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A simulated system to scale users dietary habits using AWS EC2, S3, Lambda, SQL, Rekognition, and Docker.
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bin
public
routes
GetAllLabels.java
README.md
References
Team 4 Presentation Slides.pptx
Team4Task02UPMC.docx
app.js
package.json
pageOne_UI.html
references_experimentalCode
rekognition.py
trigger_service.py

README.md

Team 4

Team Project

rekognition.py

rekognition.py contains the main functions of our project, which include invoking AWS rekognition API, Google Cloud Vision API and nutritionix API. At the end of rekognition.py, we send a POST request with our nutrition facts and the final score.

trigger_service.py

copy a random image from a source bucket to our team test bucket by using FLask.

.gitignore

.gitignore is used to filter certain file types

package.json, bin, public, routes, app.js

These files are used to run node and handle request with js

References

All the source is cited in references

GetAllLabels.java

Experimental code ONLY. Code to get labels from all images in a local folder using the GCP Vision.

references_experimentalCode

References for GetAllLabels.java

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