🙋‍♂️ This Project is divided across three repositories, This repo deals with the app/model deployed on cloud

For Frontend App see Frontend Repo
For Model Training see Training Notebook

What is this? :

The Project aims to solve the problem of identification of medicinal herbs.
The Machine Learning Model uses ResNet, with a validation accuracy of 98% and testing accuracy of 96%.
The training and testing dataset contains over 1500 images across 30 medicinal herb species.

Tech Stack Used :

Python 3
Flask
NumPy
Pandas
PyTorch
Tensorflow-Keras
FineTuned ResNet50 model trained on Mendeley Medicinal Leaf Dataset
Dataset: S, Roopashree; J, Anitha (2020), “Medicinal Leaf Dataset”, Mendeley Data, V1, doi: 10.17632/nnytj2v3n5.1
Google Cloud
GitHub

Model Deployment as a Flask WebApp on Google Cloud Services ☁️ :

Gcloud SDK shell commands to push, and deploy the service (should've set up GCloud beforehand, you don't need Docker on your machine though):

if you are running the shell in the same directory as your Dockerfile (which you probably should), replace Path/to/Dockerfile with . (a period or fullstop)
replace projectid with the ID associated with your project on the Gcloud console.

replace function with the name of the function you want your POST request from frontend to hit On.

 gcloud builds submit --tag gcr.io/{projectid}/{function} Path/to/Dockerfile

 gcloud run deploy --image gcr.io/{projectid}/{function}  --platform managed

Example Post request :

    import requests
    response = requests.post("YourServiceURLfromGcloud", files={'file': open("imgPath", 'rb')})

For instance, a post request for my deployed service on GCP is the following :

  response = requests.post("https://aushadhub-prcsxigeha-el.a.run.app/upload", files={'file': open("imgPath", 'rb')})

if you're encountering 403 Error (proxy or tunnel connection error), make sure you are not using Python-Anywhere to host your front-end, else try adding header and/or proxy to your requests :

header = { Define your header, to find it just run "navigator.userAgent" on your Chrome dev console }
proxy = { define your proxy, you may find it on the internet }
response = requests.post("https://aushadhub-prcsxigeha-el.a.run.app/upload", files={'file': open("imgPath", 'rb')}, header=header, proxies=proxies)

Return Type (JSON):

{ "prediction": "predName", "info": "description", "confidence_level": "probability" }

Example Code for unpacking returned JSON :

      import json
      responsedata = response.json()

      prediction = responsedata['prediction']
      confidence_level = responsedata['confidence_level']
      info = responsedata['info']

In Action :

TestPost.mp4

AusafMo/AushadhHub-BackEnd