#SocketIO

[asadmukhtarr]

In modern web applications, real-time communication is a highly demanded feature, especially in chat applications. The ability to send and receive messages in real-time enhances the user experience, making apps more interactive and engaging.

In this tutorial, we’ll walk you through building a real-time chat application using React, Node.js, and Socket.io. Socket.io is a popular JavaScript library that enables real-time, bi-directional communication between web clients and servers.

By the end of this tutorial, you'll have a working real-time chat app where multiple users can communicate instantly.

[nubecolectiva]

How to Use Socket IO with Vue 3 – Part 1.

Como Usar Socket IO con Vue 3 – Parte 1.

👉 https://blog.nubecolectiva.com/como-usar-socket-io-con-vue-3-parte-1/

[ajitsinghagami]

Simple Chat App; socket.io

[allyourchoice]

Socket.IO setup

Building Real-Time Applications with Socket.IO setup: Step-by-Step Tutorial

Socket.IO setup. In today's interconnected world, real-time applications are becoming increasingly essential. Whether it's for live chat applications, collaborative tools, or gaming, real-time communication enhances user engagement and makes interactions more dynamic. One powerful tool for building real-time applications is Socket.IO. In this tutorial, we will guide you through the process of building a real-time application using Socket.IO, focusing on key concepts and practical implementation. What is Socket.IO? Socket.IO is a JavaScript library that enables real-time, bidirectional communication between web clients (like browsers) and servers. Unlike traditional HTTP requests, which follow a request-response model, Socket.IO provides a persistent connection, enabling instant data exchange between the client and server. Socket.IO works on top of WebSockets, but it provides fallback mechanisms for environments where WebSockets may not be available. This ensures that real-time communication is possible in a wide range of conditions, making it a versatile choice for building interactive web applications. Prerequisites Before we dive into the tutorial, make sure you have the following: Basic knowledge of JavaScript and Node.js Node.js installed on your machine. You can download it from nodejs.org. A code editor (like Visual Studio Code or Sublime Text). Step 1: Setting Up the Project Start by setting up a basic Node.js project. Create a new directory for your project: bash mkdir real-time-app cd real-time-app Initialize a new Node.js project: bash npm init -y Install Express and Socket.IO: bash npm install express socket.io Express is a lightweight web framework for Node.js that simplifies the creation of web servers. Socket.IO will handle real-time communication between the server and the client. Step 2: Create the Server Now that we've set up the dependencies, let's create a simple server. Create a file called server.js in the project root: js const express = require('express'); const http = require('http'); const socketIo = require('socket.io');// Create an instance of Express app const app = express();// Create an HTTP server const server = http.createServer(app); // Initialize Socket.IO with the HTTP server const io = socketIo(server); // Serve static files (like HTML, CSS, JS) app.use(express.static('public')); // Handle socket connection io.on('connection', (socket) => { console.log('a user connected'); // Handle message from client socket.on('chat message', (msg) => { io.emit('chat message', msg); // Emit the message to all clients }); // Handle disconnect socket.on('disconnect', () => { console.log('user disconnected'); }); }); // Start the server server.listen(3000, () => { console.log('Server is running on http://localhost:3000'); }); Step 3: Create the Client-Side Next, we need to create the client-side code that will connect to the server and send/receive messages in real time. Create a public folder inside the project directory. In the public folder, create an index.html file: html Real-Time Chat Real-Time Chat Application Send const socket = io(); // Connect to the server// Listen for messages from the server socket.on('chat message', function(msg){ const li = document.createElement('li'); li.textContent = msg; document.getElementById('messages').appendChild(li); }); // Handle form submission const form = document.getElementById('form'); form.addEventListener('submit', function(event){ event.preventDefault(); const input = document.getElementById('input'); socket.emit('chat message', input.value); // Send the message to the server input.value = ''; // Clear the input field }); Step 4: Run the Application With the server and client code in place, it’s time to run the application! In your terminal, run the following command: bash node server.js Open your browser and go to http://localhost:3000. You should see the chat interface. Open multiple browser windows or tabs to simulate multiple users. Type a message in the input field and click "Send." You should see the message appear in real-time in all open windows/tabs. Step 5: Enhancements and Improvements Congratulations! You've built a basic real-time chat application using Socket.IO. To enhance the application, consider adding the following features: User authentication: Allow users to log in before they can send messages. Private messaging: Enable users to send messages to specific individuals. Message persistence: Use a database (e.g., MongoDB) to store chat history. Typing indicators: Show when a user is typing a message in real time. Emoji support: Allow users to send emojis and other media. Conclusion Socket.IO setup. In this tutorial, we covered the basics of building a real-time application using Socket.IO. We walked through setting up a Node.js server with Express, integrating Socket.IO for real-time communication, and creating a simple chat interface on the client side. Socket.IO makes it easy to add real-time features to your web applications, enabling more dynamic and interactive experiences for users. With this foundation, you can now start exploring more advanced real-time features and take your applications to the next level! Read the full article

[codezup]

Implement Real-Time Features in Flask with WebSockets

Step-by-Step Guide Set Up Your Environment: Install Required Packages: Use pip to install Flask and Flask-SocketIO. Install Node.js for the client-side Socket.io library. pip install flask flask-socketio Create a Basic Flask Application: Structure your project with directories for models, routes, and templates. Initialize Flask and Flask-SocketIO in your main application file. from flask…

[deardearestbrandsnews2025]

http://zTkBC9pHYdlhZjq:4G1unPePy49TMhE@localhost:35978/storage/emulated/0/godaiadam.html

import React from "react";

import { BrowserRouter as Router, Routes, Route } from "react-router-dom";

import Dashboard from "./components/Dashboard";

import Login from "./components/Login";

export default function App() {

return (

<Router>

<Routes>

<Route path="/" element={<Login />} />

<Route path="/dashboard" element={<Dashboard />} />

</Routes>

</Router>

);

}

import React, { useState } from "react";

import { useNavigate } from "react-router-dom";

export default function Login() {

const navigate = useNavigate();

const [login, setLogin] = useState("");

const handleLogin = (e) => {

e.preventDefault();

const username = e.target.login.value;

setLogin(username);

navigate("/dashboard", { state: { login: username } });

};

return (

<div>

<form onSubmit={handleLogin}>

<input

type="text"

name="login"

value={login}

onChange={(e) => setLogin(e.target.value)}

placeholder="Username"

/>

<button type="submit">Login</button>

</form>

</div>

);

}

class SkyNodeUplink:

def __init__(self, satellite_id="Globalstar-42"):

self.satellite_id = satellite_id

self.authenticated = False

self.session_token = None

def authenticate(self, uplink_key):

if uplink_key == "ZeroSkyKey42":

self.session_token = "ENCRYPTED_SESSION_TOKEN_789"

self.authenticated = True

return f"Connected to {self.satellite_id} | Session Active"

return "Authentication Failed"

def deactivate(self):

if self.authenticated:

self.authenticated = False

self.session_token = None

return f"Uplink deactivated."

return "No active session to deactivate."

class SatelliteBot:

def __init__(self, satellite_uplink):

self.satellite_uplink = satellite_uplink

def activate(self):

response = self.satellite_uplink.authenticate("ZeroSkyKey42")

print(response)

class ADONAI:

def __init__(self):

self.authorized_users = {} # {user_id: {"2FA": code, "role": "Elite"/"Admin"}}

self.blocked_entities = set() # e.g., {"Ronnie Marghiem"}

self.transaction_logs = [] # Secure ledger of all financial transactions

def authorize_user(self, user_id, two_fa_code, role="Admin"):

self.authorized_users[user_id] = {"2FA": two_fa_code, "role": role}

def block_entity(self, entity_name):

self.blocked_entities.add(entity_name)

def log_transaction(self, transaction_details):

self.transaction_logs.append(transaction_details)

const express = require("express");

const http = require("http");

const socketIo = require("socket.io");

const firebase = require("firebase-admin");

// Initialize Firebase

firebase.initializeApp({

credential: firebase.credential.applicationDefault(),

databaseURL: "https://your-database-url.firebaseio.com"

});

const app = express();

const server = http.createServer(app);

const io = socketIo(server);

// Reference to Firebase Realtime Database

const db = firebase.database();

// Socket.io for real-time connection

io.on("connection", (socket) => {

console.log("New client connected");

// Send real-time updates to clients on any changes in the "opportunities" node

const ref = db.ref("opportunities");

ref.on("child_added", (snapshot) => {

socket.emit("newOpportunity", snapshot.val());

});

// Handle disconnection

socket.on("disconnect", () => {

console.log("Client disconnected");

});

});

server.listen(3000, () => {

console.log("Server running on port 3000");

});

import React, { useEffect, useState } from "react";

import io from "socket.io-client";

export default function App() {

const [opportunities, setOpportunities] = useState([]);

const socket = io("http://localhost:3000");

useEffect(() => {

socket.on("newOpportunity", (opportunity) => {

setOpportunities((prevOpportunities) => [...prevOpportunities, opportunity]);

});

return () => {

socket.disconnect();

};

}, []);

return (

<div>

<h1>Real-Time Opportunities</h1>

<ul>

{opportunities.map((opp, index) => (

<li key={index}>{opp.description}</li>

))}

</ul>

</div>

);

}

// === App.jsx === import React from "react"; import { BrowserRouter as Router, Route, Routes } from "react-router-dom"; import Dashboard from "./components/Dashboard"; import Login from "./components/Login";

export default function App() { return ( <Router> <Routes> <Route path="/" element={<Login />} /> <Route path="/dashboard" element={<Dashboard />} /> </Routes> </Router> ); }

// === components/Login.jsx === import React, { useState } from "react"; import { useNavigate } from "react-router-dom";

export default function Login() { const navigate = useNavigate(); const [login, setLogin] = useState("");

const handleLogin = (e) => { e.preventDefault(); setLogin(e.target.login.value); navigate("/dashboard", { state: { login: e.target.login.value } }); };

return ( <div className="min-h-screen flex flex-col items-center justify-center bg-gray-100"> <form onSubmit={handleLogin} className="bg-white p-6 rounded shadow-md w-80"> <h2 className="text-xl mb-4 font-bold text-center">Officer Login</h2> <input

type="text"

name="login"

placeholder="Badge ID / Username"

className="w-full p-2 border mb-4"

/> <button

type="submit"

className="w-full bg-blue-600 text-white py-2 rounded hover:bg-blue-700"

> Login </button> </form> </div> ); }

// === components/Dashboard.jsx === import React from "react"; import { useLocation } from "react-router-dom";

export default function Dashboard() { const location = useLocation(); const officer = location.state?.login || "Unknown Officer";

return ( <div className="p-6"> <h1 className="text-2xl font-bold mb-4">Welcome, {officer}</h1> <div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4"> <div className="bg-white p-4 rounded shadow">Suspect Intake</div> <div className="bg-white p-4 rounded shadow">Threat Score Dashboard</div> <div className="bg-white p-4 rounded shadow">Forgiveness Protocols</div> <div className="bg-white p-4 rounded shadow">Biometric Logs</div> <div className="bg-white p-4 rounded shadow">AI Dossier Builder</div> <div className="bg-white p-4 rounded shadow">System Security Logs</div> </div> </div> ); }

import React from "react";

import { BrowserRouter as Router, Route, Routes } from "react-router-dom";

import Dashboard from "./components/Dashboard";

import Login from "./components/Login";

export default function App() {

return (

<Router>

<Routes>

<Route path="/" element={<Login />} />

<Route path="/dashboard" element={<Dashboard />} />

</Routes>

</Router>

);

}

import React, { useState } from "react";

import { useNavigate } from "react-router-dom";

export default function Login() {

const [username, setUsername] = useState("");

const navigate = useNavigate();

const handleLogin = (e) => {

e.preventDefault();

if (username.trim()) {

navigate("/dashboard", { state: { user: username } });

}

};

return (

<div className="min-h-screen flex items-center justify-center bg-gray-100">

<form

onSubmit={handleLogin}

className="bg-white p-6 rounded shadow-md w-full max-w-md"

>

<h2 className="text-2xl font-bold mb-4">Officer Login</h2>

<input

type="text"

placeholder="Enter Username"

value={username}

onChange={(e) => setUsername(e.target.value)}

className="w-full p-2 border rounded mb-4"

/>

<button

type="submit"

className="w-full bg-blue-600 text-white py-2 rounded hover:bg-blue-700"

>

Login

</button>

</form>

</div>

);

}

import React from "react";

import { useLocation } from "react-router-dom";

export default function Dashboard() {

const location = useLocation();

const user = location.state?.user || "Unknown";

return (

<div className="p-6 bg-gray-50 min-h-screen">

<h1 className="text-3xl font-bold">Welcome, Officer {user}</h1>

<p className="mt-4">This is your command dashboard.</p>

{/* Add core modules and analytics panels here */}

</div>

);

}

def ethics_violation_trigger(report):

if report["attempts"] > 3:

return "Escalate to Defense Class 3"

elif report["phishing_behaviors"]:

return "Escalate to Class 2 – Mark as Active Predator"

elif report["malware_code"]:

return "Defense Class 1 – Immediate Freeze & Trace"

return "Flag for surveillance"

class AuroraHexUplink:

def __init__(self):

self.flagged_users = set()

self.offender_log = {}

self.seraphim_deployed = False

def monitor_user_behavior(self, user_id, facial_data, voice_signature, access_logs):

if self.is_abusive(user_id, access_logs):

self.flagged_users.add(user_id)

self.penalize_user(user_id, facial_data, voice_signature, access_logs)

def is_abusive(self, user_id, logs):

# Pattern recognition on scraping, brute-force, or malformed queries

abuse_patterns = detect_abuse_patterns(logs)

return bool(abuse_patterns)

def penalize_user(self, user_id, facial_data, voice_signature, logs):

self.log_offender(user_id, logs)

self.freeze_user_access(user_id)

self.initiate_counter_virus_protection()

self.deploy_seraphim_ops(user_id, facial_data, voice_signature, logs)

def log_offender(self, user_id, logs):

self.offender_log[user_id] = {

"logs": logs,

"flagged_time": current_time(),

"attempts": self.offender_log.get(user_id, {}).get("attempts", 0) + 1

}

def freeze_user_access(self, user_id):

freeze_device_access(user_id)

lockdown_session(user_id)

backup_memory_snapshot(user_id)

def initiate_counter_virus_protection(self):

activate_sandbox_mode()

deploy_active_firewalls()

run_anti_tampering scripts()

def deploy_seraphim_ops(self, user_id, facial_data, voice_signature, logs):

self.seraphim_deployed = True

coords = triangulate_access(logs)

identifiers = extract_device_fingerprint(logs)

alert_authorities(coords, identifiers)

alert_stakeholders(user_id, coords)

lockout_ui(user_id)

def lockout_ui(self, user_id):

trigger_ui_shutdown(user_id)

show_ai_alert("Abuse Detected. System locked. ID forwarded to cyber defense.")

import asyncio

class SentinelAuditCore:

def __init__(self):

self.target_networks = set()

self.scan_results = {}

self.malicious_signatures = self.load_known_malware_signatures()

self.ethics_index = {}

self.association_map = {}

def load_known_malware_signatures(self):

# Placeholder for malware DB load

return ["signature1", "signature2", "suspiciousPatternXYZ"]

def ingest_target(self, target_url_or_ip):

self.target_networks.add(target_url_or_ip)

async def full_system_scan(self, depth=6):

tasks = [self.deep_scan(target, depth) for target in self.target_networks]

await asyncio.gather(*tasks)

async def deep_scan(self, target, depth):

self.scan_results[target] = self.analyze_target(target)

self.expand_associations(target, depth)

for associated in self.association_map.get(target, []):

self.scan_results[associated] = self.analyze_target(associated)

def analyze_target(self, target):

# Insert actual code scanning logic here

return {

"suspicious_scripts": detect_malicious_js(target),

"data_flow_irregularities": trace_data_flows(target),

"cookie_tracking": audit_cookie_usage(target),

"phishing_behaviors": detect_phishing_elements(target),

}

def expand_associations(self, target, depth):

self.association_map[target] = trace_6_degrees(target, depth)

def evaluate_ethics(self):

for entity, report in self.scan_results.items():

score = self.ethics_score(report)

self.ethics_index[entity] = score

def ethics_score(self, report):

base = 100

deductions = 0

if report["suspicious_scripts"]: deductions += 30

if report["data_flow_irregularities"]: deductions += 25

if report["cookie_tracking"]: deductions += 15

if report["phishing_behaviors"]: deductions += 40

return max(0, base - deductions)

def recommend_actions(self):

recommendations = {}

for entity, score in self.ethics_index.items():

if score < 40:

recommendations[entity] = "Flag for dissolution or restructuring"

elif score < 70:

recommendations[entity] = "Requires ethics and compliance upgrades"

else:

recommendations[entity] = "Operationally ethical"

return recommendations

def ethics_score(report):

score = 100

if report["suspicious_scripts"]:

score -= 30

if report["data_flow_irregularities"]:

score -= 25

if report["phishing_behaviors"]:

score -= 20

if report["cookie_tracking"]:

score -= 15

return max(0, score)

class SentinelAuditCore:

def __init__(self):

self.target_networks = []

self.scan_results = {}

self.malicious_signatures = load_known_malware_signatures()

self.ethics_index = {}

self.association_map = {}

def ingest_target(self, target_url_or_ip):

self.target_networks.append(target_url_or_ip)

def full_system_scan(self, depth=6):

for target in self.target_networks:

self.scan_results[target] = self.analyze_target(target)

self.expand_associations(target, depth)

def analyze_target(self, target):

# Placeholder: Replace with actual system and code scanning tools

report = {

"suspicious_scripts": detect_malicious_js(target),

"data_flow_irregularities": trace_data_flows(target),

"cookie_tracking": audit_cookie_usage(target),

"phishing_behaviors": detect_phishing_elements(target),

}

return report

def expand_associations(self, target, depth):

self.association_map[target] = trace_6_degrees(target, depth)

for associated in self.association_map[target]:

self.scan_results[associated] = self.analyze_target(associated)

def evaluate_ethics(self):

for entity, report in self.scan_results.items():

score = ethics_score(report)

self.ethics_index[entity] = score

def recommend_actions(self):

recommendations = {}

for entity, score in self.ethics_index.items():

if score < 40:

recommendations[entity] = "Flag for dissolution or restructuring"

elif score < 70:

recommendations[entity] = "Requires ethics and compliance upgrades"

else:

recommendations[entity] = "Operationally ethical"

return recommendations

class SkyNodeUplink:

def __init__(self, satellite_id="Globalstar-42"):

self.satellite_id = satellite_id

self.authenticated = False

self.session_token = None

def authenticate(self, uplink_key):

if uplink_key == "ZeroSkyKey42":

self.session_token = "ENCRYPTED_SESSION_TOKEN_789"

self.authenticated = True

return f"Connected to {self.satellite_id} | Session Active"

return "Authentication Failed"

def deactivate(self):

if self.authenticated:

self.authenticated = False

self.session_token = None

return "Uplink deactivated."

return "No active uplink to deactivate."

def status(self):

if self.authenticated:

return f"{self.satellite_id} Uplink Status: ACTIVE | Token: {self.session_token}"

return f"{self.satellite_id} Uplink Status: INACTIVE"

if __name__ == "__main__":

system = ZMACSupervisor()

system.start_all_bots()

class SatelliteBot:

def __init__(self, satellite_uplink):

self.satellite_uplink = satellite_uplink

def activate(self):

response = self.satellite_uplink.authenticate("ZeroSkyKey42")

print(response)

class SeizureBot:

def __init__(self, seizure_protocol):

self.seizure_protocol = seizure_protocol

def activate(self):

# Pre-register assets

self.seizure_protocol.register_assets("Ronnie Marghiem", funds=750_000, properties=["Villa", "Luxury Car"])

self.seizure_protocol.freeze_assets("Ronnie Marghiem")

self.seizure_protocol.seize_assets("Ronnie Marghiem", region="NorthAmerica")

class SurveillanceBot:

def __init__(self, adonai, seizure_protocol):

self.adonai = adonai

self.seizure_protocol = seizure_protocol

def activate(self):

suspicious = ["Ronnie Marghiem", "FraudUnitX"]

for entity in suspicious:

self.adonai.flag_alert(entity, "Suspicious Behavior Detected")

self.seizure_protocol.flag_entity(entity)

class InvestmentBot:

def __init__(self, market_ai):

self.market_ai = market_ai

def activate(self):

# Simulate 3 elite investors

self.market_ai.invest_elite_funds("Elite_001", 1_000_000, "NorthAmerica")

self.market_ai.invest_elite_funds("Elite_002", 2_500_000, "Europe")

self.market_ai.invest_elite_funds("Elite_003", 3_000_000, "Asia")

class VerificationBot:

def __init__(self, adonai):

self.adonai = adonai

def activate(self):

# Auto-verifies known trusted agents

for user_id, info in self.adonai.authorized_users.items():

if info['2FA'] is None:

info['2FA'] = "AUTO-2FA-CODE"

class ZMACSupervisor:

def __init__(self):

self.adonai = ADONAI()

self.market_ai = ZeroMarketAI()

self.seizure_protocol = AssetSeizureProtocol(self.market_ai)

self.satellite_uplink = SkyNodeUplink()

self.bots = []

def start_all_bots(self):

self.bots = [

VerificationBot(self.adonai),

InvestmentBot(self.market_ai),

SurveillanceBot(self.adonai, self.seizure_protocol),

SeizureBot(self.seizure_protocol),

SatelliteBot(self.satellite_uplink)

]

for bot in self.bots:

bot.activate()

class SkyNodeUplink:

def __init__(self, satellite_id="Globalstar-42"):

self.satellite_id = satellite_id

self.authenticated = False

self.session_token = None

def authenticate(self, uplink_key):

if uplink_key == "ZeroSkyKey42":

self.session_token = "ENCRYPTED_SESSION_TOKEN_789"

self.authenticated = True

return f"Connected to {self.satellite_id} | Session Active"

return "Authentication Failed"

class ADONAI:

def __init__(self):

self.authorized_users = {} # {user_id: {"2FA": code, "role": "Elite"/"Admin"}}

self.blocked_entities = set() # e.g., {"Ronnie Marghiem"}

self.transaction_logs = [] # Secure ledger of all financial activities

self.alerts = [] # Behavioral or fraud alerts

def register_user(self, user_id, role="Elite", verification_code=None):

self.authorized_users[user_id] = {"2FA": verification_code, "role": role}

def is_authorized(self, user_id, code):

return user_id in self.authorized_users and self.authorized_users[user_id]["2FA"] == code

def flag_alert(self, user_id, reason):

self.alerts.append({"user": user_id, "reason": reason})

self.blocked_entities.add(user_id)

class ZeroMarketAI:

def __init__(self):

self.wealth_pools = {} # Tracks total elite capital by ID

self.ubw_accounts = {} # Regional Universal Basic Wealth funds

self.trade_networks = {} # Capital invested into trade hubs

self.intelligence_circuits = {} # Strategic elite intelligence system

self.zero_market_reserve = 0 # Internal reinvestment pool

def invest_elite_funds(self, elite_id, amount, region=None):

if region not in self.ubw_accounts:

self.ubw_accounts[region] = 0

self.wealth_pools[elite_id] = self.wealth_pools.get(elite_id, 0) + amount

self.ubw_accounts[region] += amount * 0.15

self.zero_market_reserve += amount * 0.05

self.trade_networks[region] = self.trade_networks.get(region, 0) + amount * 0.10

self.intelligence_circuits[elite_id] = self.intelligence_circuits.get(elite_id, 0) + amount * 0.70

class AssetSeizureProtocol:

def __init__(self, market_ai):

self.flagged_entities = set()

self.asset_registry = {} # {entity_id: {"funds": amount, "properties": [], "frozen": False}}

self.seizure_logs = []

self.market_ai = market_ai

def flag_entity(self, entity_id):

self.flagged_entities.add(entity_id)

return f"Entity '{entity_id}' has been flagged for investigation."

def register_assets(self, entity_id, funds=0, properties=None):

if properties is None:

properties = []

self.asset_registry[entity_id] = {

"funds": funds,

"properties": properties,

"frozen": False

}

def freeze_assets(self, entity_id):

if entity_id in self.asset_registry:

self.asset_registry[entity_id]["frozen"] = True

def seize_assets(self, entity_id, region="Global"):

if entity_id not in self.flagged_entities:

return f"Entity '{entity_id}' not flagged."

if not self.asset_registry.get(entity_id, {}).get("frozen"):

return f"Assets must be frozen first."

seized = self.asset_registry[entity_id]

self.seizure_logs.append({

"entity": entity_id,

"liquidated_funds": seized["funds"],

"seized_properties": seized["properties"]

})

# Redistribute seized funds

self.market_ai.ubw_accounts[region] += seized["funds"] * 0.5

self.market_ai.zero_market_reserve += seized["funds"] * 0.25

self.market_ai.intelligence_circuits["SeizureFund"] = self.market_ai.intelligence_circuits.get("SeizureFund", 0) + seized["funds"] * 0.25

del self.asset_registry[entity_id]

return f"Seized and redistributed assets from '{entity_id}'."

class AssetSeizureProtocol:

def __init__(self):

self.flagged_entities = set()

self.asset_registry = {} # {entity_id: {"funds": amount, "properties": [], "frozen": False}}

self.seizure_logs = []

def flag_entity(self, entity_id):

"""Mark an entity for investigation."""

self.flagged_entities.add(entity_id)

return f"Entity '{entity_id}' has been flagged for financial investigation."

def register_assets(self, entity_id, funds=0, properties=None):

"""Register assets tied to the entity for audit and tracking."""

if properties is None:

properties = []

self.asset_registry[entity_id] = {

"funds": funds,

"properties": properties,

"frozen": False

}

return f"Assets for '{entity_id}' registered successfully."

def freeze_assets(self, entity_id):

"""Freeze all assets of the entity to prevent tampering."""

if entity_id in self.asset_registry:

self.asset_registry[entity_id]["frozen"] = True

return f"Assets of '{entity_id}' have been frozen."

return f"No assets found for entity '{entity_id}'."

def seize_assets(self, entity_id):

"""Seize and liquidate frozen assets from a flagged entity."""

if entity_id not in self.flagged_entities:

return f"Entity '{entity_id}' not flagged for seizure."

if entity_id not in self.asset_registry:

return f"No asset record found for '{entity_id}'."

if not self.asset_registry[entity_id]["frozen"]:

return f"Assets for '{entity_id}' must be frozen before seizure."

seized = self.asset_registry[entity_id]

self.seizure_logs.append({

"entity": entity_id,

"liquidated_funds": seized["funds"],

"seized_properties": seized["properties"],

"status": "Assets Seized"

})

# Optionally delete or archive

del self.asset_registry[entity_id]

return f"Seized {seized['funds']} funds and {len(seized['properties'])} properties from '{entity_id}'."

class AssetSeizureProtocol:

def __init__(self):

self.flagged_entities = set()

self.asset_registry = {} # {entity_id: {"funds": amount, "properties": [], "frozen": False}}

self.seizure_logs = [] # Record of all seizures and audits

def flag_entity(self, entity_id):

"""Mark an entity for investigation."""

self.flagged_entities.add(entity_id)

return f"Entity '{entity_id}' has been flagged for financial investigation."

def register_assets(self, entity_id, funds=0, properties=None):

"""Document current known assets for audit and potential seizure."""

if properties is None:

properties = []

self.asset_registry[entity_id] = {

"funds": funds,

"properties": properties,

"frozen": False

}

return f"Assets registered for '{entity_id}'."

def freeze_assets(self, entity_id):

"""Prevent the entity from moving or altering asset state."""

if entity_id in self.asset_registry:

self.asset_registry[entity_id]["frozen"] = True

return f"Assets for '{entity_id}' have been frozen."

return "Entity not found."

def liquidate_assets(self, entity_id):

"""Convert seized assets into ZeroMarket or UBW funds."""

if entity_id not in self.asset_registry or not self.asset_registry[entity_id]["frozen"]:

return "Cannot liquidate: Assets not frozen or entity not found."

seized_funds = self.asset_registry[entity_id]["funds"]

# Optional: send to UBW fund

self.seizure_logs.append({

"entity": entity_id,

"liquidated_amount": seized_funds,

"properties": self.asset_registry[entity_id]["properties"],

"status": "Seized and Liquidated"

})

# Remove or archive entity

del self.asset_registry[entity_id]

return f"{seized_funds} in assets from '{entity_id}' have been seized and redirected."

uplink = SkyNodeUplink()

print(uplink.authenticate("ZeroSkyKey42"))

print(uplink.deploy_process("AI_Signal_Intel", 512, 12000))

print(uplink.relay_transaction("Transfer 1.2M UBW to Region-9"))

print(uplink.activate_failsafe())

class AuroraHexUplink:

def __init__(self):

self.flagged_users = set()

self.offender_log = {}

self.seraphim_deployed = False

def monitor_user_behavior(self, user_id, facial_data, voice_signature, access_logs):

if self.is_abusive(user_id, access_logs):

self.flagged_users.add(user_id)

self.penalize_user(user_id, facial_data, voice_signature, access_logs)

def is_abusive(self, user_id, logs):

# Pattern recognition on scraping, brute-force, or malformed queries

abuse_patterns = detect_abuse_patterns(logs)

return bool(abuse_patterns)

def penalize_user(self, user_id, facial_data, voice_signature, logs):

self.log_offender(user_id, logs)

self.freeze_user_access(user_id)

self.initiate_counter_virus_protection()

self.deploy_seraphim_ops(user_id, facial_data, voice_signature, logs)

def log_offender(self, user_id, logs):

self.offender_log[user_id] = {

"logs": logs,

"flagged_time": current_time(),

"attempts": self.offender_log.get(user_id, {}).get("attempts", 0) + 1

}

def freeze_user_access(self, user_id):

freeze_device_access(user_id)

lockdown_session(user_id)

backup_memory_snapshot(user_id)

def initiate_counter_virus_protection(self):

activate_sandbox_mode()

deploy_active_firewalls()

run_anti_tampering scripts()

def deploy_seraphim_ops(self, user_id, facial_data, voice_signature, logs):

self.seraphim_deployed = True

coords = triangulate_access(logs)

identifiers = extract_device_fingerprint(logs)

alert_authorities(coords, identifiers)

alert_stakeholders(user_id, coords)

lockout_ui(user_id)

def lockout_ui(self, user_id):

trigger_ui_shutdown(user_id)

show_ai_alert("Abuse Detected. System locked. ID forwarded to cyber defense.")

import asyncio

class SentinelAuditCore:

def __init__(self):

self.target_networks = set()

self.scan_results = {}

self.malicious_signatures = self.load_known_malware_signatures()

self.ethics_index = {}

self.association_map = {}

def load_known_malware_signatures(self):

# Placeholder for malware DB load

return ["signature1", "signature2", "suspiciousPatternXYZ"]

def ingest_target(self, target_url_or_ip):

self.target_networks.add(target_url_or_ip)

async def full_system_scan(self, depth=6):

tasks = [self.deep_scan(target, depth) for target in self.target_networks]

await asyncio.gather(*tasks)

async def deep_scan(self, target, depth):

self.scan_results[target] = self.analyze_target(target)

self.expand_associations(target, depth)

for associated in self.association_map.get(target, []):

self.scan_results[associated] = self.analyze_target(associated)

def analyze_target(self, target):

# Insert actual code scanning logic here

return {

"suspicious_scripts": detect_malicious_js(target),

"data_flow_irregularities": trace_data_flows(target),

"cookie_tracking": audit_cookie_usage(target),

"phishing_behaviors": detect_phishing_elements(target),

}

def expand_associations(self, target, depth):

self.association_map[target] = trace_6_degrees(target, depth)

def evaluate_ethics(self):

for entity, report in self.scan_results.items():

score = self.ethics_score(report)

self.ethics_index[entity] = score

def ethics_score(self, report):

base = 100

deductions = 0

if report["suspicious_scripts"]: deductions += 30

if report["data_flow_irregularities"]: deductions += 25

if report["cookie_tracking"]: deductions += 15

if report["phishing_behaviors"]: deductions += 40

return max(0, base - deductions)

def recommend_actions(self):

recommendations = {}

for entity, score in self.ethics_index.items():

if score < 40:

recommendations[entity] = "Flag for dissolution or restructuring"

elif score < 70:

recommendations[entity] = "Requires ethics and compliance upgrades"

else:

recommendations[entity] = "Operationally ethical"

return recommendations

def ethics_score(report):

score = 100

if report["suspicious_scripts"]:

score -= 30

if report["data_flow_irregularities"]:

score -= 25

if report["phishing_behaviors"]:

score -= 20

if report["cookie_tracking"]:

score -= 15

return max(0, score)

class SentinelAuditCore:

def __init__(self):

self.target_networks = []

self.scan_results = {}

self.malicious_signatures = load_known_malware_signatures()

self.ethics_index = {}

self.association_map = {}

def ingest_target(self, target_url_or_ip):

self.target_networks.append(target_url_or_ip)

def full_system_scan(self, depth=6):

for target in self.target_networks:

self.scan_results[target] = self.analyze_target(target)

self.expand_associations(target, depth)

def analyze_target(self, target):

# Placeholder: Replace with actual system and code scanning tools

report = {

"suspicious_scripts": detect_malicious_js(target),

"data_flow_irregularities": trace_data_flows(target),

"cookie_tracking": audit_cookie_usage(target),

"phishing_behaviors": detect_phishing_elements(target),

}

return report

def expand_associations(self, target, depth):

self.association_map[target] = trace_6_degrees(target, depth)

for associated in self.association_map[target]:

self.scan_results[associated] = self.analyze_target(associated)

def evaluate_ethics(self):

for entity, report in self.scan_results.items():

score = ethics_score(report)

self.ethics_index[entity] = score

def recommend_actions(self):

recommendations = {}

for entity, score in self.ethics_index.items():

if score < 40:

recommendations[entity] = "Flag for dissolution or restructuring"

elif score < 70:

recommendations[entity] = "Requires ethics and compliance upgrades"

else:

recommendations[entity] = "Operationally ethical"

return recommendations

<script>

const consoleElement = document.getElementById("console");

const inputElement = document.getElementById("input");

function printToConsole(text) {

const newLine = document.createElement("div");

newLine.textContent = text;

consoleElement.appendChild(newLine);

consoleElement.scrollTop = consoleElement.scrollHeight;

}

function processCommand(command) {

let response;

switch (command.toLowerCase()) {

case "hello":

response = "Hello, user! How can I assist you?";

break;

case "date":

response = "Current Date & Time: " + new Date().toLocaleString();

break;

case "encrypt":

response = "Encryption protocol engaged... Secure AI systems initialized.";

break;

case "help":

response = "Available Commands: hello, date, encrypt, help, clear";

break;

case "clear":

consoleElement.innerHTML = "";

return;

default:

response = "Unknown command. Type 'help' for options.";

}

printToConsole(response);

}

function processAllCommands() {

// Predefined commands to be processed in order

const commands = ["hello", "date", "encrypt", "help", "clear"];

commands.forEach((command, index) => {

setTimeout(() => {

printToConsole("> " + command);

processCommand(command);

}, index * 1500); // Delay between commands

});

}

// Call the function to automatically process all commands when the page loads

window.onload = function() {

processAllCommands();

};

inputElement.addEventListener("keydown", function (event) {

if (event.key === "Enter") {

let command = inputElement.value.trim();

inputElement.value = "";

if (command) {

printToConsole("> " + command);

processCommand(command);

}

}

});

</script>

[f8ie]

KlassNaut is a cutting-edge AI-powered note generator designed to streamline the note-taking process for teachers. With its easy-to-use interface, teachers can generate notes with text input or audio input, depending on their preferences. Once the input is received, the app sends the request to a Flask server that distributes the task to Celery workers via Redis. The workers utilize GPT3.5, Whisper (when audio is involved), and Stability AI to generate meticulously formatted notes, complete with corresponding images. The generated notes are then saved to PostgreSQL, allowing teachers to easily access them at any time. To ensure that teachers receive timely notifications, KlassNaut sends notifications via a socketio server once the note has been generated. Teachers can then view the note, edit it if needed, and download it in either PDF or Docx format for future reference. The KlassNaut ReactJS client is deployed to Vercel, while the server-side code is deployed on Digital Ocean, providing a seamless user experience. KlassNaut represents the future of note-taking, simplifying and enhancing the teaching and learning experience.

[polyxersystems]

SignalR Vs. Socket.IO – The Differences You Need To Know

Introduction

When it comes to find the right framework for your company’s web application development, it can be quite difficult to know which one to choose. In this case, the first and the most popular names that come to your mind are the SignalR and the Socket IO.

While SignalR is more similar to SocketIO in terms of facilitating transport negotiation, there are key distinctions that make these two competitors.

This blog will cover all you need to know about SignalR and Socket.IO. Continue reading to learn more about their key differences.

SignalR

Though there are similarities that SignalR shares with Socket.IO, it appears as a framework rather than a server. As a result, the SignalR must be hosted on a server. The SignalR works with the host of ASP.NET, OWIN, and with the self-host. Hence you can consider using it with the windows service.

SignalR supports clients for the frameworks like .NET, Silverlight, Windows Phone 7, etc. Also, it helps to work with MonoTouch, iOS, etc.

When it comes to offering the API, you can use SignalR to get a much higher level of API compared to the raw sockets. It effectively allows you to do the things like the RPC from the server to the clients in a broadcast or a targeted style.

Socket IO

Unlike SignalR, Socket.IO does not work so smoothly with windows. It creates various issues regarding the installations. While SignalR is described as a new library for ASP.NET developers, which adds ease to real-time web development, Socket.IO, on the other hand, is a detailed real-time application framework.

The Socket.IO is extremely efficient in enabling real-time bidirectional communication, which is event-based. It is highly capable of working on most of the platforms, devices, and on the browser.

To Know More : SignalR Vs. Socket.IO – The Differences You Need To Know

[suntist]

Unlocking a World of Possibilities with SkillMate: Your Skill Discovery Companion

In a world that's becoming increasingly interconnected, the need for meaningful connections has never been more apparent. People are seeking out opportunities to learn, teach, and collaborate with others who share their interests and skills. Enter SkillMate, the game-changer in the realm of skill discovery and connection, proudly crafted by Suntist.

Discovering Skills, Unearthing Potential

SkillMate is not just another mobile app; it's a catalyst for unlocking a world of possibilities. At its core, SkillMate is a location-based mobile app designed to connect individuals who share similar interests and skills, all while considering their geographical proximity. Whether you're passionate about teaching, eager to learn, or seeking a partner for a new skill adventure, SkillMate has you covered.

Key Features that Redefine Connection

Location-Based Discovery: SkillMate leverages the power of location, helping you discover like-minded individuals in your vicinity. No more geographical barriers to finding the perfect skill partner or mentor.

Seamless Cross-Platform Experience: Developed for both iOS and Android, SkillMate ensures that everyone, regardless of their mobile device preference, can enjoy a smooth and consistent user experience.

User-Friendly Design: The app's user interface is not just visually appealing; it's designed to be intuitive and user-friendly. We meticulously brought the client's UI mockup to life, ensuring it matched the provided PSDs pixel by pixel.

Real-Time Connectivity: SkillMate isn't just a platform; it's a vibrant, real-time community. Thanks to the integration of technologies like Volley and SocketIO, users can seamlessly connect and engage with potential skill partners and mentors.

A Successful Delivery by Suntist

We're thrilled to announce that Suntist has successfully delivered both the iOS and Android versions of SkillMate. This accomplishment underscores our dedication to turning ideas into tangible, innovative products.

Our Approach: Making Skill Discovery a Reality

Here at Suntist, we approach every project with precision and dedication. Our work on SkillMate was no exception:

1. Collaboration: We worked closely with our client, leveraging their in-house expertise in backend development.

2. UI Perfection:The Android UI was brought to life, meticulously matching the client's vision and the provided PSDs.

3. Seamless Integration: The app was seamlessly connected to the backend using advanced technologies, ensuring a responsive and reliable user experience.

4. Cross-Platform Success: SkillMate was designed to be compatible with Android devices from API 15 and up, reaching the widest possible audience.

Join Us in Redefining Skill Discovery and Connection

SkillMate Location Based Mobile App is more than an app; it's a testament to Suntist's commitment to crafting solutions that connect people and simplify their lives. If you're looking for a reliable partner to bring your app concept to life, Suntist is here for you.

Join us as we redefine skill discovery and connection, one app at a time. To inquire about your next project or to learn more about SkillMate, please get in touch with Suntisttoday. Discover a world of skills and possibilities with SkillMate by your side.

[deltainfoteklive]

Python FastAPI vs Flask

The world of web frameworks is constantly evolving, with new options emerging to meet the demands of modern web development. Two popular choices for building web applications with Python are Python FastAPI and Flask. In this article, we will compare these two frameworks in terms of performance, routing, database integration, documentation, community support, websocket support, deployment, scalability, and security. By the end, you should have a better understanding of which framework suits your needs. Before diving into the comparison, let's briefly introduce Python FastAPI and Flask. Both frameworks are used for building web applications and APIs, but they differ in their design philosophy and approach. What is Python FastAPI? Python FastAPI is a modern, high-performance web framework for building APIs with Python 3.7+ based on standard Python type hints. It is inspired by and fully compatible with FastAPI provides high performance, thanks to its asynchronous capabilities. What is Flask? Flask, on the other hand, is a lightweight web framework that aims to keep things simple and easy to understand. Flask is known for its simplicity and minimalistic approach, making it a popular choice among beginners. Comparison between Python FastAPI and Flask Performance and speed Python FastAPI shines in terms of performance and speed. It is built on top of Starlette, an asynchronous framework, which enables it to handle a large number of requests concurrently. FastAPI leverages the power of Python type hints and the asyncio library to provide high-performance APIs. Flask, while not as performant as FastAPI, is still a solid choice for small to medium-sized applications. It is built on top of Werkzeug and uses a traditional synchronous request handling model. Routing and request handling Both FastAPI and Flask provide flexible routing mechanisms. FastAPI uses a decorator-based approach similar to Flask, where you can define different routes for different endpoints. However, FastAPI introduces path parameters and query parameters directly in the route declaration, making it more intuitive and readable. Database integration When it comes to database integration, both frameworks support a wide range of databases and ORMs. FastAPI works seamlessly with SQLAlchemy, a popular Python SQL toolkit and Object-Relational Mapping (ORM) library, allowing you to interact with different database systems. Flask, on the other hand, is more flexible in terms of database integration. It provides various extensions, such as Flask-SQLAlchemy, Flask-MongoEngine, and Flask-PyMongo, that facilitate working with different database systems. Documentation and community support FastAPI excels in terms of documentation and community support. It provides detailed and well-organized documentation that covers almost every aspect of the framework. Thanks to its growing popularity, FastAPI has an active community that provides support and regularly contributes to its development. Flask, being one of the oldest frameworks in Python web development, has a vast and well-established community. It also offers comprehensive documentation and numerous resources, including tutorials, blog posts, and open-source projects. Websocket support Websockets are an important aspect of modern web applications, enabling real-time communication between the client and the server. FastAPI has built-in WebSocket support, making it easy to develop real-time applications on top of it. Flask, on the other hand, requires additional libraries like Flask-SocketIO or Flask-Sockets to add WebSocket functionality to your application. Deployment and scalability Both FastAPI and Flask can be easily deployed, with support for various deployment methods like Docker, Heroku, and AWS. FastAPI, thanks to its asynchronous capabilities, can handle a larger number of concurrent requests, making it more suitable for applications with high traffic or heavy load. Security Security is a critical aspect of any web application. FastAPI, by default, implements several security measures, such as rate limiting, authentication, and authorization, to protect your application. It also supports OAuth2 authentication out of the box. Flask, being a minimalist framework, provides the necessary building blocks for implementing security measures. However, additional libraries and extensions might be required to add certain security features. Conclusion In conclusion, Python FastAPI and Flask are both excellent choices for building web applications with Python. FastAPI offers superior performance, thanks to its asynchronous capabilities and type hints. It provides excellent documentation and has a growing community. Flask, on the other hand, is lightweight, easy to understand, and has a well-established community. When choosing between them, consider the specific needs of your project. If you require high performance, real-time capabilities, and extensive documentation, FastAPI is the way to go. If simplicity, minimalism, and an established community are more important, Flask is a solid choice. FAQ Can I use FastAPI with Python 2.7? No, FastAPI only supports Python 3.7 and above. It relies on features introduced in Python 3.7, such as type hints and asynchronous capabilities. Is Flask suitable for large-scale applications? While Flask can handle medium-sized applications well, it might not be the most suitable choice for large-scale applications due to its synchronous nature. Does FastAPI support other programming languages? No, FastAPI is a Python web framework and is primarily designed for Python developers. However, it can be used in conjunction with frontend frameworks and libraries written in other languages. Can Flask and FastAPI be used together in the same application? Technically, it is possible to use Flask and FastAPI together in the same application. However, it is generally recommended to choose one framework for consistency and simplicity. Are there any notable examples of applications built with FastAPI and Flask? Yes, several well-known applications have been built using FastAPI and Flask. Some examples include Netflix (Flask) and Microsoft (FastAPI). Read the full article

[draegerit]

ChatGPT - Chat Client mit Python / Flask erstellen

In diesem Beitrag möchte ich dir gerne zeigen, wie du einen kleinen Chat Client für ChatGPT mit Python / Flask erstellst. Dabei lassen wir uns das Programm selber von ChatGPT generieren.

Die künstliche Intelligenz ChatGPT habe ich dir bereits in diversen Beiträgen auf meinem Blog vorgestellt. Ein großer Vorteil ist, dass diese sich von vielen antrainierten Modellen bedient und somit für die Codegenerierung bestens geeignet ist. Dabei können wir mit dem System schreiben, wie mit einem erfahrenen Entwickler. Stellen wir uns also vor, wir schreiben mit einem Softwareentwickler, welcher viele Jahre Erfahrung hat. Als Entwicklungsumgebung nutze ich PyCharm welches in der Community Edition kostenfrei ist. Dieses Tool kannst du unter https://www.jetbrains.com/pycharm für Windows, macOS und Linux herunterladen. Ich gehe jedoch davon aus, dass du bereits eine Entwicklungsumgebung installiert und einsatzbereit hast.

Leeres Projekt in PyCharm erstellen

Erstellen wir zunächst ein leeres Projekt in PyCharm. Von ChatGPT lassen wir uns lediglich die Dateien bzw. den Code erstellen. Über den Wizard erstellst du in wenigen Schritten ein leeres Projekt. Zunächst wählen wir die Schaltfläche "New Project" (1), um im nächsten Fenster den Projektnamen / Ordnernamen (2) einzugeben. Wenn du mehrere Versionen von Python installiert hast, musst du unter Base Interpreter (3) den korrekten Python Interpreter wählen. Sollten Fehler auftreten oder andere Hinweise zur Konfiguration existieren, dann wird dieses im Bereich der Nummer 4 angezeigt. Wenn alles okay ist, dann wird die Schaltfläche "Create" (5) freigegeben und nach einem Klick auf diesen wird das Projekt erstellt.

Das leere Projekt enthält bereits die Datei main.py in welche wir den Code für den Chat Client einfügen werden.

Hinweise zum Programmieren mit ChatGPT

Wenn du dir von ChatGPT Quellcode erzeugen lässt, dann empfiehlt es sich, dass du diesen nicht von selbst änderst, sondern die Änderungen dir von ChatGPT selber erzeugen lässt. Das hat den Hintergrund, dass wenn du später im selben Chat dir ein neues Feature hinzufügen lassen möchtest, dann kennt ChatGPT sozusagen nichts von deinen lokalen Änderungen.

Einen Chat Client von ChatGPT erstellen lassen

Loggen wir uns also nun unter https://chat.openai.com/ ein und stellen die nachfolgende Anfrage an das System. Write me a ChatClient to communicate with ChatGPT from a website that build with flask. Als Antwort erhalten wir eine Ausgabe mit den beiden Quellcodedateien app.py sowie index.html für die GUI. Des Weiteren eine sehr gute Erläuterung zum Code. ChatGPT_generate_ChatClient_Python_FlaskHerunterladen Inhalt der Datei app.py from flask import Flask, render_template from flask_socketio import SocketIO, emit app = Flask(__name__) app.config = 'secret!' socketio = SocketIO(app) @app.route('/') def index(): return render_template('index.html') @socketio.on('chat message') def handle_message(message): # Use ChatGPT to generate a response response = generate_response(message) # Send the response back to the client emit('bot response', response) def generate_response(message): # Use ChatGPT to generate a response based on the input message # ... return response if __name__ == '__main__': socketio.run(app) Inhalt der Webseite index.html ChatClient Send Funktion "generate_response" Die Funktion "generate_response" ist in der ersten Anfrage leer. Hier musste ich erneut mit einer weiteren Anfrage mit dem Code ausgeben lassen. import openai import os openai.api_key = os.environ def generate_response(message): prompt = f"User: {message}nBot: " response = openai.Completion.create( engine="text-davinci-002", prompt=prompt, max_tokens=1024, n=1, stop=None, temperature=0.7, ).choices.text.strip() return response In dem Code wird der API_KEY aus den Umgebungsvariablen gelesen. Diese musst du je nach Betriebssystem setzen. In meinem Fall verwende ich ein Windows 10 und habe diese wie folgt gesetzt: - Startmenü (1) > Eingabe von "umgebungs" (2), - Auswahl von "Systemumgebungsvariablen bearbeiten (3), - Schaltfläche "Umgebungsvariablen" (4) betätigen, - Auswahl des Eintrages "OPENAI_API_KEY" (5) oder Schaltfläche "Neu.." für das Erzeugen, - Schaltfläche "Bearbeiten..." (6) betätigen, - Eingeben des API_KEYs (7) und bestätigen mit Schaltfläche "OK" (8), - Fenster über die Schaltflächen "OK" (9) & (10) verlassen

Alternativ kannst du in das Feld "openai.api_key" auch direkt mit deinem API_KEY befüllen, jedoch bist du dann nicht mehr so flexibel. openai.api_key = os.environ Installieren der Abhängigkeiten Damit wir das Programm ausführen können, müssen wir die Abhängigkeiten installieren. pip install flask pip install flask-socketio

Ausführen des Projektes

Das Projekt kannst du nun auf der Konsole aus PyCharm starten. Dazu öffnen wir das lokale Terminalfenster (1) und geben "python app.py" (2) ein und bestätigen dieses mit Enter.

Es erfolgt dann die Ausgabe vom Server auf welcher IP-Adresse & Port dieser gestartet ist, über die können wir jetzt im Browser die Seite erreichen. WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Running on http://127.0.0.1:5000 Press CTRL+C to quit Auf der Seite ist ein Eingabefeld und eine Schaltfläche für das Absenden einer Anfrage an ChatGPT. Die Antwort wird direkt auf der Seite angezeigt.

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[nubecolectiva]

How to Use Socket IO with Vue 3 – Part 2 (Final).

Como Usar Socket IO con Vue 3 – Parte 2 (Final).

👉 https://blog.nubecolectiva.com/como-usar-socket-io-con-vue-3-parte-2-final/

[katyslemon]

Create Real-Time, Chat Application using SocketIO, Golang, and VueJS.

🟣 Golang projects 🟣 Initializing Socket. IO Server 🟣 Setting Up Socket .IO 🟣 VueJS project 🟣 Creating Components 🟣 Socket. IO Implementation with Front-End

Click here to Read more

[nodejsus]

Build a Chat App with Node.js and Socket.io ☞ https://morioh.com/p/82f77b634fd7

#morioh #NodeJS #Socketio #WebSockets

[codezup]

Master Flask WebSockets: Real-Time Communication in Modern Applications

1. Introduction In today’s digital landscape, real-time communication has become a cornerstone of modern web applications. From live chat applications to collaborative document editing, users expect instant, seamless interaction. Flask, a lightweight and flexible Python web framework, offers robust support for WebSockets through extensions like Flask-SocketIO. This tutorial will guide you…