codeperfectplus

# save this file as postQuantization.py
def representative_dataset():
    for _ in range(100):
      data = np.random.rand(1, 320, 320, 3)
      yield [data.astype(np.float32)]

import numpy as np
import tensorflow as tf

saved_model_dir = "output/exported_models/tflite_infernce/saved_model"

codeperfectplus

''' Writing MetaData to TfLite Model 
save it as MetaDataWriterFile.py'''

from tflite_support.metadata_writers import object_detector
from tflite_support.metadata_writers import writer_utils
from tflite_support import metadata

ObjectDetectorWriter = object_detector.MetadataWriter
_MODEL_PATH = <tf_lite_model_path>
_LABEL_FILE = <label_path>

codeperfectplus

from PIL import Image
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r"path_to_tessract.exe"

def ocr(filename):
    img = Image.open(filename)
    text = pytesseract.image_to_string(img)
    return text

codeperfectplus

# Send discord message using webhook and asynchronous api with aiohttp and asyncio

# Asynchronous calls do not block (or wait) for the API call to return from the server. 
# Execution continues on in your program, and when the call returns from the server, a “callback” function is executed.

```python
import aiohttp
import asyncio

url = "<your url>" #webhook url, from here: https://i.imgur.com/f9XnAew.png

codeperfectplus

'''
Script to get GitHub user Data using Asynchronous API, logging and save the output in the SQLite Database.
'''
import logging
import sqlite3
import aiohttp
import asyncio
from sqlite3 import Error

async def fetch_github(url):

codeperfectplus

codeperfectplus

""" python gist to create parser """"
import argparse

def add(x, y):
    """ it will return the addition of x and y """
    return x + y

parser = argparse.ArgumentParser()

parser.add_argument('--x', type=int, default=1)

codeperfectplus

Inspired by dannyfritz/commit-message-emoji

See also gitmoji.

Commit type	Emoji
Initial commit	🎉 :tada:
Version tag	🔖 :bookmark:
New feature	✨ :sparkles:
Bugfix	🐛 :bug:

codeperfectplus

""" Solver Linear equation using inverse matrix """
import numpy as np

class LinearEquationSolver(object):
    """ Linear Equation solver using inverse matrix 
    
    equation: AX = B

    X = inverse(A) . B

codeperfectplus

from flask import Flask, jsonify, request
# Create a Flask app
app = Flask(__name__)

# sample superhero data
superheroes = [
    {
        "id": 1,
        "name": "Superman",
        "email": "[email protected]",