service: "service.py:svc"  # Same as the argument passed to bentoml
labels:
  owner: Sarthak
  stage: development
include:
  - "*.py"  # A pattern for matching which files to include in the bento
python:
   packages:  # Additional pip packages required by the service
     - scikit-learn
     - numpy
     - pandas
     - pydantic

import numpy as np
import pandas as pd
from fastapi import FastAPI
from pydantic import BaseModel
import json
import typing
import bentoml
from <http://bentoml.io|bentoml.io> import JSON
from <http://bentoml.io|bentoml.io> import NumpyNdarray

BENTO_MODEL_TAG = "iris_clf:3byq2muajs7tt6e6"


iris_classifier_runner = bentoml.sklearn.get(BENTO_MODEL_TAG).to_runner()

svc = bentoml.Service("iris_classifier_pydantic", runners=[iris_classifier_runner])

class IrisFeatures(BaseModel):
    sepal_len:float
    sepal_width:float
    petal_len:float
    petal_width:float

    #Optional Field
    request_id:typing.Optional[int]
    
    class Config:
        extra="forbid"

input_spec=JSON(pydantic_model=IrisFeatures)
    
    
@svc.api(input=input_spec,output=NumpyNdarray())
async def classify(input_data:IrisFeatures)->np.ndarray:
    if input_data.request_id is not None:
        print("Received request Id :" ,input_data.request_id)

    input_df=pd.DataFrame([input_data.dict(exclude={"request_id"})])
    return await iris_classifier_runner.predict.async_run(input_df)