Methods and systems for calculating an edible score in a display interface

1. A system for calculating a score for an edible in a display interface, the system comprising a computing device configured to:

initiate a display interface within the computing device;

retrieve a performance profile relating to a user, wherein the performance profile includes a current level of age related degradation, wherein retrieving the performance profile comprises retrieving the performance profile as a function of a user questionnaire on the display interface and at least a sensor configured to detect at least a hematological parameter, wherein the performance profile comprises a biological extraction and at least an element of data, the element of data comprising at least a user's toxicity level and at least a user's medication use;

determine an edible of interest, wherein determining the edible further comprises:

identifying a user dietary habit as a function of a user database;

receiving an element of user geolocation data; and

determining the edible of interest as a function of the user dietary habit, element of user geolocation data, and an edible database;

determine nourishment information relating to the edible of interest using a dietary classifier configured to receive the edible of interest and output a dietary label using a classification process;

generate a score machine-learning process, wherein generating the score machine-learning process further comprises:

training, iteratively, the score machine-learning process using edible training data, wherein edible training data contains a plurality of data entries, each data entry containing a performance profile and nourishment information and a correlated score data based on the performance profile and nourishment information for a given edible, wherein the edible training data further comprises previous outputs from the score machine-learning process; and

calculating the score for the edible of interest as a function of the score machine-learning process, wherein the score machine-learning process uses the performance profile and the nourishment information relating to the edible of interest as an input, and outputs the score; and

display the score and the dietary label within the display interface.

2. The system of claim 1 , wherein the performance profile comprises sensor data.

3. The system of claim 1 , wherein the at least an element of data further comprises a user's fitness level.

4. The system of claim 1 , wherein the at least an element of data further comprises a user's sleeping patterns.

5. The system of claim 1 , wherein the computing device is further configured to:

identify a plurality of edibles as a function of the element of user geolocation data;

display the plurality of edibles within the display interface; and

receive a user selection containing the edible of interest.

6. The system of claim 1 , wherein nourishment information comprises a caloric input.

7. The system of claim 1 , wherein nourishment information comprises a nutrient input.

8. The system of claim 1 , wherein the computing device is further configured to:

calculate a first edible score for a first edible;

calculate a second edible score for a second edible;

chart the first edible score as a function of the second edible score; and

display the first edible score as a function of the second edible score within the display interface.

9. The system of claim 1 , wherein the computing device is further configured to generate a dietary classification machine-learning process, wherein generating the dietary classification machine-learning process further comprises:

training, iteratively, the dietary classification machine-learning process using dietary training data, wherein the dietary training data contains a plurality of data entries, each data entry correlating an edible datum with a dietary label datum;

generating a dietary label as a function of the dietary classification machine-learning process, wherein the dietary classification machine-learning process uses the edible of interest as an input, and outputs the dietary label, wherein the dietary label comprises a portion of the received nourishment information.

10. The system of claim 1 , wherein the performance profile further includes temporal data on the user's exposure to artificial light.

11. A method of calculating a score in a display interface, the method comprising:

initiating by a computing device, a display interface;

retrieving, by the display interface on the computing device, a performance profile relating to a user, wherein the performance profile includes a current level of age related degradation, wherein retrieving the performance profile comprises retrieving the performance profile as a function of a user questionnaire on the display interface and at least a sensor configured to detect at least a hematological parameter, wherein the performance profile comprises a biological extraction and at least an element of data, the element of data comprising at least a user's toxicity level and at least a user's medication use;

determining by the computing device, an edible of interest, wherein determining the edible further comprises:

identifying a user dietary habit;

receiving an element of user geolocation data; and

determining the edible of interest as a function of the user dietary habit, element of user geolocation data, and an edible database;

determining by the computing device, nourishment information relating to the edible of interest using a dietary classifier configured to receive the edible of interest and output a dietary label;

generating by the computing device, a score machine-learning process, wherein generating the score machine-learning process further comprises:

training, iteratively, the score machine-learning process using edible training data, wherein edible training data contains a plurality of data entries containing a performance profile and nourishment information relating to an edible correlated to an edible score based on the performance profile and nourishment information for the edible, wherein the edible training data further comprises previous outputs from the score machine-learning process; and

calculating the score for the edible of interest as a function of training the score machine-learning process, wherein the score machine-learning process uses the performance profile and the nourishment information relating to the edible of interest as an input, and outputs the score for the edible of interest; and

displaying by the computing device, the edible score within the display interface.

12. The method of claim 11 , wherein the performance profile comprises sensor data.

13. The method of claim 11 , wherein the at least an element of data further comprises a user's fitness level.

14. The method of claim 11 , wherein the at least an element of data further comprises a user's sleeping patterns.

15. The method of claim 11 , wherein determining the edible of interest further comprises:

identifying a plurality of edibles as a function of the element of user geolocation data;

displaying the plurality of edibles within the display interface; and

receiving a user selection containing the edible of interest.

16. The method of claim 11 , wherein nourishment information comprises a caloric input.

17. The method of claim 11 , wherein nourishment information comprises a nutrient input.

18. The method of claim 11 , wherein displaying the edible score further comprises:

calculating a first edible score for a first edible;

calculating a second edible score for a second edible;

charting the first edible score as a function of the second edible score; and

displaying the first edible score as a function of the second edible score within the display interface.