nestorsgarzonc

name: Flutter CI

on:
  push:
    branches:
      - "main"
  pull_request:
    branches:
    # Your desired branches
      - "main"

nestorsgarzonc

name: Flutter CD - Deployment Android

on:
  push:
    branches:
      - "main"
  pull_request:
    branches:
      - "main"

nestorsgarzonc

  Future<void> getRestaurant() async {
    state = state.copyWith(restaurant: StateAsync.loading());
    final result = await restaurantRepository.getMenuByRestaurant();
    result.fold(
      (failure) => state = state.copyWith(restaurant: StateAsync.error(failure)),
      (restaurant) => state = state.copyWith(restaurant: StateAsync.success(restaurant)
    );
  }
  
  

nestorsgarzonc

class ChatNotifier extends StateNotifier<ChatState> {
  ChatNotifier() : super(const ChatState()) {
    _initSocket();
    _handleSocketListening();
  }

  final _messagesStream = StreamController<dynamic>();
  final _usersStream = StreamController<dynamic>();
  final scrollController = ScrollController();
  late io.Socket socket;

nestorsgarzonc

void main() {
  final participants=["SB", "SG", "JS", "SL"];
  final features=[
    "Activation",
    "App",
    "Auth",
    "Bank",
    "Commercial",
    "Credit card",
    "Error",

nestorsgarzonc

import 'package:flutter/material.dart';

void main() => runApp(MyApp());

class MyApp extends StatelessWidget {
  const MyApp({Key? key}) : super(key: key);

  @override
  Widget build(BuildContext context) {
    return MaterialApp(

nestorsgarzonc

class Node:
    def __init__(self, item, priority):
        """
        Clase elemento en el cual se almacena la prioridad y el elemento
        item: Dynamic 
        priority: Int  
        """
        self.item = item
        self.priority = priority

nestorsgarzonc

from keras.layers import Conv2D,Flatten
model = Sequential()

# Add a convolutional layer of 32 filters of size 3x3
model.add(Conv2D(32, kernel_size = 3, input_shape = (28, 28, 1), activation = 'relu'))

# Add a convolutional layer of 16 filters of size 3x3
model.add(Conv2D(16, kernel_size =3, activation = 'relu'))

# Flatten the previous layer output

nestorsgarzonc

-- *************************************************************
-- Versión para Microsoft SQL Server
-- *************************************************************
/* Crear una tabla que contenga las características presentadas en el
enunciado del problema e incluya las columnas digitador y fecha */
CREATE TABLE Empleado
(
    identificacion BIGINT,
    nombres VARCHAR(30),
    apellidos VARCHAR(30),

nestorsgarzonc

#https://keras.io/api/preprocessing/image/
TRAINING_DIR = "/tmp/cats-v-dogs/training"
train_datagen = ImageDataGenerator(rescale=1.0/255.0)

# NOTE: YOU MUST USE A BATCH SIZE OF 10 (batch_size=10) FOR THE 
train_generator = train_datagen.flow_from_directory(TRAINING_DIR,
                                                    batch_size=10,
                                                    class_mode="categorical",
                                                    color_mode: "grayscale",
                                                    target_size=(1020, 480)