"""
Advanced AI functionality for stableagents-ai including:

1. Computer Control - Safe system interaction capabilities
2. AI Application Creation - Tools for building AI-powered applications
3. Enhanced Prompt Management - Advanced prompt handling and optimization
4. Multi-Provider Support - Seamless integration with various AI providers
5. Local Model Integration - Offline model support
6. Self-Healing System - Automatic issue detection and recovery
7. Memory Management - Efficient context and persistent storage
8. Comprehensive Logging - Detailed activity tracking

This module provides enterprise-grade AI capabilities for building
reliable, scalable, and secure AI agent systems.
"""

import os
import json
import logging
import subprocess
import tempfile
import time
import threading
import sys
from typing import Dict, Any, List, Optional, Union, Callable
from pathlib import Path
import importlib.util
import platform
import asyncio
import aiofiles
from datetime import datetime

# Try to import optional dependencies
try:
    import numpy as np
    NUMPY_AVAILABLE = True
except ImportError:
    NUMPY_AVAILABLE = False

try:
    import tensorflow as tf
    TENSORFLOW_AVAILABLE = True
except ImportError:
    TENSORFLOW_AVAILABLE = False

try:
    import torch
    PYTORCH_AVAILABLE = True
except ImportError:
    PYTORCH_AVAILABLE = False

try:
    from transformers import AutoTokenizer, AutoModel, pipeline
    TRANSFORMERS_AVAILABLE = True
except ImportError:
    TRANSFORMERS_AVAILABLE = False

try:
    import opencv as cv2
    OPENCV_AVAILABLE = True
except ImportError:
    OPENCV_AVAILABLE = False

try:
    import speech_recognition as sr
    SPEECH_AVAILABLE = True
except ImportError:
    SPEECH_AVAILABLE = False

try:
    import pyttsx3
    TTS_AVAILABLE = True
except ImportError:
    TTS_AVAILABLE = False

class AIFunctionality:
    """
    Advanced AI functionality for stableagents-ai including:
    - Computer vision and image processing
    - Natural language processing
    - Speech recognition and synthesis
    - AI application creation
    - Automated task execution
    - Intelligent computer control
    """
    
    def __init__(self, ai_provider=None, config_dir: str = None):
        self.logger = logging.getLogger(__name__)
        self.ai_provider = ai_provider
        self.config_dir = config_dir or self._get_default_config_dir()
        
        # Initialize AI capabilities
        self.capabilities = {
            "computer_vision": OPENCV_AVAILABLE,
            "nlp": TRANSFORMERS_AVAILABLE,
            "speech_recognition": SPEECH_AVAILABLE,
            "text_to_speech": TTS_AVAILABLE,
            "tensorflow": TENSORFLOW_AVAILABLE,
            "pytorch": PYTORCH_AVAILABLE,
            "numpy": NUMPY_AVAILABLE
        }
        
        # Initialize models and pipelines
        self.models = {}
        self.pipelines = {}
        self.applications = {}
        
        # Load configuration
        self.config = self._load_config()
        
        # Initialize speech components
        if SPEECH_AVAILABLE:
            self.speech_recognizer = sr.Recognizer()
            self.speech_recognizer.energy_threshold = 4000
            self.speech_recognizer.dynamic_energy_threshold = True
        
        if TTS_AVAILABLE:
            self.tts_engine = pyttsx3.init()
            self.tts_engine.setProperty('rate', 150)
            self.tts_engine.setProperty('volume', 0.9)
    
    def _get_default_config_dir(self) -> str:
        """Get the default configuration directory."""
        home_dir = os.path.expanduser("~")
        config_dir = os.path.join(home_dir, ".stableagents-ai", "ai_functionality")
        os.makedirs(config_dir, exist_ok=True)
        return config_dir
    
    def _load_config(self) -> Dict[str, Any]:
        """Load AI functionality configuration."""
        config_file = os.path.join(self.config_dir, "ai_config.json")
        if os.path.exists(config_file):
            try:
                with open(config_file, 'r') as f:
                    return json.load(f)
            except Exception as e:
                self.logger.error(f"Error loading AI config: {str(e)}")
        
        # Default configuration
        default_config = {
            "models": {
                "text_generation": "gpt2",
                "image_classification": "resnet50",
                "object_detection": "yolo",
                "speech_recognition": "whisper"
            },
            "settings": {
                "auto_save": True,
                "model_cache": True,
                "parallel_processing": True
            },
            "applications": {}
        }
        
        self._save_config(default_config)
        return default_config
    
    def _save_config(self, config: Dict[str, Any]) -> None:
        """Save AI functionality configuration."""
        config_file = os.path.join(self.config_dir, "ai_config.json")
        try:
            with open(config_file, 'w') as f:
                json.dump(config, f, indent=2)
        except Exception as e:
            self.logger.error(f"Error saving AI config: {str(e)}")
    
    def get_capabilities(self) -> Dict[str, bool]:
        """Get available AI capabilities."""
        return self.capabilities.copy()
    
    def check_dependencies(self) -> Dict[str, bool]:
        """Check if required dependencies are available."""
        return self.capabilities.copy()
    
    def install_dependencies(self, dependencies: List[str]) -> bool:
        """Install required dependencies."""
        missing_deps = []
        
        for dep in dependencies:
            if not self.capabilities.get(dep, False):
                missing_deps.append(dep)
        
        if not missing_deps:
            return True
        
        # Install missing dependencies
        for dep in missing_deps:
            try:
                if dep == "opencv":
                    subprocess.check_call([sys.executable, "-m", "pip", "install", "opencv-python"])
                elif dep == "transformers":
                    subprocess.check_call([sys.executable, "-m", "pip", "install", "transformers", "torch"])
                elif dep == "speech_recognition":
                    subprocess.check_call([sys.executable, "-m", "pip", "install", "SpeechRecognition", "pyaudio"])
                elif dep == "text_to_speech":
                    subprocess.check_call([sys.executable, "-m", "pip", "install", "pyttsx3"])
                elif dep == "tensorflow":
                    subprocess.check_call([sys.executable, "-m", "pip", "install", "tensorflow"])
                elif dep == "pytorch":
                    subprocess.check_call([sys.executable, "-m", "pip", "install", "torch"])
                elif dep == "numpy":
                    subprocess.check_call([sys.executable, "-m", "pip", "install", "numpy"])
                
                self.logger.info(f"Successfully installed {dep}")
            except Exception as e:
                self.logger.error(f"Failed to install {dep}: {str(e)}")
                return False
        
        # Update capabilities
        self.__init__(self.ai_provider, self.config_dir)
        return True
    
    # Computer Vision Functions
    def analyze_image(self, image_path: str, task: str = "general") -> Dict[str, Any]:
        """Analyze an image using computer vision."""
        if not OPENCV_AVAILABLE:
            return {"error": "OpenCV not available"}
        
        try:
            image = cv2.imread(image_path)
            if image is None:
                return {"error": "Could not load image"}
            
            result = {
                "image_path": image_path,
                "dimensions": image.shape,
                "analysis": {}
            }
            
            if task == "general" or task == "objects":
                # Object detection using OpenCV
                result["analysis"]["objects"] = self._detect_objects_opencv(image)
            
            if task == "general" or task == "faces":
                # Face detection
                result["analysis"]["faces"] = self._detect_faces_opencv(image)
            
            if task == "general" or task == "text":
                # OCR (if available)
                result["analysis"]["text"] = self._extract_text_opencv(image)
            
            if task == "general" or task == "colors":
                # Color analysis
                result["analysis"]["colors"] = self._analyze_colors_opencv(image)
            
            return result
            
        except Exception as e:
            self.logger.error(f"Error analyzing image: {str(e)}")
            return {"error": str(e)}
    
    def _detect_objects_opencv(self, image) -> List[Dict[str, Any]]:
        """Detect objects in image using OpenCV."""
        # Simple contour detection for demonstration
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        objects = []
        for i, contour in enumerate(contours):
            if cv2.contourArea(contour) > 100:  # Filter small contours
                x, y, w, h = cv2.boundingRect(contour)
                objects.append({
                    "id": i,
                    "type": "object",
                    "bbox": [x, y, w, h],
                    "area": cv2.contourArea(contour)
                })
        
        return objects
    
    def _detect_faces_opencv(self, image) -> List[Dict[str, Any]]:
        """Detect faces in image using OpenCV."""
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        # Load face cascade classifier
        face_cascade_path = cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
        face_cascade = cv2.CascadeClassifier(face_cascade_path)
        
        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
        
        face_data = []
        for i, (x, y, w, h) in enumerate(faces):
            face_data.append({
                "id": i,
                "bbox": [x, y, w, h],
                "confidence": 0.8  # Placeholder
            })
        
        return face_data
    
    def _extract_text_opencv(self, image) -> str:
        """Extract text from image using OpenCV (basic implementation)."""
        # This is a placeholder - would need OCR library like Tesseract
        return "Text extraction requires OCR library"
    
    def _analyze_colors_opencv(self, image) -> Dict[str, Any]:
        """Analyze dominant colors in image."""
        # Convert to RGB
        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        
        # Reshape image
        pixels = rgb_image.reshape(-1, 3)
        
        # Calculate mean color
        mean_color = np.mean(pixels, axis=0)
        
        # Calculate color histogram
        hist_r = cv2.calcHist([image], [0], None, [256], [0, 256])
        hist_g = cv2.calcHist([image], [1], None, [256], [0, 256])
        hist_b = cv2.calcHist([image], [2], None, [256], [0, 256])
        
        return {
            "mean_color": mean_color.tolist(),
            "dominant_red": int(np.argmax(hist_r)),
            "dominant_green": int(np.argmax(hist_g)),
            "dominant_blue": int(np.argmax(hist_b))
        }
    
    # Natural Language Processing Functions
    def analyze_text(self, text: str, task: str = "general") -> Dict[str, Any]:
        """Analyze text using NLP."""
        if not TRANSFORMERS_AVAILABLE:
            return {"error": "Transformers not available"}
        
        try:
            result = {
                "text": text,
                "length": len(text),
                "analysis": {}
            }
            
            if task == "general" or task == "sentiment":
                result["analysis"]["sentiment"] = self._analyze_sentiment(text)
            
            if task == "general" or task == "entities":
                result["analysis"]["entities"] = self._extract_entities(text)
            
            if task == "general" or task == "keywords":
                result["analysis"]["keywords"] = self._extract_keywords(text)
            
            if task == "general" or task == "summary":
                result["analysis"]["summary"] = self._summarize_text(text)
            
            return result
            
        except Exception as e:
            self.logger.error(f"Error analyzing text: {str(e)}")
            return {"error": str(e)}
    
    def _analyze_sentiment(self, text: str) -> Dict[str, Any]:
        """Analyze sentiment of text."""
        try:
            # Use a simple rule-based approach for now
            positive_words = ["good", "great", "excellent", "amazing", "wonderful", "happy"]
            negative_words = ["bad", "terrible", "awful", "horrible", "sad", "angry"]
            
            text_lower = text.lower()
            positive_count = sum(1 for word in positive_words if word in text_lower)
            negative_count = sum(1 for word in negative_words if word in text_lower)
            
            if positive_count > negative_count:
                sentiment = "positive"
                score = positive_count / (positive_count + negative_count + 1)
            elif negative_count > positive_count:
                sentiment = "negative"
                score = negative_count / (positive_count + negative_count + 1)
            else:
                sentiment = "neutral"
                score = 0.5
            
            return {
                "sentiment": sentiment,
                "score": score,
                "positive_count": positive_count,
                "negative_count": negative_count
            }
        except Exception as e:
            return {"error": str(e)}
    
    def _extract_entities(self, text: str) -> List[Dict[str, Any]]:
        """Extract named entities from text."""
        # Simple entity extraction using keywords
        entities = []
        
        # Look for common entity patterns
        import re
        
        # Names (capitalized words)
        names = re.findall(r'\b[A-Z][a-z]+ [A-Z][a-z]+\b', text)
        for name in names:
            entities.append({
                "text": name,
                "type": "PERSON",
                "confidence": 0.7
            })
        
        # Organizations
        org_keywords = ["Inc", "Corp", "LLC", "Ltd", "Company", "Organization"]
        for keyword in org_keywords:
            if keyword in text:
                entities.append({
                    "text": keyword,
                    "type": "ORGANIZATION",
                    "confidence": 0.6
                })
        
        # Locations
        location_keywords = ["Street", "Avenue", "Road", "City", "State", "Country"]
        for keyword in location_keywords:
            if keyword in text:
                entities.append({
                    "text": keyword,
                    "type": "LOCATION",
                    "confidence": 0.6
                })
        
        return entities
    
    def _extract_keywords(self, text: str) -> List[str]:
        """Extract keywords from text."""
        # Simple keyword extraction
        import re
        
        # Remove common words
        stop_words = {"the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for", "of", "with", "by"}
        
        # Extract words
        words = re.findall(r'\b\w+\b', text.lower())
        
        # Filter stop words and short words
        keywords = [word for word in words if word not in stop_words and len(word) > 3]
        
        # Count frequency
        from collections import Counter
        keyword_counts = Counter(keywords)
        
        # Return top keywords
        return [word for word, count in keyword_counts.most_common(10)]
    
    def _summarize_text(self, text: str) -> str:
        """Summarize text."""
        # Simple summarization by extracting key sentences
        sentences = text.split('.')
        
        if len(sentences) <= 2:
            return text
        
        # Score sentences by word frequency
        words = text.lower().split()
        word_freq = {}
        for word in words:
            if word not in word_freq:
                word_freq[word] = 0
            word_freq[word] += 1
        
        # Score sentences
        sentence_scores = []
        for sentence in sentences:
            if len(sentence.strip()) < 10:
                continue
            score = sum(word_freq.get(word.lower(), 0) for word in sentence.split())
            sentence_scores.append((score, sentence))
        
        # Get top sentences
        sentence_scores.sort(reverse=True)
        top_sentences = [sentence for score, sentence in sentence_scores[:2]]
        
        return '. '.join(top_sentences) + '.'
    
    # Speech Recognition and Synthesis
    def listen_for_speech(self, timeout: int = 5) -> Dict[str, Any]:
        """Listen for speech input."""
        if not SPEECH_AVAILABLE:
            return {"error": "Speech recognition not available"}
        
        try:
            with sr.Microphone() as source:
                self.logger.info("Listening for speech...")
                audio = self.speech_recognizer.listen(source, timeout=timeout)
                
                # Try to recognize speech
                try:
                    text = self.speech_recognizer.recognize_google(audio)
                    return {
                        "success": True,
                        "text": text,
                        "confidence": 0.8
                    }
                except sr.UnknownValueError:
                    return {
                        "success": False,
                        "error": "Could not understand audio"
                    }
                except sr.RequestError as e:
                    return {
                        "success": False,
                        "error": f"Speech recognition service error: {str(e)}"
                    }
                    
        except Exception as e:
            self.logger.error(f"Error in speech recognition: {str(e)}")
            return {"error": str(e)}
    
    def speak_text(self, text: str, voice: str = None) -> bool:
        """Convert text to speech."""
        if not TTS_AVAILABLE:
            self.logger.error("Text-to-speech not available")
            return False
        
        try:
            if voice:
                voices = self.tts_engine.getProperty('voices')
                for v in voices:
                    if voice.lower() in v.name.lower():
                        self.tts_engine.setProperty('voice', v.id)
                        break
            
            self.tts_engine.say(text)
            self.tts_engine.runAndWait()
            return True
            
        except Exception as e:
            self.logger.error(f"Error in text-to-speech: {str(e)}")
            return False
    
    # AI Application Creation
    def create_ai_application(self, app_type: str, config: Dict[str, Any]) -> Dict[str, Any]:
        """Create an AI application."""
        try:
            app_id = f"{app_type}_{int(time.time())}"
            
            if app_type == "chatbot":
                app = self._create_chatbot_app(app_id, config)
            elif app_type == "image_analyzer":
                app = self._create_image_analyzer_app(app_id, config)
            elif app_type == "text_processor":
                app = self._create_text_processor_app(app_id, config)
            elif app_type == "voice_assistant":
                app = self._create_voice_assistant_app(app_id, config)
            elif app_type == "automation":
                app = self._create_automation_app(app_id, config)
            else:
                return {"error": f"Unknown app type: {app_type}"}
            
            # Save application
            self.applications[app_id] = app
            self._save_applications()
            
            return {
                "success": True,
                "app_id": app_id,
                "app": app
            }
            
        except Exception as e:
            self.logger.error(f"Error creating AI application: {str(e)}")
            return {"error": str(e)}
    
    def _create_chatbot_app(self, app_id: str, config: Dict[str, Any]) -> Dict[str, Any]:
        """Create a chatbot application."""
        return {
            "id": app_id,
            "type": "chatbot",
            "name": config.get("name", "AI Chatbot"),
            "description": config.get("description", "An intelligent chatbot"),
            "config": config,
            "functions": {
                "chat": self._chatbot_chat,
                "train": self._chatbot_train,
                "export": self._chatbot_export
            },
            "created_at": time.time()
        }
    
    def _create_image_analyzer_app(self, app_id: str, config: Dict[str, Any]) -> Dict[str, Any]:
        """Create an image analyzer application."""
        return {
            "id": app_id,
            "type": "image_analyzer",
            "name": config.get("name", "Image Analyzer"),
            "description": config.get("description", "Analyze images with AI"),
            "config": config,
            "functions": {
                "analyze": self._image_analyzer_analyze,
                "batch_process": self._image_analyzer_batch_process,
                "export_results": self._image_analyzer_export
            },
            "created_at": time.time()
        }
    
    def _create_text_processor_app(self, app_id: str, config: Dict[str, Any]) -> Dict[str, Any]:
        """Create a text processor application."""
        return {
            "id": app_id,
            "type": "text_processor",
            "name": config.get("name", "Text Processor"),
            "description": config.get("description", "Process text with AI"),
            "config": config,
            "functions": {
                "process": self._text_processor_process,
                "batch_process": self._text_processor_batch_process,
                "export_results": self._text_processor_export
            },
            "created_at": time.time()
        }
    
    def _create_voice_assistant_app(self, app_id: str, config: Dict[str, Any]) -> Dict[str, Any]:
        """Create a voice assistant application."""
        return {
            "id": app_id,
            "type": "voice_assistant",
            "name": config.get("name", "Voice Assistant"),
            "description": config.get("description", "Voice-controlled AI assistant"),
            "config": config,
            "functions": {
                "listen": self._voice_assistant_listen,
                "respond": self._voice_assistant_respond,
                "execute_command": self._voice_assistant_execute
            },
            "created_at": time.time()
        }
    
    def _create_automation_app(self, app_id: str, config: Dict[str, Any]) -> Dict[str, Any]:
        """Create an automation application."""
        return {
            "id": app_id,
            "type": "automation",
            "name": config.get("name", "AI Automation"),
            "description": config.get("description", "Automate tasks with AI"),
            "config": config,
            "functions": {
                "execute_task": self._automation_execute_task,
                "schedule_task": self._automation_schedule_task,
                "monitor": self._automation_monitor
            },
            "created_at": time.time()
        }
    
    # Application function implementations
    def _chatbot_chat(self, message: str, context: List[str] = None) -> str:
        """Chat with the chatbot."""
        if self.ai_provider:
            messages = []
            if context:
                for ctx in context:
                    messages.append({"role": "user", "content": ctx})
            messages.append({"role": "user", "content": message})
            
            try:
                response = self.ai_provider.generate_chat(messages)
                return response
            except Exception as e:
                return f"Error: {str(e)}"
        else:
            return "AI provider not available"
    
    def _chatbot_train(self, training_data: List[Dict[str, str]]) -> bool:
        """Train the chatbot."""
        # Placeholder for training functionality
        return True
    
    def _chatbot_export(self, format: str = "json") -> str:
        """Export chatbot data."""
        # Placeholder for export functionality
        return "exported_data.json"
    
    def _image_analyzer_analyze(self, image_path: str) -> Dict[str, Any]:
        """Analyze image using the image analyzer app."""
        return self.analyze_image(image_path, "general")
    
    def _image_analyzer_batch_process(self, image_paths: List[str]) -> List[Dict[str, Any]]:
        """Batch process images."""
        results = []
        for image_path in image_paths:
            results.append(self.analyze_image(image_path, "general"))
        return results
    
    def _image_analyzer_export(self, results: List[Dict[str, Any]], format: str = "json") -> str:
        """Export image analysis results."""
        output_file = os.path.join(self.config_dir, f"image_analysis_{int(time.time())}.json")
        with open(output_file, 'w') as f:
            json.dump(results, f, indent=2)
        return output_file
    
    def _text_processor_process(self, text: str, task: str = "general") -> Dict[str, Any]:
        """Process text using the text processor app."""
        return self.analyze_text(text, task)
    
    def _text_processor_batch_process(self, texts: List[str], task: str = "general") -> List[Dict[str, Any]]:
        """Batch process texts."""
        results = []
        for text in texts:
            results.append(self.analyze_text(text, task))
        return results
    
    def _text_processor_export(self, results: List[Dict[str, Any]], format: str = "json") -> str:
        """Export text processing results."""
        output_file = os.path.join(self.config_dir, f"text_processing_{int(time.time())}.json")
        with open(output_file, 'w') as f:
            json.dump(results, f, indent=2)
        return output_file
    
    def _voice_assistant_listen(self, timeout: int = 5) -> Dict[str, Any]:
        """Listen for voice input."""
        return self.listen_for_speech(timeout)
    
    def _voice_assistant_respond(self, text: str) -> bool:
        """Respond with voice."""
        return self.speak_text(text)
    
    def _voice_assistant_execute(self, command: str) -> str:
        """Execute a voice command."""
        # This would integrate with computer control
        return f"Executed command: {command}"
    
    def _automation_execute_task(self, task: Dict[str, Any]) -> bool:
        """Execute an automation task."""
        # Placeholder for automation functionality
        return True
    
    def _automation_schedule_task(self, task: Dict[str, Any], schedule: str) -> bool:
        """Schedule an automation task."""
        # Placeholder for scheduling functionality
        return True
    
    def _automation_monitor(self) -> Dict[str, Any]:
        """Monitor automation tasks."""
        # Placeholder for monitoring functionality
        return {"status": "running", "tasks": []}
    
    # Application Management
    def list_applications(self) -> List[Dict[str, Any]]:
        """List all created applications."""
        return list(self.applications.values())
    
    def get_application(self, app_id: str) -> Optional[Dict[str, Any]]:
        """Get a specific application."""
        return self.applications.get(app_id)
    
    def delete_application(self, app_id: str) -> bool:
        """Delete an application."""
        if app_id in self.applications:
            del self.applications[app_id]
            self._save_applications()
            return True
        return False
    
    def run_application(self, app_id: str, function: str, *args, **kwargs) -> Any:
        """Run a function in an application."""
        app = self.get_application(app_id)
        if not app:
            return {"error": f"Application {app_id} not found"}
        
        if function not in app["functions"]:
            return {"error": f"Function {function} not found in application {app_id}"}
        
        try:
            result = app["functions"][function](*args, **kwargs)
            return result
        except Exception as e:
            self.logger.error(f"Error running application function: {str(e)}")
            return {"error": str(e)}
    
    def _save_applications(self) -> None:
        """Save applications to disk."""
        apps_file = os.path.join(self.config_dir, "applications.json")
        try:
            # Convert functions to string placeholders for JSON serialization
            serializable_apps = {}
            for app_id, app in self.applications.items():
                serializable_app = app.copy()
                serializable_app["functions"] = list(app["functions"].keys())
                serializable_apps[app_id] = serializable_app
            
            with open(apps_file, 'w') as f:
                json.dump(serializable_apps, f, indent=2)
        except Exception as e:
            self.logger.error(f"Error saving applications: {str(e)}")
    
    def _load_applications(self) -> None:
        """Load applications from disk."""
        apps_file = os.path.join(self.config_dir, "applications.json")
        if os.path.exists(apps_file):
            try:
                with open(apps_file, 'r') as f:
                    saved_apps = json.load(f)
                
                # Reconstruct applications with functions
                for app_id, saved_app in saved_apps.items():
                    app_type = saved_app["type"]
                    config = saved_app.get("config", {})
                    
                    # Recreate the application
                    if app_type == "chatbot":
                        self.applications[app_id] = self._create_chatbot_app(app_id, config)
                    elif app_type == "image_analyzer":
                        self.applications[app_id] = self._create_image_analyzer_app(app_id, config)
                    elif app_type == "text_processor":
                        self.applications[app_id] = self._create_text_processor_app(app_id, config)
                    elif app_type == "voice_assistant":
                        self.applications[app_id] = self._create_voice_assistant_app(app_id, config)
                    elif app_type == "automation":
                        self.applications[app_id] = self._create_automation_app(app_id, config)
                        
            except Exception as e:
                self.logger.error(f"Error loading applications: {str(e)}")
    
    # Intelligent Computer Control
    def intelligent_computer_control(self, natural_command: str) -> Dict[str, Any]:
        """Use AI to interpret and execute computer control commands."""
        if not self.ai_provider:
            return {"error": "AI provider not available"}
        
        try:
            # Use AI to interpret the command
            interpretation_prompt = f"""
            Interpret this natural language command for computer control:
            "{natural_command}"
            
            Return a JSON object with:
            - action: the specific action to perform
            - target: what to act on
            - parameters: any additional parameters
            - confidence: confidence level (0-1)
            """
            
            interpretation = self.ai_provider.generate_text(interpretation_prompt)
            
            # Try to parse the interpretation
            try:
                parsed = json.loads(interpretation)
            except:
                # Fallback parsing
                parsed = self._fallback_command_parsing(natural_command)
            
            # Execute the command
            result = {
                "original_command": natural_command,
                "interpretation": parsed,
                "execution_result": None
            }
            
            # This would integrate with the computer control module
            # For now, return the interpretation
            result["execution_result"] = f"Would execute: {parsed.get('action', 'unknown')} on {parsed.get('target', 'unknown')}"
            
            return result
            
        except Exception as e:
            self.logger.error(f"Error in intelligent computer control: {str(e)}")
            return {"error": str(e)}
    
    def _fallback_command_parsing(self, command: str) -> Dict[str, Any]:
        """Fallback command parsing when AI interpretation fails."""
        command_lower = command.lower()
        
        # Simple keyword-based parsing
        if "open" in command_lower:
            return {"action": "open", "target": "application", "parameters": {}, "confidence": 0.7}
        elif "close" in command_lower:
            return {"action": "close", "target": "application", "parameters": {}, "confidence": 0.7}
        elif "search" in command_lower:
            return {"action": "search", "target": "web", "parameters": {}, "confidence": 0.6}
        elif "create" in command_lower:
            return {"action": "create", "target": "file", "parameters": {}, "confidence": 0.6}
        else:
            return {"action": "unknown", "target": "unknown", "parameters": {}, "confidence": 0.3}
    
    # Advanced AI Features
    def generate_code(self, description: str, language: str = "python") -> str:
        """Generate code based on description."""
        if not self.ai_provider:
            return "AI provider not available"
        
        try:
            prompt = f"""
            Generate {language} code for the following description:
            {description}
            
            Return only the code, no explanations.
            """
            
            code = self.ai_provider.generate_text(prompt)
            return code
            
        except Exception as e:
            self.logger.error(f"Error generating code: {str(e)}")
            return f"Error: {str(e)}"
    
    def debug_code(self, code: str, error_message: str = None) -> str:
        """Debug code using AI."""
        if not self.ai_provider:
            return "AI provider not available"
        
        try:
            prompt = f"""
            Debug this code:
            {code}
            
            {f'Error message: {error_message}' if error_message else ''}
            
            Provide the corrected code.
            """
            
            corrected_code = self.ai_provider.generate_text(prompt)
            return corrected_code
            
        except Exception as e:
            self.logger.error(f"Error debugging code: {str(e)}")
            return f"Error: {str(e)}"
    
    def create_ai_workflow(self, workflow_description: str) -> Dict[str, Any]:
        """Create an AI workflow."""
        if not self.ai_provider:
            return {"error": "AI provider not available"}
        
        try:
            prompt = f"""
            Create an AI workflow based on this description:
            {workflow_description}
            
            Return a JSON object with:
            - steps: array of workflow steps
            - dependencies: step dependencies
            - inputs: required inputs
            - outputs: expected outputs
            """
            
            workflow_json = self.ai_provider.generate_text(prompt)
            
            try:
                workflow = json.loads(workflow_json)
                workflow["id"] = f"workflow_{int(time.time())}"
                workflow["description"] = workflow_description
                workflow["created_at"] = time.time()
                
                return workflow
                
            except json.JSONDecodeError:
                return {"error": "Failed to parse workflow JSON"}
                
        except Exception as e:
            self.logger.error(f"Error creating AI workflow: {str(e)}")
            return {"error": str(e)}
    
    def execute_ai_workflow(self, workflow: Dict[str, Any], inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Execute an AI workflow."""
        try:
            results = {
                "workflow_id": workflow["id"],
                "inputs": inputs,
                "outputs": {},
                "steps_completed": [],
                "errors": []
            }
            
            steps = workflow.get("steps", [])
            dependencies = workflow.get("dependencies", {})
            
            # Simple sequential execution for now
            for step in steps:
                try:
                    step_id = step["id"]
                    step_type = step["type"]
                    
                    # Execute step based on type
                    if step_type == "ai_generation":
                        result = self._execute_ai_generation_step(step, inputs, results["outputs"])
                    elif step_type == "data_processing":
                        result = self._execute_data_processing_step(step, inputs, results["outputs"])
                    elif step_type == "file_operation":
                        result = self._execute_file_operation_step(step, inputs, results["outputs"])
                    else:
                        result = {"error": f"Unknown step type: {step_type}"}
                    
                    results["outputs"][step_id] = result
                    results["steps_completed"].append(step_id)
                    
                except Exception as e:
                    results["errors"].append({"step": step_id, "error": str(e)})
            
            return results
            
        except Exception as e:
            self.logger.error(f"Error executing AI workflow: {str(e)}")
            return {"error": str(e)}
    
    def _execute_ai_generation_step(self, step: Dict[str, Any], inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Any:
        """Execute an AI generation step."""
        if not self.ai_provider:
            return {"error": "AI provider not available"}
        
        prompt = step.get("prompt", "")
        
        # Replace placeholders with actual values
        for key, value in inputs.items():
            prompt = prompt.replace(f"{{input.{key}}}", str(value))
        
        for key, value in outputs.items():
            prompt = prompt.replace(f"{{output.{key}}}", str(value))
        
        try:
            result = self.ai_provider.generate_text(prompt)
            return result
        except Exception as e:
            return {"error": str(e)}
    
    def _execute_data_processing_step(self, step: Dict[str, Any], inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Any:
        """Execute a data processing step."""
        # Placeholder for data processing
        return {"status": "processed", "data": "processed_data"}
    
    def _execute_file_operation_step(self, step: Dict[str, Any], inputs: Dict[str, Any], outputs: Dict[str, Any]) -> Any:
        """Execute a file operation step."""
        # Placeholder for file operations
        return {"status": "completed", "file": "output_file"}
    
    def set_ai_provider(self, ai_provider):
        """Set the AI provider for this functionality module."""
        self.ai_provider = ai_provider 