### Smart Contract Automation Example (Conceptual)

Source: https://coraxcolab.com/en/tjanster/web3

This conceptual example illustrates how a smart contract can automate processes, such as releasing a payment upon confirmation of a completed task. It highlights the use of smart contracts for automating business logic within a supply chain, triggered by external confirmations like those from a GAPbot.

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

contract SupplyChainAutomation {
    address public owner;
    address public gapbotAddress;
    bool public missionCompleted = false;
    uint public paymentAmount = 100 ether;
    address payable public recipientAddress;

    event MissionCompletionConfirmed(address indexed completer);
    event PaymentReleased(address indexed recipient, uint amount);

    modifier onlyOwner() {
        require(msg.sender == owner, "Only the owner can call this function.");
        _;
    }

    modifier onlyGapbot() {
        require(msg.sender == gapbotAddress, "Only the GAPbot can call this function.");
        _;
    }

    constructor(address payable _recipientAddress, address _gapbotAddress) {
        owner = msg.sender;
        recipientAddress = _recipientAddress;
        gapbotAddress = _gapbotAddress;
    }

    function confirmMissionCompletion() public onlyGapbot {
        require(!missionCompleted, "Mission already completed.");
        missionCompleted = true;
        emit MissionCompletionConfirmed(msg.sender);
        releasePayment();
    }

    function releasePayment() private {
        require(missionCompleted, "Mission not completed yet.");
        require(address(this).balance >= paymentAmount, "Insufficient contract balance.");
        
        (bool success, ) = recipientAddress.call{value: paymentAmount}("");
        require(success, "Payment release failed.");
        
        emit PaymentReleased(recipientAddress, paymentAmount);
    }

    function setPaymentAmount(uint _amount) public onlyOwner {
        paymentAmount = _amount;
    }

    function setGapbotAddress(address _newGapbotAddress) public onlyOwner {
        gapbotAddress = _newGapbotAddress;
    }

    // Fallback function to receive Ether
    receive() external payable {}
}
```

--------------------------------

### API Layer Implementation (Python - Flask & FastAPI)

Source: https://coraxcolab.com/en/gap

Demonstrates the hybrid API architecture using Flask for business logic and FastAPI for high-performance data streams in Python. Includes initialization and specific endpoint examples.

```python
# app.py (Flask - Business Logic)
from flask import Flask

app = Flask(__name__)

@app.route('/api/business/process')
def process_business_logic():
    return "Business logic processed"

# main.py (FastAPI - Real-time Data Streams)
from fastapi import FastAPI

main_app = FastAPI()

@main_app.get('/api/data/stream')
async def get_data_stream():
    return {"message": "Real-time data stream"}

# gap_complete_system.py
# This script would initialize both Flask and FastAPI applications
def initialize_system():
    print("Initializing GAP complete system...")
    # Logic to start Flask app
    # Logic to start FastAPI app
    print("System initialized.")

# patrol_endpoints.py
# Example for patrol mission endpoints
def create_patrol_mission(schedule, area):
    print(f"Creating patrol mission for {area} on schedule {schedule}")

# xai_endpoints.py
# Example for Explainable AI endpoints
def get_ai_explanation(model_id, input_data):
    print(f"Getting explanation for model {model_id} with input {input_data}")
```

--------------------------------

### GAP Platform Plugin System Modules

Source: https://coraxcolab.com/en/gap

This snippet details the extensibility of the GAP platform through its plugin system, which offers over 200 modules. It highlights AI modules for predictive maintenance and anomaly detection, industrial protocol integrations, monitoring systems, and a comprehensive security layer with AI-driven threat detection.

```text
AI Modules (predictive_maintenance, anomaly_detection, multi_model_ensemble, NLP)
Industrial Protocols (Modbus, OPC-UA, Ethernet/IP, BACnet)
Monitoring (Health Monitoring, Telemetry Systems, Alert Management)
Security Layer (Zero Trust, PQC, RBAC, JWT, ai_threat_detection_advanced.py)
```

--------------------------------

### Hybrid Backend Architecture: Flask and FastAPI Integration

Source: https://coraxcolab.com/en/gap

Demonstrates the integration of Flask for stable transactional processes and FastAPI for high-performance data streams within the GAP platform. This architecture ensures robustness for business logic and speed for real-time data management.

```python
import flask
import fastapi

# Flask app for critical processes
flask_app = flask.Flask(__name__)

@flask_app.route('/auth')
def authenticate_user():
    return "User authenticated"

# FastAPI app for high-performance data streams
fastapi_app = fastapi.FastAPI()

@fastapi_app.websocket('/telemetry')
async def handle_telemetry(websocket: fastapi.WebSocket):
    await websocket.accept()
    while True:
        data = await websocket.receive_text()
        # Process telemetry data
        await websocket.send_text(f"Telemetry received: {data}")

# Example of running both (in a real scenario, use a proper ASGI server like uvicorn)
if __name__ == "__main__":
    # This is a simplified example. In production, use a tool like 'gunicorn' with 'uvicorn workers'
    # to run both Flask and FastAPI applications concurrently.
    print("Flask app running on http://127.0.0.1:5000")
    print("FastAPI app running on ws://127.0.0.1:8000/telemetry")
    # flask_app.run(port=5000)
    # import uvicorn
    # uvicorn.run(fastapi_app, host="127.0.0.1", port=8000)
    pass

```

--------------------------------

### GAPbot Hardware Abstraction Layer (HAL) in Python

Source: https://coraxcolab.com/en/gap

This Python code snippet illustrates the Hardware Abstraction Layer (HAL) for the GAPbot, separating software from physical hardware. It includes abstracted controllers for actuators like motors and servos, and interfaces for various sensors such as cameras (RGB/IR), ultrasonic, infrared, and edge sensors.

```python
class MotorController:
    def move(self, speed):
        pass

class ServoController:
    def set_angle(self, angle):
        pass

class Camera:
    def capture_rgb(self):
        pass

    def capture_ir(self):
        pass

class UltrasonicSensor:
    def get_distance(self):
        pass

class InfraredSensor:
    def detect(self):
        pass

class EdgeSensors:
    def process_data(self):
        pass

# Example HAL usage
class HardwareAbstractionLayer:
    def __init__(self):
        self.motor_controller = MotorController()
        self.servo_controller = ServoController()
        self.camera = Camera()
        self.ultrasonic_sensor = UltrasonicSensor()
        self.infrared_sensor = InfraredSensor()
        self.edge_sensors = EdgeSensors()

    def perform_movement(self, speed, angle):
        self.motor_controller.move(speed)
        self.servo_controller.set_angle(angle)

    def get_sensor_data(self):
        rgb_image = self.camera.capture_rgb()
        distance = self.ultrasonic_sensor.get_distance()
        # ... other sensor readings
```

--------------------------------

### Industrial Protocol Manager for GAP

Source: https://coraxcolab.com/en/gap

Illustrates the `protocol_manager.py` script's role in uniformly handling various industrial communication protocols. This enables seamless integration with diverse hardware, from modern robots to legacy factory machines.

```python
import opcua
import pymodbus.server
import paho.mqtt.client as mqtt

class ProtocolManager:
    def __init__(self):
        self.protocols = {}

    def register_protocol(self, name, handler):
        self.protocols[name] = handler
        print(f"Registered protocol: {name}")

    def send_command(self, protocol_name, device_address, command):
        if protocol_name in self.protocols:
            handler = self.protocols[protocol_name]
            # In a real implementation, this would involve specific logic for each protocol
            print(f"Sending command '{command}' to {device_address} via {protocol_name}")
            # Example: handler.send(device_address, command)
            return True
        else:
            print(f"Error: Protocol '{protocol_name}' not supported.")
            return False

# Example Usage (Conceptual):
# protocol_manager = ProtocolManager()
# protocol_manager.register_protocol('OPC-UA', opcua_handler)
# protocol_manager.register_protocol('Modbus', modbus_handler)
# protocol_manager.register_protocol('MQTT', mqtt_handler)
#
# protocol_manager.send_command('Modbus', '192.168.1.10', 'READ_HOLDING_REGISTERS, 0, 5')

```

--------------------------------

### AI-Driven Resource Optimization Modules

Source: https://coraxcolab.com/en/gap

Showcases conceptual Python modules for `predictive_maintenance` and `anomaly_detection` used in the GAP ecosystem. These AI components optimize resource utilization, reduce downtime, and minimize waste.

```python
import numpy as np
import pandas as pd

class PredictiveMaintenance:
    def __init__(self, historical_data):
        self.model = self._train_model(historical_data)

    def _train_model(self, data):
        # Placeholder for training a predictive maintenance model (e.g., using LSTMs, Random Forests)
        print("Training predictive maintenance model...")
        # Example: model = train_your_model(data)
        return "trained_model_object"

    def predict_failure(self, current_data):
        # Placeholder for predicting remaining useful life or probability of failure
        print("Predicting potential failures...")
        # Example: prediction = self.model.predict(current_data)
        return np.random.rand() # Simulate prediction

class AnomalyDetection:
    def __init__(self, baseline_data):
        self.baseline = baseline_data
        # Placeholder for initializing anomaly detection algorithms (e.g., Isolation Forest, One-Class SVM)
        print("Initializing anomaly detection...")

    def detect_anomaly(self, current_data):
        # Placeholder for detecting deviations from normal operating conditions
        print("Detecting anomalies...")
        # Example: is_anomaly = self.is_anomaly(current_data, self.baseline)
        return np.random.choice([True, False], p=[0.05, 0.95]) # Simulate detection

# Example Usage (Conceptual):
# historical_sensor_data = pd.read_csv('sensor_history.csv')
# pm_optimizer = PredictiveMaintenance(historical_sensor_data)
#
# current_sensor_readings = pd.read_csv('current_sensors.csv')
# failure_prob = pm_optimizer.predict_failure(current_sensor_readings)
# print(f"Probability of failure: {failure_prob:.2f}")
#
# ad_detector = AnomalyDetection(baseline_data=historical_sensor_data)
# if ad_detector.detect_anomaly(current_sensor_readings):
#     print("Anomaly detected in system performance!")

```

--------------------------------

### GAPbot Edge AI Systems in Python

Source: https://coraxcolab.com/en/gap

This Python code snippet showcases the Edge AI capabilities of the GAPbot, running over 25 AI modules locally for low latency. It includes agricultural AI for plant, disease, and weed identification, an AI inference optimizer using YOLOv8 and TFLite, and integration with LLMs via Langchain for complex decision-making and ethical AI security.

```python
class PlantRecognition:
    def identify(self, image):
        pass

class DiseaseIdentifier:
    def detect(self, image):
        pass

class WeedIdentifier:
    def identify(self, image):
        pass

class EdgeAIProcessor:
    def __init__(self, model_path):
        # Uses YOLOv8 and TensorFlow Lite (TFLite)
        pass

    def infer(self, data):
        pass

class LangchainAIAgent:
    def __init__(self):
        # Integration with Large Language Models (LLM)
        pass

    def make_decision(self, context):
        pass

class EthicalAISecurity:
    def ensure_compliance(self, ai_output):
        pass

# Example Edge AI usage
class GAPbotAI:
    def __init__(self):
        self.plant_recognizer = PlantRecognition()
        self.disease_detector = DiseaseIdentifier()
        self.weed_identifier = WeedIdentifier()
        self.ai_processor = EdgeAIProcessor(model_path="models/yolov8.tflite")
        self.llm_agent = LangchainAIAgent()
        self.ethical_checker = EthicalAISecurity()

    def process_environment(self, image):
        plant_type = self.plant_recognizer.identify(image)
        disease = self.disease_detector.detect(image)
        weed = self.weed_identifier.identify(image)
        # ... further AI processing
```

--------------------------------

### GAPbot Core Logic and Monitoring in Python

Source: https://coraxcolab.com/en/gap

This Python code snippet represents the core logic and monitoring system of the GAPbot, managed by `robot_core.py`. It handles task states, battery management, navigation modes, and utilizes `EdgeSystemWatchdog` to ensure the continuous operation of local services. It also includes PID control for precise movement.

```python
class TaskStatus:
    pass

class BatteryStatus:
    # Phase I autonomous charging
    pass

class NavigationMode:
    pass

class EdgeSystemWatchdog:
    def __init__(self):
        # Monitors AI, servo, MQTT, camera, network services
        pass

    def monitor_services(self):
        # Logic to check and restart services
        pass

class PID:
    def __init__(self, kp, ki, kd):
        pass

    def update(self, error):
        pass

# robot_core.py
class RobotCore:
    def __init__(self):
        self.task_status = TaskStatus()
        self.battery_status = BatteryStatus()
        self.navigation_mode = NavigationMode()
        self.watchdog = EdgeSystemWatchdog()
        self.pid_controller = PID(kp=1.0, ki=0.1, kd=0.01)

    def run(self):
        # Main loop for robot operation
        self.watchdog.monitor_services()
        # ... other core logic
        pass

if __name__ == "__main__":
    robot = RobotCore()
    robot.run()
```

--------------------------------

### AI Threat Detection and MQTT Command Authorization (Python)

Source: https://coraxcolab.com/en/gap

Ensures real-time threat mitigation and protects against unauthorized robot control using Python scripts. These are critical for maintaining system integrity.

```python
# ai_threat_detection_advanced.py
import ai_threat_detection

def monitor_threats():
    while True:
        if ai_threat_detection.detect_threat():
            ai_threat_detection.mitigate_threat()

# mqtt_command_authorizer.py
import mqtt_client

def authorize_command(command):
    if command.is_authorized():
        mqtt_client.publish(command)
    else:
        print("Unauthorized command rejected.")
```

--------------------------------

### Langchain AI Agent for Cognitive Tasks

Source: https://coraxcolab.com/en/gap

Illustrates the use of `langchain_ai_agent.py` for integrating Large Language Models (LLMs) like Phi-3. This enables complex reasoning, self-healing actions, and cognitive capabilities for the robot.

```python
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
# Assuming a specific LLM integration, e.g., Ollama for local models
from langchain_community.llms import Ollama

class LangchainAIAgent:
    def __init__(self, model_name="phi3"):
        # Initialize the LLM (e.g., Phi-3 running locally via Ollama)
        self.llm = Ollama(model=model_name)
        self.prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a helpful AI assistant for a robot. Respond concisely."),
            ("user", "{input}")
        ])
        self.output_parser = StrOutputParser()
        self.chain = self.prompt | self.llm | self.output_parser
        print(f"Langchain AI Agent initialized with model: {model_name}")

    def get_reasoning(self, user_input):
        # Generate a response based on the input using the LLM chain
        print(f"Generating reasoning for: '{user_input}'")
        response = self.chain.invoke({"input": user_input})
        return response

    def perform_self_healing_action(self, error_description):
        # Example of using the agent to suggest a self-healing action
        prompt_text = f"The robot encountered an error: '{error_description}'. Suggest a simple self-healing action."
        action = self.get_reasoning(prompt_text)
        print(f"Suggested self-healing action: {action}")
        return action

# Example Usage (Conceptual):
# ai_agent = LangchainAIAgent(model_name="phi3")
#
# current_status = "Battery level is low and navigation sensors are offline."
# suggested_action = ai_agent.perform_self_healing_action(current_status)
#
# print(f"AI Agent Response: {suggested_action}")

```

--------------------------------

### GAPbot Industrial Protocol Integration in Python

Source: https://coraxcolab.com/en/gap

This Python code snippet demonstrates the integration of various industrial protocols within the GAP platform, managed by a `protocol_manager.py`. It enables GAPbot to communicate directly with industrial hardware supporting protocols like Modbus (RTU/TCP), OPC-UA, Ethernet/IP, and BACnet.

```python
class ModbusClient:
    def read_register(self, address):
        pass

    def write_register(self, address, value):
        pass

class OPCUAClient:
    def read_node(self, node_id):
        pass

class EthernetIPClient:
    def send_data(self, tag, data):
        pass

class BACnetClient:
    def read_property(self, object_id, property_id):
        pass

class ProtocolManager:
    def __init__(self):
        self.modbus = ModbusClient()
        self.opcua = OPCUAClient()
        self.ethernetip = EthernetIPClient()
        self.bacnet = BACnetClient()

    def communicate(self, protocol, device_address, data):
        if protocol == "Modbus":
            # Handle Modbus communication
            pass
        elif protocol == "OPC-UA":
            # Handle OPC-UA communication
            pass
        # ... other protocols

# Example usage
protocol_manager = ProtocolManager()
protocol_manager.communicate("Modbus", "192.168.1.100", {"read": [1, 2]})
protocol_manager.communicate("OPC-UA", "opc.tcp://server:4840", {"read": "ns=1;s=MyVariable"})
```

--------------------------------

### GAP Platform Core Functionality Modules

Source: https://coraxcolab.com/en/gap

This snippet outlines the core functionality modules of the GAP platform, each addressing specific aspects of robotics and industrial automation. These modules cover areas like workflow automation, AI compliance, precision agriculture, industrial system integration, digital twin creation, swarm management, blockchain applications, and cloud/edge computing.

```text
automation/
workflow/
ai_act_compliance/
precision_agriculture/
industrial_systems/
digital_twin/
ar_vr/
swarm_management/
blockchain/
cloud_edge/
edge_computing/
gapbot_integration/
```

--------------------------------

### Weed Navigation System (Python)

Source: https://coraxcolab.com/en/gap

A Python-based system for navigating and targeting weeds in agricultural fields, crucial for automated spraying operations.

```python
# weed_navigation_system.py
import navigation_module
import spraying_module

def navigate_and_spray(target_location):
    path = navigation_module.plan_path(target_location)
    navigation_module.execute_path(path)
    spraying_module.target_and_spray(target_location)
```

--------------------------------

### Ethical AI Security and XAI Endpoints

Source: https://coraxcolab.com/en/gap

Demonstrates the implementation of `ethical_ai_security.py` and `xai_endpoints.py` for ensuring secure, ethical AI operations and providing explainability (XAI) for AI decisions, crucial for EU AI Act compliance.

```python
import json

class EthicalAISecurity:
    def __init__(self):
        # Placeholder for security mechanisms like access control, data encryption
        print("Ethical AI Security module initialized.")

    def ensure_secure_operation(self, ai_model):
        # Logic to enforce security policies on AI model usage
        print(f"Ensuring secure operation for AI model: {ai_model}")
        return True

    def check_compliance(self, ai_decision):
        # Logic to verify AI decisions against ethical guidelines and regulations
        print(f"Checking ethical compliance for AI decision: {ai_decision}")
        # Example: return is_compliant(ai_decision)
        return True

class XAIEndpoints:
    def __init__(self):
        # Placeholder for mechanisms to provide explanations for AI decisions
        print("XAI Endpoints module initialized.")

    def get_explanation(self, ai_decision_id):
        # Retrieve and format explanation for a specific AI decision
        print(f"Retrieving explanation for decision ID: {ai_decision_id}")
        # Example: explanation = retrieve_explanation_from_log(ai_decision_id)
        explanation = {
            "decision_id": ai_decision_id,
            "reason": "Based on sensor data X and historical pattern Y, action Z was recommended.",
            "confidence": 0.85
        }
        return json.dumps(explanation)

# Example Usage (Conceptual):
# ethical_security = EthicalAISecurity()
# xai = XAIEndpoints()
#
# ai_model_instance = "object_detection_v1"
# if ethical_security.ensure_secure_operation(ai_model_instance):
#     print("AI model is operating securely.")
#
# sample_decision = {"action": "avoid_obstacle", "target": "object_123"}
# if ethical_security.check_compliance(sample_decision):
#     print("AI decision is ethically compliant.")
#
# decision_identifier = "decision_abc123"
# explanation_json = xai.get_explanation(decision_identifier)
# print(f"AI Decision Explanation: {explanation_json}")

```