CleanPegasus

pragma circom 2.0.0;

include "../node_modules/circomlib/circuits/poseidon.circom";
include "../node_modules/circomlib/circuits/comparators.circom";
include "../node_modules/circomlib/circuits/gates.circom";

template CreditProof() {
    signal input creditScore;
    signal input minCreditScore;
    signal input maxCreditScore;

CleanPegasus

pragma circom 2.0.0;

include "../node_modules/circomlib/circuits/poseidon.circom";
include "../node_modules/circomlib/circuits/comparators.circom";


template AgeProof() {
    signal input doBTimestamp; // Timestamp of the DoB in seconds
    signal input address; // Address of the user
    signal input currentTimestamp; // Current time in seconds

CleanPegasus

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;

import "@openzeppelin/contracts/access/Ownable.sol";

contract DIdentity is Ownable {

    struct Identity {
        bytes32 UID;
        bytes32 nameHash; // keccak256 hash of name

CleanPegasus

from tensordash.tensordash import Customdash

histories = Customdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

try:

    for epoch in range(num_epochs):

CleanPegasus

from tensordash.fastdash import Fastdash

my_cb = Tensordash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

try:
    learn.fit(epochs, learning_rate, callbacks = my_cb)
except:

CleanPegasus

from tensordash.torchdash import Torchdash

histories = Torchdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

try:
    for epoch in range(epochs):
        losses = []

CleanPegasus

from tensordash.tensordash import Tensordash

histories = Tensordash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')
    
try:
    model.fit(
    X_train, 

CleanPegasus

import tensorflow as tf

def sample(x, y, z):
  return tf.reduce_sum(x + y * z)

CleanPegasus

import requests
import json

EMOTIONS = ["angry", "disgust", "scared", "happy", "sad", "surprised", "neutral"] # Emotions corresponding to the output
data = json.dumps({"signature_name": "serving_default", "instances": img.tolist()}) # making the input in the format required for serving

headers = {"content-type": "application/json"} # specifying that the input will be in json format
json_response = requests.post('http://localhost:8501/v1/models/emotion_model:predict', data=data, headers=headers) # post request to the served model
predictions = json.loads(json_response.text)['predictions'] # getting the predictions

CleanPegasus

img = cv2.imread('img.jpg') # reading the image
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # converting the image to grayscale
img =  cv2.resize(img, (48, 48)) # resizing the image
img = img/255 # normalizing the image
img = np.reshape(img, [-1, 48, 48, 1]) # reshaping the image to be suitable for serving