data set to test the performance of Google Vision on these candidate items. Some example images are posted below.
For the recommendation part, I built two functionalities. The first function will accept an input including two keys, tags and ingredients. It will output a list of recipe names for users to select from. Some sample input and output is as follows:
Input:
test_request = {‘tag’: [‘non-dairy’];’ingredient’: [‘apple’, ‘orange’,’tuna’]}
Output:
[Apple and orange jam’]
If no recipe can be matched to the request, then it will return the following output:
[‘No results found’]
All samples above are the real inputs and outputs of the system.
Also, after the user selects the recipe they want to use, the next function will accept input of recipe index, and return a list of instructions. A sample output will be the following:
[‘Cut 2 apples and 2 oranges into small chunks’, ‘boil 200ml of water in a pot’,
‘add apples, oranges and 50g of sugar into the pot, and stew with medium heat’, ‘when the content becomes thick, turn off the heat and add 2 table spoons of lemon juice’]
The progress is on schedule. I will use the performance result from Google Vision to decide the 9 items and 30 recipes next week and generate more test cases to test on the recipe recommendations. Also, I am currently working on the email sending recipes to user functionality. There are some issues with the authentication, but I will try to figure it out next week.