gen.sh

#!/bin/bash
# gen.sh (Main Setup and Deployment Script)

set -euo pipefail
IFS=$'\n\t'

# Function to create directories
create_dir() {
    if [ ! -d "$1" ]; then
        mkdir -p "$1"
        echo "Created directory: $1"
    else
        echo "Directory already exists: $1"
    fi
}

# Function to create files with content
create_file() {
    local filepath=$1
    local content=$2
    create_dir "$(dirname "$filepath")"
    if [ ! -f "$filepath" ]; then
        echo -e "$content" > "$filepath"
        echo "Created file: $filepath"
    else
        echo "File already exists: $filepath"
    fi
}

# 1. Root Files
create_file "README.md" "# AI Platform

A comprehensive AI platform encompassing various domains such as chatbots, predictive analytics, personalization, cybersecurity, content creation, healthcare, AI consulting, VR/AR, supply chain optimization, AutoML, and more.

## Features

- **AI Chatbot:** Natural language interactions powered by GPT-4.
- **Predictive Analytics:** Risk assessment, customer behavior prediction, and time series forecasting.
- - **Personalization Engine:** Real-time and hybrid recommendation systems.
- **Cybersecurity AI:** Threat intelligence, incident response, and anomaly detection.
- **Content Creation AI:** Generate text, images, and multimedia content.
- **Healthcare AI:** Drug discovery and medical image diagnostics.
- **AI Consulting:** Strategy development using data analysis.
- **VR/AR AI:** Enhance VR/AR experiences with AI-powered features.
- **Supply Chain AI:** Optimize demand prediction, inventory management, and logistics.
- **AutoML:** Hyperparameter tuning and model selection with Optuna.
- **C++ and Rust Anomaly Detectors:** High-performance anomaly detection using C++ and Rust.

## Getting Started

Follow the [Installation Guide](docs/user_manual/installation_guide.md) to set up the project.

## Documentation

Comprehensive documentation is available in the \`docs\` directory, including API references, development guides, and user manuals.

## Contributing

Contributions are welcome! Please read the [Coding Standards](docs/dev_guide/coding_standards.md) and follow the pull request process.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
"

create_file "LICENSE" "MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

[Full MIT License Text should be inserted here.]"

create_file ".gitignore" "# Python
__pycache__/
*.pyc
*.pyo
*.pyd
env/
venv/
ENV/
env.bak/
venv.bak/

# Go
/bin/
pkg/
*.test

# Rust
/target/
Cargo.lock

# C++
/build/
*.o
*.obj
*.exe
*.out

# Docker
*.log
.docker/

# Kubernetes
*.yaml

# OS Files
.DS_Store
Thumbs.db

# IDEs
.idea/
*.sublime-project
*.sublime-workspace

# Test artifacts
*.cover
coverage.xml

# Logs
*.log
logs/

# Virtual Environment
.venv/
"

create_file "Makefile" ".PHONY: all build test deploy clean

all: build test deploy

build:
	bash scripts/build.sh

test:
	bash scripts/test_runner.sh

deploy:
	bash scripts/deploy.sh

clean:
	rm -rf build
	docker system prune -f
"

create_file "gen.sh" "#!/bin/bash
# gen.sh (Main Setup and Deployment Script)

set -euo pipefail
IFS=$'\n\t'

# Define the project root, defaulting to the current directory if not provided
PROJECT_ROOT=\"\${1:-\$(pwd)}\"

echo \"Starting AI Platform setup and deployment...\"

# Execute the setup and build scripts
bash \"\$PROJECT_ROOT/scripts/setup_env.sh\"
bash \"\$PROJECT_ROOT/scripts/install_dependencies.sh\"
bash \"\$PROJECT_ROOT/scripts/test_runner.sh\"
bash \"\$PROJECT_ROOT/scripts/deploy.sh\"

echo \"AI Platform setup and deployment completed successfully.\"
"

chmod +x gen.sh

# 2. Source Code Directory
create_dir "src"

# 2.1 AI Chatbot Module
create_dir "src/ai_chatbot"

create_file "src/ai_chatbot/chatbot.py" "from flask import Flask, request, jsonify
import logging
import logging.config

app = Flask(__name__)

# Configure logging
logging.config.fileConfig('../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('ai_chatbot.chatbot')

@app.route('/chat', methods=['POST'])
def chat():
    data = request.get_json()
    message = data.get('message', '')
    logger.info(f\"Received message: {message}\")
    response = generate_response(message)
    logger.info(f\"Sending response: {response}\")
    return jsonify({'response': response})

@app.route('/healthz', methods=['GET'])
def healthz():
    return \"OK\", 200

@app.route('/readyz', methods=['GET'])
def readyz():
    # Implement readiness logic here
    return \"Ready\", 200

def generate_response(message):
    # Placeholder for GPT-4 integration
    return f\"Echo: {message}\"

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000)
"

create_file "src/ai_chatbot/requirements.txt" "Flask==2.3.2
requests==2.31.0
"

# 2.2 Predictive Analytics Module
create_dir "src/predictive_analytics/data_processing"

create_file "src/predictive_analytics/data_processing/data_processor.py" "import pandas as pd
import numpy as np

class DataProcessor:
    def __init__(self):
        pass

    def clean_data(self, df: pd.DataFrame) -> pd.DataFrame:
        # Implement data cleaning logic
        df = df.dropna()
        return df
"

create_file "src/predictive_analytics/data_processing/requirements.txt" "pandas==1.5.3
numpy==1.23.5
"

create_dir "src/predictive_analytics/risk_assessment"

create_file "src/predictive_analytics/risk_assessment/catboost_risk_model.py" "from catboost import CatBoostClassifier
import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('predictive_analytics.risk_assessment')

class RiskAssessmentModel:
    def __init__(self):
        self.model = CatBoostClassifier()

    def train(self, X, y):
        logger.info(\"Training CatBoost Risk Assessment Model...\")
        self.model.fit(X, y)
        logger.info(\"Model training completed.\")
    
    def predict(self, X):
        logger.info(\"Predicting risk scores...\")
        return self.model.predict_proba(X)[:, 1].tolist()
"

create_dir "src/predictive_analytics/customer_behavior_prediction"

create_file "src/predictive_analytics/customer_behavior_prediction/xgboost_behavior_model.py" "import xgboost as xgb
import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('predictive_analytics.customer_behavior_prediction')

class BehaviorPredictionModel:
    def __init__(self):
        self.model = xgb.XGBClassifier()

    def train(self, X, y):
        logger.info(\"Training XGBoost Customer Behavior Prediction Model...\")
        self.model.fit(X, y)
        logger.info(\"Model training completed.\")
    
    def predict(self, X):
        logger.info(\"Predicting customer behavior...\")
        return self.model.predict(X).tolist()
"

create_dir "src/predictive_analytics/time_series_forecasting"

create_file "src/predictive_analytics/time_series_forecasting/prophet_forecasting_model.py" "from fbprophet import Prophet
import pandas as pd
import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('predictive_analytics.time_series_forecasting')

class TimeSeriesForecastingModel:
    def __init__(self):
        self.model = Prophet()

    def train(self, df: pd.DataFrame):
        logger.info(\"Training Prophet Time Series Forecasting Model...\")
        self.model.fit(df)
        logger.info(\"Model training completed.\")
    
    def predict(self, periods: int):
        future = self.model.make_future_dataframe(periods=periods)
        forecast = self.model.predict(future)
        return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(periods).to_dict(orient='records')
"

# 2.3 Personalization Engine Module
create_dir "src/personalization_engine/real_time_personalization/go_service"

create_file "src/personalization_engine/real_time_personalization/go_service/main.go" "package main

import (
	"fmt"
	"log"
	"net/http"
)

func recommendHandler(w http.ResponseWriter, r *http.Request) {
	// Placeholder for recommendation logic
	fmt.Fprintf(w, \"Recommendations: [301, 302, 303]\")
}

func main() {
	http.HandleFunc(\"/recommend\", recommendHandler)
	log.Println(\"Go Recommender Service is running on port 8001...\")
	log.Fatal(http.ListenAndServe(\":8001\", nil))
}
"

create_file "src/personalization_engine/real_time_personalization/go_service/go.mod" "module go_recommender_service

go 1.20
"

create_file "src/personalization_engine/real_time_personalization/go_service/go.sum" "" # Assuming no dependencies

create_dir "src/personalization_engine/real_time_personalization"

create_file "src/personalization_engine/real_time_personalization/real_time_recommender.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('personalization_engine.real_time_personalization')

class RealTimeRecommender:
    def __init__(self):
        pass

    def get_recommendations(self, user_id: int, item_id: int) -> list:
        logger.info(f\"Generating recommendations for User {user_id} and Item {item_id}\")
        # Placeholder for recommendation logic
        return [301, 302, 303]
"

create_dir "src/personalization_engine/recommender"

create_file "src/personalization_engine/recommender/hybrid_recommender.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('personalization_engine.recommender.hybrid_recommender')

class HybridRecommender:
    def __init__(self):
        pass

    def recommend(self, user_id: int, item_id: int) -> list:
        logger.info(f\"Hybrid recommending for User {user_id} and Item {item_id}\")
        # Placeholder for hybrid recommendation logic
        return [301, 302, 303]
"

create_file "src/personalization_engine/recommender/content_based_filtering.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('personalization_engine.recommender.content_based_filtering')

class ContentBasedFiltering:
    def __init__(self):
        pass

    def recommend(self, user_id: int, item_id: int) -> list:
        logger.info(f\"Content-Based recommending for User {user_id} and Item {item_id}\")
        # Placeholder for content-based filtering logic
        return [301, 302, 303]
"

create_file "src/personalization_engine/recommender/collaborative_filtering.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('personalization_engine.recommender.collaborative_filtering')

class CollaborativeFiltering:
    def __init__(self):
        pass

    def recommend(self, user_id: int, item_id: int) -> list:
        logger.info(f\"Collaborative Filtering recommending for User {user_id} and Item {item_id}\")
        # Placeholder for collaborative filtering logic
        return [301, 302, 303]
"

create_dir "src/personalization_engine/model"

create_file "src/personalization_engine/model/recommendation_model.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('personalization_engine.model.recommendation_model')

class RecommendationModel:
    def __init__(self):
        pass

    def train(self, data):
        logger.info(\"Training recommendation model...\")
        # Placeholder for training logic
    
    def predict(self, user_id, item_id):
        logger.info(f\"Predicting recommendations for User {user_id} and Item {item_id}\")
        # Placeholder for prediction logic
        return [301, 302, 303]
"

# 2.4 Cybersecurity AI Module
create_dir "src/cybersecurity_ai/threat_intelligence"

create_file "src/cybersecurity_ai/threat_intelligence/gnn_threat_analyzer.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('cybersecurity_ai.threat_intelligence')

class GNNThreatAnalyzer:
    def __init__(self):
        pass

    def analyze(self, data):
        logger.info(\"Analyzing threats using GNN...\")
        # Placeholder for GNN threat analysis
        return ['Threat1', 'Threat2']
"

create_dir "src/cybersecurity_ai/incident_response"

create_file "src/cybersecurity_ai/incident_response/reinforcement_responder.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('cybersecurity_ai.incident_response')

class ReinforcementResponder:
    def __init__(self):
        pass

    def respond(self, threat):
        logger.info(f\"Responding to threat: {threat}\")
        # Placeholder for reinforcement learning-based response
        return f\"Responded to {threat}\"
"

create_dir "src/cybersecurity_ai/anomaly_detection"

create_file "src/cybersecurity_ai/anomaly_detection/anomaly_detector.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('cybersecurity_ai.anomaly_detection')

class AnomalyDetector:
    def __init__(self):
        pass

    def detect(self, data):
        logger.info(\"Detecting anomalies...\")
        # Placeholder for anomaly detection logic
        return ['Anomaly Detected']
"

create_dir "src/cybersecurity_ai/anomaly_detection/rust_service/src"

create_file "src/cybersecurity_ai/anomaly_detection/rust_service/src/lib.rs" "// lib.rs (Rust Anomaly Detector Library)
pub fn detect_anomaly(data: &str) -> Vec<String> {
    vec![\"Anomaly Detected\".to_string()]
}
"

create_dir "src/cybersecurity_ai/anomaly_detection/rust_service/tests"

create_file "src/cybersecurity_ai/anomaly_detection/rust_service/tests/lib_test.rs" "// lib_test.rs (Rust Anomaly Detector Tests)
use crate::detect_anomaly;

#[test]
fn test_detect_anomaly() {
    let result = detect_anomaly(\"Test data\");
    assert_eq!(result, vec![\"Anomaly Detected\".to_string()]);
}
"

create_dir "src/cpp_module/include"

create_file "src/cpp_module/include/cpp_anomaly_detector.h" "// cpp_anomaly_detector.h (C++ Anomaly Detector Header)
#ifndef CPP_ANOMALY_DETECTOR_H
#define CPP_ANOMALY_DETECTOR_H

#include <string>
#include <vector>

class CppAnomalyDetector {
public:
    CppAnomalyDetector();
    std::vector<std::string> detect(const std::string& data);
};

#endif // CPP_ANOMALY_DETECTOR_H
"

create_dir "src/cpp_module/src"

create_file "src/cpp_module/src/cpp_anomaly_detector.cpp" "// cpp_anomaly_detector.cpp (C++ Anomaly Detector Implementation)
#include \"cpp_anomaly_detector.h\"

CppAnomalyDetector::CppAnomalyDetector() {}

std::vector<std::string> CppAnomalyDetector::detect(const std::string& data) {
    // Placeholder for C++ anomaly detection logic
    return {\"Anomaly Detected\"};
}
"

create_dir "src/cpp_module/tests"

create_file "src/cpp_module/tests/test_cpp_anomaly_detector.cpp" "// test_cpp_anomaly_detector.cpp (C++ Anomaly Detector Unit Tests)
#define CATCH_CONFIG_MAIN
#include \"catch.hpp\"
#include \"cpp_anomaly_detector.h\"

TEST_CASE(\"CppAnomalyDetector detects anomalies\", \"[AnomalyDetector]\") {
    CppAnomalyDetector detector;
    std::vector<std::string> result = detector.detect(\"Test data\");
    REQUIRE(result.size() == 1);
    REQUIRE(result[0] == \"Anomaly Detected\");
}
"

create_file "src/cpp_module/tests/catch.hpp" "// catch.hpp (Catch2 Single Header)
#define CATCH_CONFIG_MAIN
#include <catch2/catch.hpp>
"

create_file "src/cpp_module/CMakeLists.txt" "cmake_minimum_required(VERSION 3.10)
project(CppAnomalyDetector)

set(CMAKE_CXX_STANDARD 17)

add_library(cpp_anomaly_detector STATIC src/cpp_anomaly_detector.cpp)

# Add include directories
target_include_directories(cpp_anomaly_detector PUBLIC include)

# Add executable for testing
add_executable(test_cpp_anomaly_detector tests/test_cpp_anomaly_detector.cpp)

# Link libraries
target_link_libraries(test_cpp_anomaly_detector cpp_anomaly_detector)
"

create_file "src/cpp_module/tests/CMakeLists.txt" "cmake_minimum_required(VERSION 3.10)
project(CppAnomalyDetectorTests)

# Add executable for tests
add_executable(test_cpp_anomaly_detector tests/test_cpp_anomaly_detector.cpp)

# Link libraries
target_link_libraries(test_cpp_anomaly_detector cpp_anomaly_detector)
"

# 2.5 Content Creation AI Module
create_dir "src/content_creation_ai/text_generation"

create_file "src/content_creation_ai/text_generation/gpt4_text_generator.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('content_creation_ai.text_generation')

class GPT4TextGenerator:
    def __init__(self):
        pass

    def generate_text(self, prompt: str) -> str:
        logger.info(f\"Generating text for prompt: {prompt}\")
        # Placeholder for GPT-4 integration
        return f\"Generated text based on: {prompt}\"
"

create_dir "src/content_creation_ai/multimedia_generation"

create_file "src/content_creation_ai/multimedia_generation/advanced_audio_generator.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('content_creation_ai.multimedia_generation')

class AdvancedAudioGenerator:
    def __init__(self):
        pass

    def generate_audio(self, parameters: dict) -> str:
        logger.info(f\"Generating audio with parameters: {parameters}\")
        # Placeholder for advanced audio generation logic
        return \"Generated audio file path\"
"

create_file "src/content_creation_ai/multimedia_generation/advanced_video_generator.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('content_creation_ai.multimedia_generation')

class AdvancedVideoGenerator:
    def __init__(self):
        pass

    def generate_video(self, parameters: dict) -> str:
        logger.info(f\"Generating video with parameters: {parameters}\")
        # Placeholder for advanced video generation logic
        return \"Generated video file path\"
"

create_dir "src/content_creation_ai/image_generation"

create_file "src/content_creation_ai/image_generation/dalle3_image_generator.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('content_creation_ai.image_generation')

class Dalle3ImageGenerator:
    def __init__(self):
        pass

    def generate_image(self, prompt: str) -> str:
        logger.info(f\"Generating image for prompt: {prompt}\")
        # Placeholder for DALL·E 3 integration
        return f\"Generated image based on: {prompt}\"
"

create_file "src/content_creation_ai/image_generation/stylegan3_image_generator.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('content_creation_ai.image_generation')

class StyleGAN3ImageGenerator:
    def __init__(self):
        pass

    def generate_image(self, parameters: dict) -> str:
        logger.info(f\"Generating image with parameters: {parameters}\")
        # Placeholder for StyleGAN3 integration
        return \"Generated StyleGAN3 image path\"
"

# 2.6 Healthcare AI Module
create_dir "src/healthcare_ai/drug_discovery"

create_file "src/healthcare_ai/drug_discovery/drug_discovery_ai.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('healthcare_ai.drug_discovery')

class DrugDiscoveryAI:
    def __init__(self):
        pass

    def discover_drugs(self, target: str) -> list:
        logger.info(f\"Discovering drugs for target: {target}\")
        # Placeholder for drug discovery logic
        return ['DrugA', 'DrugB']
"

create_dir "src/healthcare_ai/diagnostics"

create_file "src/healthcare_ai/diagnostics/diagnostics_ai.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('healthcare_ai.diagnostics')

class DiagnosticsAI:
    def __init__(self):
        pass

    def diagnose(self, image_data: bytes) -> str:
        logger.info(\"Diagnosing medical images...\")
        # Placeholder for medical image diagnostics logic
        return \"Diagnosis Result\"
"

# 2.7 AI Consulting Module
create_dir "src/ai_consulting/strategy_development"

create_file "src/ai_consulting/strategy_development/strategy_developer.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('ai_consulting.strategy_development')

class StrategyDeveloper:
    def __init__(self):
        pass

    def develop_strategy(self, data: dict) -> dict:
        logger.info(f\"Developing strategy with data: {data}\")
        # Placeholder for strategy development logic
        return {\"strategy\": \"Optimized Strategy\"}
"

# 2.8 VR/AR AI Module
create_dir "src/vr_ar_ai/object_recognition"

create_file "src/vr_ar_ai/object_recognition/real_time_tracking.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('vr_ar_ai.object_recognition.real_time_tracking')

class RealTimeTracking:
    def __init__(self):
        pass

    def track_object(self, object_id: int) -> dict:
        logger.info(f\"Tracking object ID: {object_id}\")
        # Placeholder for real-time tracking logic
        return {\"object_id\": object_id, \"status\": \"Tracking\"}
"

create_file "src/vr_ar_ai/object_recognition/object_detector.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('vr_ar_ai.object_recognition.object_detector')

class ObjectDetector:
    def __init__(self):
        pass

    def detect_objects(self, image_data: bytes) -> list:
        logger.info(\"Detecting objects in image data...\")
        # Placeholder for object detection logic
        return ['Object1', 'Object2']
"

create_dir "src/vr_ar_ai/personalized_experience"

create_file "src/vr_ar_ai/personalized_experience/adaptive_content.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('vr_ar_ai.personalized_experience')

class AdaptiveContent:
    def __init__(self):
        pass

    def adapt_content(self, user_preferences: dict) -> dict:
        logger.info(f\"Adapting content based on preferences: {user_preferences}\")
        # Placeholder for adaptive content logic
        return {\"content\": \"Personalized Content\"}
"

create_dir "src/vr_ar_ai/dynamic_content_generation"

create_file "src/vr_ar_ai/dynamic_content_generation/gan_content_generator.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('vr_ar_ai.dynamic_content_generation.gan_content_generator')

class GANContentGenerator:
    def __init__(self):
        pass

    def generate_content(self, parameters: dict) -> str:
        logger.info(f\"Generating content with GAN parameters: {parameters}\")
        # Placeholder for GAN-based content generation logic
        return \"Generated GAN Content\"
"

# 2.9 Supply Chain AI Module
create_dir "src/supply_chain_ai/logistics_optimization"

create_file "src/supply_chain_ai/logistics_optimization/supplier_risk_assessment.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('supply_chain_ai.logistics_optimization')

class SupplierRiskAssessment:
    def __init__(self):
        pass

    def assess_risk(self, supplier_id: int) -> float:
        logger.info(f\"Assessing risk for Supplier ID: {supplier_id}\")
        # Placeholder for risk assessment logic
        return 0.75
"

create_dir "src/supply_chain_ai/inventory_optimization"

create_file "src/supply_chain_ai/inventory_optimization/dynamic_pricing_model.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('supply_chain_ai.inventory_optimization')

class DynamicPricingModel:
    def __init__(self):
        pass

    def set_price(self, product_id: int, price: float) -> float:
        logger.info(f\"Setting dynamic price for Product ID: {product_id} to {price}\")
        # Placeholder for dynamic pricing logic
        return price
"

create_file "src/supply_chain_ai/inventory_optimization/erp_integration.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('supply_chain_ai.inventory_optimization')

class ERPIntegration:
    def __init__(self):
        pass

    def integrate(self, data: dict) -> bool:
        logger.info(f\"Integrating data with ERP system: {data}\")
        # Placeholder for ERP integration logic
        return True
"

create_file "src/supply_chain_ai/inventory_optimization/iot_data_integration.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('supply_chain_ai.inventory_optimization')

class IoTDataIntegration:
    def __init__(self):
        pass

    def integrate_data(self, sensor_data: dict) -> bool:
        logger.info(f\"Integrating IoT sensor data: {sensor_data}\")
        # Placeholder for IoT data integration logic
        return True
"

create_dir "src/supply_chain_ai/demand_prediction"

create_file "src/supply_chain_ai/demand_prediction/demand_predictor.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('supply_chain_ai.demand_prediction')

class DemandPredictor:
    def __init__(self):
        pass

    def predict_demand(self, product_id: int) -> int:
        logger.info(f\"Predicting demand for Product ID: {product_id}\")
        # Placeholder for demand prediction logic
        return 100
"

create_dir "src/supply_chain_ai/inventory_management"

create_file "src/supply_chain_ai/inventory_management/inventory_optimizer.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('supply_chain_ai.inventory_management')

class InventoryOptimizer:
    def __init__(self):
        pass

    def optimize_inventory(self, product_id: int, stock_level: int) -> int:
        logger.info(f\"Optimizing inventory for Product ID: {product_id} with stock level: {stock_level}\")
        # Placeholder for inventory optimization logic
        return stock_level + 50
"

# 2.10 AutoML Module
create_dir "src/automl"

create_file "src/automl/hyperparameter_tuning.py" "import optuna
import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('automl.hyperparameter_tuning')

def objective(trial):
    # Placeholder for hyperparameter tuning logic
    x = trial.suggest_float('x', -10, 10)
    return (x - 2) ** 2

def tune_hyperparameters():
    logger.info(\"Starting hyperparameter tuning...\")
    study = optuna.create_study(direction='minimize')
    study.optimize(objective, n_trials=100)
    logger.info(f\"Best hyperparameters: {study.best_params}\")
    logger.info(f\"Best objective value: {study.best_value}\")
    return study.best_params, study.best_value

if __name__ == '__main__':
    tune_hyperparameters()
"

create_file "src/automl/model_selection.py" "import logging
import logging.config

# Configure logging
logging.config.fileConfig('../../monitoring/logging/log_config.yaml', disable_existing_loggers=False)
logger = logging.getLogger('automl.model_selection')

class ModelSelection:
    def __init__(self):
        pass

    def select_best_model(self, models: list, metrics: dict) -> str:
        logger.info(f\"Selecting best model based on metrics: {metrics}\")
        # Placeholder for model selection logic
        return models[0]
"

# 2.11 Common Utilities
create_dir "src/common/utils"

create_file "src/common/utils/logging.py" "import logging
import logging.config

def setup_logging(default_path='../../monitoring/logging/log_config.yaml', default_level=logging.INFO):
    logging.config.fileConfig(default_path, disable_existing_loggers=False)
"

# 3. Deployment Directory
create_dir "deployment"

# 3.1 Dockerfiles
create_dir "deployment/docker"

create_file "deployment/docker/ai_chatbot_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/ai_chatbot/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/ai_chatbot/ .

EXPOSE 8000

CMD [\"python\", \"chatbot.py\"]
"

create_file "deployment/docker/cpp_module_Dockerfile" "FROM ubuntu:22.04

WORKDIR /app

# Install dependencies
RUN apt-get update && \
    apt-get install -y build-essential cmake

# Copy source code
COPY src/cpp_module/ .

# Build the C++ application
RUN mkdir -p build && \
    cd build && \
    cmake .. && \
    make

CMD [\"./build/cpp_anomaly_detector\"]
"

create_file "deployment/docker/rust_anomaly_detector_Dockerfile" "FROM rust:1.70

WORKDIR /app

# Copy source code
COPY src/cybersecurity_ai/anomaly_detection/rust_service/ .

# Build the Rust application
RUN cargo build --release

CMD [\"./target/release/anomaly_detector\"]
"

create_file "deployment/docker/go_recommender_service_Dockerfile" "FROM golang:1.20-alpine

WORKDIR /app

# Copy go.mod and download dependencies
COPY src/personalization_engine/real_time_personalization/go_service/go.mod .
COPY src/personalization_engine/real_time_personalization/go_service/go.sum .
RUN go mod download

# Copy source code
COPY src/personalization_engine/real_time_personalization/go_service/ .

# Build the Go application
RUN go build -o go_recommender_service

EXPOSE 8001

CMD [\"./go_recommender_service\"]
"

create_file "deployment/docker/predictive_analytics_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/predictive_analytics/data_processing/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/predictive_analytics/ .

EXPOSE 8002

CMD [\"python\", \"risk_assessment/catboost_risk_model.py\"]
"

create_file "deployment/docker/personalization_engine_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/personalization_engine/recommender/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/personalization_engine/recommender/ .

EXPOSE 8003

CMD [\"python\", \"hybrid_recommender.py\"]
"

create_file "deployment/docker/content_creation_ai_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/content_creation_ai/text_generation/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/content_creation_ai/ .

EXPOSE 8004

CMD [\"python\", \"text_generation/gpt4_text_generator.py\"]
"

create_file "deployment/docker/healthcare_ai_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/healthcare_ai/drug_discovery/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/healthcare_ai/ .

EXPOSE 8005

CMD [\"python\", \"drug_discovery/drug_discovery_ai.py\"]
"

create_file "deployment/docker/ai_consulting_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/ai_consulting/strategy_development/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/ai_consulting/strategy_development/ .

EXPOSE 8006

CMD [\"python\", \"strategy_developer.py\"]
"

create_file "deployment/docker/vr_ar_ai_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/vr_ar_ai/object_recognition/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/vr_ar_ai/ .

EXPOSE 8007

CMD [\"python\", \"object_recognition/object_detector.py\"]
"

create_file "deployment/docker/supply_chain_ai_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/supply_chain_ai/logistics_optimization/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/supply_chain_ai/ .

EXPOSE 8008

CMD [\"python\", \"logistics_optimization/supplier_risk_assessment.py\"]
"

create_file "deployment/docker/automl_Dockerfile" "FROM python:3.12-slim

WORKDIR /app

# Install Python dependencies
COPY src/automl/requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt

# Copy source code
COPY src/automl/ .

EXPOSE 8009

CMD [\"python\", \"hyperparameter_tuning.py\"]
"

# 3.2 Kubernetes Deployment Manifests
create_dir "deployment/k8s"

# Example Deployment Manifest for AI Chatbot
create_file "deployment/k8s/ai_chatbot_deployment.yaml" "apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-chatbot
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-chatbot
  template:
    metadata:
      labels:
        app: ai-chatbot
    spec:
      containers:
      - name: ai-chatbot
        image: your-docker-registry/ai-chatbot:latest
        ports:
        - containerPort: 8000
        resources:
          requests:
            memory: \"512Mi\"
            cpu: \"500m\"
          limits:
            memory: \"1Gi\"
            cpu: \"1\"
        livenessProbe:
          httpGet:
            path: /healthz
            port: 8000
          initialDelaySeconds: 15
          periodSeconds: 20
        readinessProbe:
          httpGet:
            path: /readyz
            port: 8000
          initialDelaySeconds: 15
          periodSeconds: 20
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: openai-secrets
              key: api_key
        volumeMounts:
        - name: data-volume
          mountPath: /app/data
      volumes:
      - name: data-volume
        persistentVolumeClaim:
          claimName: ai-chatbot-data
---
apiVersion: v1
kind: Service
metadata:
  name: ai-chatbot-service
spec:
  selector:
    app: ai-chatbot
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8000
  type: ClusterIP
"
# [Continue with creating other Kubernetes deployment manifests similarly]

# 4. CI/CD Directory
create_dir "deployment/ci_cd/build_scripts"

create_file "deployment/ci_cd/build_scripts/install_dependencies.sh" "#!/bin/bash
# install_dependencies.sh (Dependency Installation and Build Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Installing Python dependencies...\"
pip install --upgrade pip
pip install -r src/predictive_analytics/data_processing/requirements.txt
echo \"Python dependencies installed.\"

echo \"Installing Go dependencies...\"
cd src/personalization_engine/real_time_personalization/go_service
go mod tidy
echo \"Go dependencies installed.\"

echo \"Installing Rust dependencies and building...\"
cd ../../../../cybersecurity_ai/anomaly_detection/rust_service
cargo build --release
echo \"Rust dependencies installed and project built.\"

echo \"Installing C++ dependencies and building...\"
cd ../../../../../cpp_module
mkdir -p build
cd build
cmake ..
make
echo \"C++ dependencies installed and project built.\"

echo \"All dependencies installed and projects built successfully.\"
"

chmod +x "deployment/ci_cd/build_scripts/install_dependencies.sh"

create_file "deployment/ci_cd/build_scripts/setup_env.sh" "#!/bin/bash
# setup_env.sh (Environment Setup Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Setting up environment variables...\"

# Load production environment variables from prod.yaml
export DATABASE_URL=$(grep 'url:' configs/environments/prod.yaml | awk '{print \$2}')
export OPENAI_API_KEY=$(grep 'api_key:' configs/environments/prod.yaml | awk '{print \$2}')
export KAFKA_BROKER=$(grep 'broker:' configs/environments/prod.yaml | awk '{print \$2}')

echo \"Environment variables set successfully.\"
"

chmod +x "deployment/ci_cd/build_scripts/setup_env.sh"

create_file "deployment/ci_cd/build_scripts/build.sh" "#!/bin/bash
# build.sh (Build Orchestration Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Starting the build process...\"

# Source environment variables
bash scripts/setup_env.sh

# Install dependencies and build projects
bash scripts/install_dependencies.sh

echo \"Build process completed successfully.\"
"

chmod +x "deployment/ci_cd/build_scripts/build.sh"

create_file "deployment/ci_cd/build_scripts/test_runner.sh" "#!/bin/bash
# test_runner.sh (Test Runner Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Running unit tests...\"

# Run C++ Anomaly Detector Unit Test
bash tests/unit/test_cpp_anomaly_detector.sh

# Run Rust Anomaly Detector Unit Test
bash tests/unit/test_rust_anomaly_detector.sh

# Run Python Unit Tests
python3 -m unittest discover -s tests/unit

echo \"Running integration tests...\"
python3 -m unittest discover -s tests/integration

echo \"Running end-to-end tests...\"
python3 -m unittest discover -s tests/e2e

echo \"All tests executed successfully.\"
"

chmod +x "deployment/ci_cd/build_scripts/test_runner.sh"

create_file "deployment/ci_cd/build_scripts/deploy.sh" "#!/bin/bash
# deploy.sh (General Deployment Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Starting Deployment of AI Platform...\"

bash scripts/deploy_personalization.sh
bash scripts/deploy_rust_anomaly_detector.sh
bash scripts/deploy_predictive_analytics.sh
bash scripts/deploy_cpp_anomaly_detector.sh
bash scripts/deploy_content_creation_ai.sh
bash scripts/deploy_healthcare_ai.sh
bash scripts/deploy_ai_consulting.sh
bash scripts/deploy_vr_ar_ai.sh
bash scripts/deploy_supply_chain_ai.sh
bash scripts/deploy_automl.sh

echo \"All Deployments Completed Successfully.\"
"

chmod +x "deployment/ci_cd/build_scripts/deploy.sh"

# 5. Scripts Directory
create_dir "scripts"

create_file "scripts/install_dependencies.sh" "#!/bin/bash
# install_dependencies.sh (Dependency Installation and Build Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Installing Python dependencies...\"
pip install --upgrade pip
pip install -r src/predictive_analytics/data_processing/requirements.txt
echo \"Python dependencies installed.\"

echo \"Installing Go dependencies...\"
cd src/personalization_engine/real_time_personalization/go_service
go mod tidy
echo \"Go dependencies installed.\"

echo \"Installing Rust dependencies and building...\"
cd ../../../../cybersecurity_ai/anomaly_detection/rust_service
cargo build --release
echo \"Rust dependencies installed and project built.\"

echo \"Installing C++ dependencies and building...\"
cd ../../../../../cpp_module
mkdir -p build
cd build
cmake ..
make
echo \"C++ dependencies installed and project built.\"

echo \"All dependencies installed and projects built successfully.\"
"

chmod +x "scripts/install_dependencies.sh"

create_file "scripts/setup_env.sh" "#!/bin/bash
# setup_env.sh (Environment Setup Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Setting up environment variables...\"

# Load production environment variables from prod.yaml
export DATABASE_URL=$(grep 'url:' configs/environments/prod.yaml | awk '{print \$2}')
export OPENAI_API_KEY=$(grep 'api_key:' configs/environments/prod.yaml | awk '{print \$2}')
export KAFKA_BROKER=$(grep 'broker:' configs/environments/prod.yaml | awk '{print \$2}')

echo \"Environment variables set successfully.\"
"

chmod +x "scripts/setup_env.sh"

create_file "scripts/build.sh" "#!/bin/bash
# build.sh (Build Orchestration Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Starting the build process...\"

# Source environment variables
bash scripts/setup_env.sh

# Install dependencies and build projects
bash scripts/install_dependencies.sh

echo \"Build process completed successfully.\"
"

chmod +x "scripts/build.sh"

create_file "scripts/test_runner.sh" "#!/bin/bash
# test_runner.sh (Test Runner Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Running unit tests...\"

# Run C++ Anomaly Detector Unit Test
bash tests/unit/test_cpp_anomaly_detector.sh

# Run Rust Anomaly Detector Unit Test
bash tests/unit/test_rust_anomaly_detector.sh

# Run Python Unit Tests
python3 -m unittest discover -s tests/unit

echo \"Running integration tests...\"
python3 -m unittest discover -s tests/integration

echo \"Running end-to-end tests...\"
python3 -m unittest discover -s tests/e2e

echo \"All tests executed successfully.\"
"

chmod +x "scripts/test_runner.sh"

create_file "scripts/deploy.sh" "#!/bin/bash
# deploy.sh (General Deployment Script)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Starting Deployment of AI Platform...\"

bash scripts/deploy_personalization.sh
bash scripts/deploy_rust_anomaly_detector.sh
bash scripts/deploy_predictive_analytics.sh
bash scripts/deploy_cpp_anomaly_detector.sh
bash scripts/deploy_content_creation_ai.sh
bash scripts/deploy_healthcare_ai.sh
bash scripts/deploy_ai_consulting.sh
bash scripts/deploy_vr_ar_ai.sh
bash scripts/deploy_supply_chain_ai.sh
bash scripts/deploy_automl.sh

echo \"All Deployments Completed Successfully.\"
"

chmod +x "scripts/deploy.sh"

# 5.1 Individual Deployment Scripts
create_file "scripts/deploy_personalization.sh" "#!/bin/bash
# deploy_personalization.sh (Deployment Script for Personalization Engine)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying Personalization Engine...\"

kubectl apply -f deployment/k8s/personalization_engine_deployment.yaml

echo \"Personalization Engine Deployment Complete.\"
"

chmod +x "scripts/deploy_personalization.sh"

create_file "scripts/deploy_rust_anomaly_detector.sh" "#!/bin/bash
# deploy_rust_anomaly_detector.sh (Deployment Script for Rust Anomaly Detector)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying Rust Anomaly Detector...\"

kubectl apply -f deployment/k8s/rust_anomaly_detector_deployment.yaml

echo \"Rust Anomaly Detector Deployment Complete.\"
"

chmod +x "scripts/deploy_rust_anomaly_detector.sh"

create_file "scripts/deploy_predictive_analytics.sh" "#!/bin/bash
# deploy_predictive_analytics.sh (Deployment Script for Predictive Analytics)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying Predictive Analytics...\"

kubectl apply -f deployment/k8s/predictive_analytics_deployment.yaml

echo \"Predictive Analytics Deployment Complete.\"
"

chmod +x "scripts/deploy_predictive_analytics.sh"

create_file "scripts/deploy_cpp_anomaly_detector.sh" "#!/bin/bash
# deploy_cpp_anomaly_detector.sh (Deployment Script for C++ Anomaly Detector)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying C++ Anomaly Detector...\"

kubectl apply -f deployment/k8s/cpp_module_deployment.yaml

echo \"C++ Anomaly Detector Deployment Complete.\"
"

chmod +x "scripts/deploy_cpp_anomaly_detector.sh"

create_file "scripts/deploy_content_creation_ai.sh" "#!/bin/bash
# deploy_content_creation_ai.sh (Deployment Script for Content Creation AI)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying Content Creation AI...\"

kubectl apply -f deployment/k8s/content_creation_ai_deployment.yaml

echo \"Content Creation AI Deployment Complete.\"
"

chmod +x "scripts/deploy_content_creation_ai.sh"

create_file "scripts/deploy_healthcare_ai.sh" "#!/bin/bash
# deploy_healthcare_ai.sh (Deployment Script for Healthcare AI)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying Healthcare AI...\"

kubectl apply -f deployment/k8s/healthcare_ai_deployment.yaml

echo \"Healthcare AI Deployment Complete.\"
"

chmod +x "scripts/deploy_healthcare_ai.sh"

create_file "scripts/deploy_ai_consulting.sh" "#!/bin/bash
# deploy_ai_consulting.sh (Deployment Script for AI Consulting)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying AI Consulting...\"

kubectl apply -f deployment/k8s/ai_consulting_deployment.yaml

echo \"AI Consulting Deployment Complete.\"
"

chmod +x "scripts/deploy_ai_consulting.sh"

create_file "scripts/deploy_vr_ar_ai.sh" "#!/bin/bash
# deploy_vr_ar_ai.sh (Deployment Script for VR/AR AI)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying VR/AR AI...\"

kubectl apply -f deployment/k8s/vr_ar_ai_deployment.yaml

echo \"VR/AR AI Deployment Complete.\"
"

chmod +x "scripts/deploy_vr_ar_ai.sh"

create_file "scripts/deploy_supply_chain_ai.sh" "#!/bin/bash
# deploy_supply_chain_ai.sh (Deployment Script for Supply Chain AI)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying Supply Chain AI...\"

kubectl apply -f deployment/k8s/supply_chain_ai_deployment.yaml

echo \"Supply Chain AI Deployment Complete.\"
"

chmod +x "scripts/deploy_supply_chain_ai.sh"

create_file "scripts/deploy_automl.sh" "#!/bin/bash
# deploy_automl.sh (Deployment Script for AutoML)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Deploying AutoML...\"

kubectl apply -f deployment/k8s/automl_deployment.yaml

echo \"AutoML Deployment Complete.\"
"

chmod +x "scripts/deploy_automl.sh"

# 6. Monitoring Directory
create_dir "monitoring/logging"

create_file "monitoring/logging/log_config.yaml" "# log_config.yaml (Logging Configuration)

version: 1
disable_existing_loggers: False

formatters:
  standard:
    format: \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"

handlers:
  console:
    class: logging.StreamHandler
    formatter: standard
    level: INFO
  file:
    class: logging.FileHandler
    formatter: standard
    filename: ai_platform.log
    level: DEBUG

loggers:
  ai_chatbot.chatbot:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  cpp_module.cpp_anomaly_detector:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  rust_anomaly_detector:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  go_recommender_service:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  predictive_analytics.risk_assessment:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  predictive_analytics.customer_behavior_prediction:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  predictive_analytics.time_series_forecasting:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  personalization_engine.recommender.hybrid_recommender:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  cybersecurity_ai.threat_intelligence:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  cybersecurity_ai.incident_response:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  cybersecurity_ai.anomaly_detection:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  content_creation_ai.text_generation:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  content_creation_ai.image_generation:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  content_creation_ai.multimedia_generation:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  healthcare_ai.drug_discovery:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  healthcare_ai.diagnostics:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  ai_consulting.strategy_development:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  vr_ar_ai.object_recognition.real_time_tracking:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  vr_ar_ai.object_recognition.object_detector:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  vr_ar_ai.personalized_experience.adaptive_content:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  vr_ar_ai.dynamic_content_generation.gan_content_generator:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  supply_chain_ai.logistics_optimization.supplier_risk_assessment:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  supply_chain_ai.inventory_optimization.dynamic_pricing_model:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  supply_chain_ai.inventory_optimization.erp_integration:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  supply_chain_ai.inventory_optimization.iot_data_integration:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  supply_chain_ai.demand_prediction.demand_predictor:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  supply_chain_ai.inventory_management.inventory_optimizer:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  automl.hyperparameter_tuning:
    handlers: [console, file]
    level: DEBUG
    propagate: False
  automl.model_selection:
    handlers: [console, file]
    level: DEBUG
    propagate: False

root:
  handlers: [console, file]
  level: INFO
"

# 7. Configuration Files Directory
create_dir "configs/environments"

create_file "configs/environments/dev.yaml" "# dev.yaml (Development Environment Configuration)

database:
  url: \"postgresql://user:password@localhost:5432/ai_db_dev\"

openai:
  api_key: \"your_development_openai_api_key\"

kafka:
  broker: \"localhost:9092\"
"

create_file "configs/environments/prod.yaml" "# prod.yaml (Production Environment Configuration)

database:
  url: \"postgresql://user:password@prod-db-host:5432/ai_db\"

openai:
  api_key: \"your_production_openai_api_key\"

kafka:
  broker: \"prod-kafka-broker:9092\"
"

# 8. Tests Directory
create_dir "tests/test_data"

create_file "tests/test_data/sample_data.py" "# sample_data.py (Sample Data for Testing)
import pandas as pd
import numpy as np

def get_sample_dataframe() -> pd.DataFrame:
    return pd.DataFrame({
        \"feature1\": [
            1,
            2,
            np.nan,
            4
        ],
        \"feature2\": [
            \"A\",
            \"B\",
            \"A\",
            \"B\"
        ]
    })
"

create_dir "tests/unit"

create_file "tests/unit/test_personalization_engine.py" "import unittest
from src.personalization_engine.recommender.hybrid_recommender import HybridRecommender

class TestHybridRecommender(unittest.TestCase):
    def setUp(self):
        self.recommender = HybridRecommender()

    def test_recommend(self):
        recommendations = self.recommender.recommend(user_id=1, item_id=101)
        self.assertEqual(recommendations, [301, 302, 303])

if __name__ == '__main__':
    unittest.main()
"

create_file "tests/unit/test_predictive_analytics.py" "import unittest
from src.predictive_analytics.risk_assessment.catboost_risk_model import RiskAssessmentModel

class TestRiskAssessmentModel(unittest.TestCase):
    def setUp(self):
        self.model = RiskAssessmentModel()

    def test_predict(self):
        predictions = self.model.predict([[10]])
        self.assertEqual(predictions, [0.75])

if __name__ == '__main__':
    unittest.main()
"

create_file "tests/unit/test_ai_chatbot.py" "import unittest
from src.ai_chatbot.chatbot import app

class TestChatbot(unittest.TestCase):
    def setUp(self):
        self.app = app.test_client()
        self.app.testing = True

    def test_chat_response(self):
        response = self.app.post('/chat', json={'message': 'Hello'})
        self.assertEqual(response.status_code, 200)
        data = response.get_json()
        self.assertIn('response', data)
        self.assertEqual(data['response'], 'Echo: Hello')

if __name__ == '__main__':
    unittest.main()
"

create_file "tests/unit/test_anomaly_detector.py" "import unittest
from src.cybersecurity_ai.anomaly_detection.anomaly_detector import AnomalyDetector

class TestAnomalyDetector(unittest.TestCase):
    def setUp(self):
        self.detector = AnomalyDetector()

    def test_detect(self):
        anomalies = self.detector.detect('Test data')
        self.assertEqual(anomalies, ['Anomaly Detected'])

if __name__ == '__main__':
    unittest.main()
"

create_file "tests/unit/test_cpp_anomaly_detector.sh" "#!/bin/bash
# test_cpp_anomaly_detector.sh (Unit Test Script for C++ Anomaly Detector)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Compiling C++ Anomaly Detector Test...\"

# Compile the C++ test
g++ -o test_cpp_anomaly_detector src/cpp_module/tests/test_cpp_anomaly_detector.cpp src/cpp_module/include/cpp_anomaly_detector.h src/cpp_module/src/cpp_anomaly_detector.cpp

echo \"Running C++ Anomaly Detector Test...\"
./test_cpp_anomaly_detector

echo \"C++ Anomaly Detector Test Passed.\"
"

chmod +x "tests/unit/test_cpp_anomaly_detector.sh"

create_file "tests/unit/test_rust_anomaly_detector.sh" "#!/bin/bash
# test_rust_anomaly_detector.sh (Unit Test Script for Rust Anomaly Detector)

set -euo pipefail
IFS=$'\\n\\t'

echo \"Running Rust Anomaly Detector Tests...\"

# Navigate to Rust project
cd src/cybersecurity_ai/anomaly_detection/rust_service

# Run tests
cargo test

echo \"Rust Anomaly Detector Tests Passed.\"
"

chmod +x "tests/unit/test_rust_anomaly_detector.sh"

create_dir "tests/integration"

create_file "tests/integration/test_database.py" "import unittest
import psycopg2
import logging

logger = logging.getLogger('tests.integration.test_database')

class TestDatabase(unittest.TestCase):
    def setUp(self):
        try:
            self.conn = psycopg2.connect(
                dbname=\"ai_db_dev\",
                user=\"user\",
                password=\"password\",
                host=\"localhost\",
                port=\"5432\"
            )
            self.cur = self.conn.cursor()
            logger.info(\"Database connection established for testing.\")
        except Exception as e:
            logger.error(f\"Error connecting to database: {type(e).__name__}: {e}\")
            raise

    def test_connection(self):
        self.cur.execute(\"SELECT 1\")
        result = self.cur.fetchone()
        self.assertEqual(result[0], 1)
        logger.info(\"Database connection test passed.\")

    def tearDown(self):
        self.cur.close()
        self.conn.close()
        logger.info(\"Database connection closed after testing.\")

if __name__ == '__main__':
    unittest.main()
"

create_file "tests/integration/test_api.py" "import unittest
import requests
import logging

logger = logging.getLogger('tests.integration.test_api')

class TestAPI(unittest.TestCase):
    AI_CHATBOT_API_URL = \"http://localhost:8000\"
    PREDICTIVE_ANALYTICS_API_URL = \"http://localhost:8002\"

    def test_ai_chatbot_endpoint(self):
        payload = {
            \"message\": \"Hello\"
        }
        try:
            response = requests.post(f\"{self.AI_CHATBOT_API_URL}/chat\", json=payload)
            self.assertEqual(response.status_code, 200)
            data = response.get_json()
            self.assertIn(\"response\", data)
            self.assertEqual(data[\"response\"], \"Echo: Hello\")
            logger.info(\"AI Chatbot endpoint test passed.\")
        except Exception as e:
            logger.error(f\"AI Chatbot endpoint test failed: {type(e).__name__}: {e}\")
            self.fail(e)

    def test_predictive_analytics_endpoint(self):
        payload = {
            \"feature1\": 1.0,
            \"feature2\": \"A\"
        }
        try:
            response = requests.post(f\"{self.PREDICTIVE_ANALYTICS_API_URL}/risk_assessment\", json=payload)
            self.assertEqual(response.status_code, 200)
            data = response.get_json()
            self.assertIn(\"risk_score\", data)
            self.assertEqual(data[\"risk_score\"], 0.75)
            logger.info(\"Predictive Analytics endpoint test passed.\")
        except Exception as e:
            logger.error(f\"Predictive Analytics endpoint test failed: {type(e).__name__}: {e}\")
            self.fail(e)

if __name__ == '__main__':
    unittest.main()
"

create_file "tests/integration/test_recommender_service.py" "import unittest
import requests
import logging

logger = logging.getLogger('tests.integration.test_recommender_service')

class TestRecommenderService(unittest.TestCase):
    RECOMMENDER_SERVICE_URL = \"http://localhost:8001/recommend\"

    def test_recommender_service(self):
        payload = {
            \"user_id\": 1,
            \"item_id\": 101
        }
        try:
            response = requests.post(self.RECOMMENDER_SERVICE_URL, json=payload)
            self.assertEqual(response.status_code, 200)
            data = response.get_json()
            self.assertIn(\"recommendations\", data)
            self.assertEqual(data[\"recommendations\"], [301, 302, 303])
            logger.info(\"Recommender Service endpoint test passed.\")
        except Exception as e:
            logger.error(f\"Recommender Service endpoint test failed: {type(e).__name__}: {e}\")
            self.fail(e)

if __name__ == '__main__':
    unittest.main()
"

create_dir "tests/e2e"

create_file "tests/e2e/test_end_to_end.py" "import unittest
from src.ai_chatbot.chatbot import app
from src.predictive_analytics.risk_assessment.catboost_risk_model import RiskAssessmentModel
import logging

logger = logging.getLogger('tests.e2e.test_end_to_end')

class TestEndToEnd(unittest.TestCase):
    def setUp(self):
        self.app = app.test_client()
        self.app.testing = True
        self.model = RiskAssessmentModel()

    def test_chatbot_and_risk_assessment(self):
        # Test Chatbot
        chatbot_response = self.app.post('/chat', json={'message': 'Hello'})
        self.assertEqual(chatbot_response.status_code, 200)
        data = chatbot_response.get_json()
        self.assertIn('response', data)
        self.assertEqual(data['response'], 'Echo: Hello')
        logger.info(\"End-to-End Chatbot test passed.\")

        # Test Risk Assessment
        risk_result = self.model.predict([[10]])
        self.assertIn('risk_score', risk_result)
        self.assertEqual(risk_result['risk_score'], 0.75)
        logger.info(\"End-to-End Risk Assessment test passed.\")

if __name__ == '__main__':
    unittest.main()
"

create_dir "tests/test_data"

create_file "tests/test_data/sample_data.py" "# sample_data.py (Sample Data for Testing)
import pandas as pd
import numpy as np

def get_sample_dataframe() -> pd.DataFrame:
    return pd.DataFrame({
        \"feature1\": [
            1,
            2,
            np.nan,
            4
        ],
        \"feature2\": [
            \"A\",
            \"B\",
            \"A\",
            \"B\"
        ]
    })
"

# 9. GitHub Actions Workflow
create_dir ".github/workflows"

create_file ".github/workflows/ci_cd.yml" "name: CI/CD Pipeline

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

jobs:
  build_and_test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.12'

      - name: Install Python Dependencies
        run: |
          bash deployment/ci_cd/build_scripts/install_dependencies.sh

      - name: Run Python Unit Tests
        run: |
          python3 -m unittest discover -s tests/unit

      - name: Run Python Integration Tests
        run: |
          python3 -m unittest discover -s tests/integration

      - name: Run Python End-to-End Tests
        run: |
          python3 -m unittest discover -s tests/e2e

      - name: Set up Go
        uses: actions/setup-go@v4
        with:
          go-version: '1.20'

      - name: Build and Test Go Recommender Service
        run: |
          cd src/personalization_engine/real_time_personalization/go_service
          go build -v .
          go test -v ./...

      - name: Set up Rust
        uses: actions-rs/toolchain@v1
        with:
          toolchain: stable
          override: true

      - name: Build and Test Rust Anomaly Detector
        run: |
          cd ../../../../cybersecurity_ai/anomaly_detection/rust_service
          cargo build --release
          cargo test

      - name: Build and Test C++ Anomaly Detector
        run: |
          bash tests/unit/test_cpp_anomaly_detector.sh

      - name: Build Docker Images
        run: |
          docker build -t your-docker-registry/ai-chatbot:latest -f deployment/docker/ai_chatbot_Dockerfile src/ai_chatbot
          docker build -t your-docker-registry/cpp-anomaly-detector:latest -f deployment/docker/cpp_module_Dockerfile src/cpp_module
          docker build -t your-docker-registry/rust-anomaly-detector:latest -f deployment/docker/rust_anomaly_detector_Dockerfile src/cybersecurity_ai/anomaly_detection/rust_service
          docker build -t your-docker-registry/go_recommender_service:latest -f deployment/docker/go_recommender_service_Dockerfile src/personalization_engine/real_time_personalization/go_service
          docker build -t your-docker-registry/predictive-analytics:latest -f deployment/docker/predictive_analytics_Dockerfile src/predictive_analytics
          docker build -t your-docker-registry/personalization-engine:latest -f deployment/docker/personalization_engine_Dockerfile src/personalization_engine/recommender
          docker build -t your-docker-registry/content_creation_ai:latest -f deployment/docker/content_creation_ai_Dockerfile src/content_creation_ai
          docker build -t your-docker-registry/healthcare_ai:latest -f deployment/docker/healthcare_ai_Dockerfile src/healthcare_ai
          docker build -t your-docker-registry/ai_consulting:latest -f deployment/docker/ai_consulting_Dockerfile src/ai_consulting/strategy_development
          docker build -t your-docker-registry/vr_ar_ai:latest -f deployment/docker/vr_ar_ai_Dockerfile src/vr_ar_ai
          docker build -t your-docker-registry/supply_chain_ai:latest -f deployment/docker/supply_chain_ai_Dockerfile src/supply_chain_ai
          docker build -t your-docker-registry/automl:latest -f deployment/docker/automl_Dockerfile src/automl

      - name: Log in to Docker Hub
        uses: docker/login-action@v2
        with:
          registry: your-docker-registry
          username: \${{ secrets.DOCKER_USERNAME }}
          password: \${{ secrets.DOCKER_PASSWORD }}

      - name: Push Docker Images
        run: |
          docker push your-docker-registry/ai-chatbot:latest
          docker push your-docker-registry/cpp-anomaly-detector:latest
          docker push your-docker-registry/rust-anomaly-detector:latest
          docker push your-docker-registry/go_recommender_service:latest
          docker push your-docker-registry/predictive-analytics:latest
          docker push your-docker-registry/personalization-engine:latest
          docker push your-docker-registry/content_creation_ai:latest
          docker push your-docker-registry/healthcare_ai:latest
          docker push your-docker-registry/ai_consulting:latest
          docker push your-docker-registry/vr_ar_ai:latest
          docker push your-docker-registry/supply_chain_ai:latest
          docker push your-docker-registry/automl:latest

  deploy:
    needs: build_and_test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2

      - name: Set up kubectl
        uses: azure/setup-kubectl@v3
        with:
          version: 'latest'

      - name: Deploy to Kubernetes
        env:
          KUBE_CONFIG_DATA: \${{ secrets.KUBE_CONFIG_DATA }}
        run: |
          echo \"\${KUBE_CONFIG_DATA}\" | base64 --decode > ~/.kube/config
          bash deployment/ci_cd/build_scripts/deploy.sh
"
# 10. Final Steps Message
echo "All files and directories have been created successfully. You can now run ./gen.sh to set up the environment, build, test, and deploy your AI Platform."