Created
October 28, 2020 06:43
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004_fastapi
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# 1. Library imports | |
import uvicorn | |
from fastapi import FastAPI | |
from Model import IrisModel, IrisSpecies | |
# 2. Create app and model objects | |
app = FastAPI() | |
model = IrisModel() | |
# 3. Expose the prediction functionality, make a prediction from the passed | |
# JSON data and return the predicted flower species with the confidence | |
@app.post('/predict') | |
def predict_species(iris: IrisSpecies): | |
data = iris.dict() | |
prediction, probability = model.predict_species( | |
data['sepal_length'], data['sepal_width'], data['petal_length'], data['petal_width'] | |
) | |
return { | |
'prediction': prediction, | |
'probability': probability | |
} | |
# 4. Run the API with uvicorn | |
# Will run on http://127.0.0.1:8000 | |
if __name__ == '__main__': | |
uvicorn.run(app, host='127.0.0.1', port=8000) |
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