Implementing an API with a combination of Lobe and FastAPI. sample_model is predict cat or dog from image.

SetUp

Run FastAPI Server form DockerImage

# docker-compose up --build -d

open http://localhost:8000/docs

if not use docker. need set up FastAPI Server.

FastAPI WebPage

Prediction(Use sample_model)

predict from base64 converted images.

import json
import requests
from image_utils import image2base64

def predict_from_base64(image_base64, url):
    data = {'base64_str': image_base64}
    response = requests.post(url, data=json.dumps(data))
    label = json.loads(response.text)['label']

    return label

# Convert the image to base64
image_bae64 = image2base64('sample_image/dog.9994.jpg')

# endpoint for predict from base64
predict_url_base64 = 'http://localhost:8000/predict_from_base64/'

# send predict request
label = predict_from_base64(image_bae64, predict_url_base64)

if predict from images:

def predict_from_image(filename, url):
    files = [('file', open(filename, 'rb'))]
    response = requests.post(url, files=files)
    label = json.loads(response.text)['label']

    return label

# endpoint for predict from image
predict_url_image = 'http://localhost:8000/predict_from_image/'

# send predict request
label = predict_from_image('sample_image/dog.9994.jpg', predict_url_image) 

For use your original model

changed model path(main.py)

# create model instance
# model = ImageModel('model folder path')
model = ImageModel('sample_model')