Prediction API client for Python
Project description
prediction-api
- App will receive an HTTP POST call in this format
curl -X POST
-H 'Authorization: Key API_KEY'
-H "Content-Type: application/json"
-d '
{
"inputs": [
{
"data": {
"image": {
"url": "https://lh3.googleusercontent.com/GNE-0Vga-Uffz1i9oxP0ueYYvPW8qG_z7_IlQX3yCqPNILam2Z-rlkYzjoRGkPp74XeEkVb96r1-N1-yRKGhvkFQK5uI=s256"
}
}
}
]
}'
https://prediction-api-dot-platform-ai-dev.appspot.com/v1/project?id=ahFzfnBsYXRmb3JtLWFpLWRldnJTCxIEVXNlciIcMGFXdExZREhlU1puZ3F1SUxpYXUzQ0NFQ1FFMgwLEgdQcm9qZWN0IiA2MmEwMjYyYjNiYTI0NDA4OGNlYTgyYjg0MTQ1MDg3Mww
-
Since this is a multi-tenant application and we cannot redploy it (or use the same key for multi customers), in order to turn on a new tenant, API keys need to be managed in Google Data Store. Project type in Datastore has a reference to valid API keys that can access it for prediction.
-
Import model_utilities from platform-ai package (TBD)
model = get_model(project_id)
model.predict_from_url(URL) returns prediction json
-
cache the model in memorystore for future requests, backend will invalidate this cache if/when the model is retrained
Error codes
- Bad URL / not accessible
- project_id invalid
- rate limit exceeded
- no trained model is available
- backend error / something blew up
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