I used pycaret to create an REST API.like
from pycaret.datasets import get_datadata = get_data('diamond')from pycaret.regression import *s = setup(data, target = 'Price', transform_target = True, log_experiment = True, #log_plots = True, experiment_name = 'diamond')lightgbm = create_model('lightgbm')create_api(lightgbm, 'my_second_api')The generated code is
# -*- coding: utf-8 -*-import pandas as pdfrom pycaret.regression import load_model, predict_modelfrom fastapi import FastAPIimport uvicornfrom pydantic import create_model# Create the appapp = FastAPI()# Load trained Pipelinemodel = load_model("my_second_api")# Create input/output pydantic modelsinput_model = create_model("my_second_api_input", **{'Carat Weight': 1.5499999523162842, 'Cut': 'Ideal', 'Color': 'F', 'Clarity': 'SI1', 'Polish': 'EX', 'Symmetry': 'EX', 'Report': 'GIA'})output_model = create_model("my_second_api_output", prediction=5169)# Define predict function@app.post("/predict", response_model=output_model)def predict(data: input_model): data = pd.DataFrame([data.dict()]) predictions = predict_model(model, data=data) return {"prediction": predictions["prediction_label"].iloc[0]}if __name__ == "__main__": uvicorn.run(app, host="127.0.0.1", port=8000)And when I try to run it with python theappi.py I get
Transformation Pipeline and Model Successfully LoadedTraceback (most recent call last): File "/media/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/MLOps1/Entrega/6. Model serving a través de APIs/my_second_api.py", line 16, in <module> input_model = create_model("my_second_api_input", **{'Carat Weight': 1.5499999523162842, 'Cut': 'Ideal', 'Color': 'F', 'Clarity': 'SI1', 'Polish': 'EX', 'Symmetry': 'EX', 'Report': 'GIA'}) File "/home/miniconda3/envs/mlops39/lib/python3.9/site-packages/pydantic/main.py", line 1441, in create_model return meta( File "/home/miniconda3/envs/mlops39/lib/python3.9/site-packages/pydantic/_internal/_model_construction.py", line 92, in __new__ private_attributes = inspect_namespace( File "/home/miniconda3/envs/mlops39/lib/python3.9/site-packages/pydantic/_internal/_model_construction.py", line 372, in inspect_namespace raise PydanticUserError(pydantic.errors.PydanticUserError: A non-annotated attribute was detected: `Carat Weight = 1.5499999523162842`. All model fields require a type annotation; if `Carat Weight` is not meant to be a field, you may be able to resolve this error by annotating it as a `ClassVar` or updating `model_config['ignored_types']`.For further information visit https://errors.pydantic.dev/2.5/u/model-field-missing-annotationI corrected the code to
# -*- coding: utf-8 -*-import pandas as pdfrom pycaret.regression import load_model, predict_modelfrom fastapi import FastAPIimport uvicornfrom pydantic import create_model# Create the appapp = FastAPI()# Load trained Pipelinemodel = load_model("my_second_api")# Create input/output pydantic models using create_modelinput_fields = {'Carat_Weight': (float, 1.5499999523162842),'Cut': (str, 'Ideal'),'Color': (str, 'F'),'Clarity': (str, 'SI1'),'Polish': (str, 'EX'),'Symmetry': (str, 'EX'),'Report': (str, 'GIA')}output_fields = {'prediction': (float, 5169)}InputModel = create_model('InputModel', **input_fields)OutputModel = create_model('OutputModel', **output_fields)# Define predict function@app.post("/predict", response_model=OutputModel)def predict(data: InputModel): data = pd.DataFrame([data.dict()]) predictions = predict_model(model, data=data) return {"prediction": predictions["prediction_label"].iloc[0]}if __name__ == "__main__": uvicorn.run(app, host="127.0.0.1", port=8000)Is pycaret generating wrong code a common occurrence? and why would be the cause of this?