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from fastapi import FastAPI
import uvicorn
from datetime import datetime
from typing import Annotated
import os
import sys
import datetime
import pandas as pd
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from src.utils import load_file, make_predcition, date_extracts
# Create an instance of FastAPI
app = FastAPI(debug=True)
# get absolute path
DIRPATH = os.path.dirname(os.path.realpath(__file__))
# set path for ml files
ml_contents_path = os.path.join(DIRPATH, '..', 'assets', 'ml_components', 'toolkit_folder')
# get contents
ml_contents = load_file(ml_contents_path)
Encoder = ml_contents["OneHotEncoder"]
model = ml_contents["model"]
features_ = ml_contents['feature_names']
# define endpoints
@app.get('/')
def root():
return 'Welcome to the Gorecery Sales Forecasting API'
@app.get('/health')
def check_health():
return {'status': 'ok'}
@app.post('/predict')
async def predict_sales( store_id: int, category_id: int, onpromotion: int,
city: str, store_type: int, cluster: int, date_: Annotated[datetime.date, "The date of sales"] = datetime.date.today()):
# create a dictionary of inputs
input = {
'store_id':[store_id],
'category_id':[category_id],
'onpromotion' :[onpromotion],
'type' : [store_type],
'cluster': [cluster],
'city' : [city],
'date_': [date_]
}
# convert to dataframe and extract datetime features
input_data = pd.DataFrame(input)
date_extracts(input_data)
# make prediction
sales = make_predcition(Encoder, model, input)
sales_value = float(sales[0])
return {'sales': sales_value}
if __name__ == "__main__":
uvicorn.run('app:app', reload=True)
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