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from fastapi import FastAPI, Request, Body
import joblib
import numpy as np
import json

app = FastAPI()


@app.get("/")
async def read_main():

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from locust import HttpUser, between, task


class Prediction(HttpUser):
    wait_time = between(1, 1)

    @task
    def health(self):
        self.client.get("/health")

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from fastapi import FastAPI, Request, Body
import joblib
import numpy as np
from pydantic import BaseModel


class User(BaseModel):
    age: int
    bmi: float

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version: '3.5'

services:
  db:
    restart: always
    image: postgres:13.5-alpine
    container_name: postgres_container
    environment:
      POSTGRES_USER: ${POSTGRES_USER:-postgres}
      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-pass12345}

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from annoy import AnnoyIndex

f = len(img_array_resut[0])
t = AnnoyIndex(f, 'euclidean')

print(f"Number of features {f}")
print("Append the item vectors")
i = 0 
for v in tqdm(img_array_resut):
    t.add_item(i, v)

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def open_image(path):
    im = Image.open(path)
    X = preprocess(im, model.input_shape[1:3])
    X = reshape([X])
    return X

def preprocess(img,input_size):
    nimg = img.convert('RGB').resize(input_size, resample= 0)
    img_arr = (np.array(nimg))/255
    return img_arr

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SELECT customer_id,
COUNT(payment_id) count_trans,
SUM(amount) total_amount
FROM payment
GROUP by 1

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SELECT customer_id, payment_date as last_payment_date
FROM (
SELECT customer_id, 
payment_date, 
DENSE_RANK() over(PARTITION by customer_id ORDER BY payment_date DESC) as rnk_
FROM payment) as lpd
WHERE rnk_ = 1
ORDER BY random();

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