wfng92

from fastapi import FastAPI, WebSocket, WebSocketDisconnect

app = FastAPI()


@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    try:
        while True:

wfng92

#1 import
try:
    import configparser
except:
    from six.moves import configparser

import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import requests

wfng92

import android.content.Context;

import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

import androidx.room.Database;
import androidx.room.Room;
import androidx.room.RoomDatabase;

@Database(entities = {User.class}, version = 1)

wfng92

import asyncio
import aiohttp
import time


async def gather_with_concurrency(n, *tasks):
    semaphore = asyncio.Semaphore(n)

    async def sem_task(task):
        async with semaphore:

wfng92

import cv2 as cv

frame_width = 680
frame_height = 480
fps = 30.0

video_capture = cv.VideoCapture(0)
video_capture.set(cv.CAP_PROP_FRAME_WIDTH, frame_width)
video_capture.set(cv.CAP_PROP_FRAME_HEIGHT, frame_height)

wfng92

from sentence_transformers import SentenceTransformer, util
import torch

# save model in current directory
model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2', device='cpu', cache_folder='./')
# save model in models folder (you need to create the folder on your own beforehand)
# model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2', device='cpu', cache_folder='./models/')

# Corpus with example sentences
corpus = [

wfng92

from phonemizer.backend import EspeakBackend

backend = EspeakBackend('en-us', preserve_punctuation=True, with_stress=True)

text = ["Hello, world!", "Welcome to Medium!"]

phonemized = backend.phonemize(text, strip=True)
print(phonemized)

wfng92

from ldm.generate import Generate

# model configuration
gr = Generate(
    'stable-diffusion-1.4',
    embedding_path='./models/embeddings.pt'  # modify the embedding path
)

# model loading
gr.load_model()

wfng92

from fastapi import FastAPI, Body
from pydantic import BaseModel

app = FastAPI()


class Number(BaseModel):
    value: int

wfng92

import ws from 'k6/ws';
import { check } from 'k6';

export let options = {
  stages: [
    { duration: '10s', target: 30 },
    { duration: '30s', target: 30 },
    { duration: '20s', target: 60 },
    { duration: '30s', target: 60 },
    { duration: '20s', target: 30 },