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annator-full-system / scripts /deploy_phase3_complete.sh

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#!/bin/bash

# ATOM Chat Interface - Phase 3 Complete Deployment
# Advanced AI-Powered Conversation Intelligence, Multi-Modal Enhancement, and Voice Integration

set -e  # Exit on any error

# Configuration
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_ROOT="$SCRIPT_DIR"
BACKEND_DIR="$PROJECT_ROOT/backend"
LOG_DIR="$PROJECT_ROOT/logs"
CONFIG_DIR="$PROJECT_ROOT/config"
PHASE3_DIR="$PROJECT_ROOT/phase3_deployment"
DEPLOYMENT_LOG="$LOG_DIR/phase3_deployment.log"

# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
PURPLE='\033[0;35m'
CYAN='\033[0;36m'
NC='\033[0m' # No Color

# Logging functions
log_info() {
    echo -e "${BLUE}[INFO]${NC} $1" | tee -a "$DEPLOYMENT_LOG"
}

log_success() {
    echo -e "${GREEN}[SUCCESS]${NC} $1" | tee -a "$DEPLOYMENT_LOG"
}

log_warning() {
    echo -e "${YELLOW}[WARNING]${NC} $1" | tee -a "$DEPLOYMENT_LOG"
}

log_error() {
    echo -e "${RED}[ERROR]${NC} $1" | tee -a "$DEPLOYMENT_LOG"
}

log_phase() {
    echo -e "${PURPLE}[PHASE 3]${NC} $1" | tee -a "$DEPLOYMENT_LOG"
}

log_feature() {
    echo -e "${CYAN}[FEATURE]${NC} $1" | tee -a "$DEPLOYMENT_LOG"
}

# Initialize deployment
initialize_deployment() {
    log_phase "🚀 Starting Phase 3 Complete Deployment"
    log_info "Project Root: $PROJECT_ROOT"
    log_info "Backend Directory: $BACKEND_DIR"
    log_info "Log Directory: $LOG_DIR"

    # Create necessary directories
    mkdir -p "$LOG_DIR"
    mkdir -p "$PHASE3_DIR"
    mkdir -p "$BACKEND_DIR/phase3_modules"

    # Clear previous deployment log
    > "$DEPLOYMENT_LOG"

    log_success "Deployment environment initialized"
}

# Check prerequisites
check_prerequisites() {
    log_phase "🔍 Checking prerequisites for Phase 3 deployment..."

    # Check Python
    if command -v python3 &> /dev/null; then
        PYTHON_VERSION=$(python3 --version | cut -d' ' -f2)
        log_success "Python $PYTHON_VERSION found"
    else
        log_error "Python 3 is required but not installed"
        exit 1
    fi

    # Check pip
    if command -v pip3 &> /dev/null; then
        log_success "pip3 found"
    else
        log_error "pip3 is required but not installed"
        exit 1
    fi

    # Check if backend directory exists
    if [ ! -d "$BACKEND_DIR" ]; then
        log_error "Backend directory not found: $BACKEND_DIR"
        exit 1
    fi

    # Check if current chat interface is running
    if curl -s http://localhost:8000/health > /dev/null; then
        log_success "Current Chat Interface Server is running"
    else
        log_warning "Current Chat Interface Server is not running - will deploy new instance"
    fi

    log_success "All prerequisites satisfied"
}

# Install Phase 3 advanced dependencies
install_phase3_dependencies() {
    log_phase "📦 Installing Phase 3 Advanced Dependencies..."

    cd "$BACKEND_DIR"

    log_feature "Installing AI/ML libraries for conversation intelligence..."
    pip3 install transformers torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
    pip3 install sentence-transformers spacy scikit-learn
    pip3 install nltk textblob vaderSentiment

    # Download spaCy model
    log_feature "Downloading spaCy language model..."
    python3 -m spacy download en_core_web_sm

    log_feature "Installing advanced file processing libraries..."
    pip3 install PyPDF2 python-docx openpyxl pptx pdfplumber
    pip3 install Pillow opencv-python-headless pytesseract

    log_feature "Installing advanced voice processing libraries..."
    pip3 install speechrecognition pydub librosa soundfile
    pip3 install gtts pyttsx3 wave audioop

    log_feature "Installing analytics and monitoring libraries..."
    pip3 install prometheus-client matplotlib seaborn plotly
    pip3 install pandas numpy scipy

    log_success "Phase 3 dependencies installed successfully"
}

# Deploy AI Conversation Intelligence Module
deploy_ai_intelligence() {
    log_phase "🧠 Deploying AI Conversation Intelligence Module..."

    cd "$BACKEND_DIR"

    # Create AI conversation intelligence module
    cat > "ai_conversation_intelligence.py" << 'EOF'
"""
AI-Powered Conversation Intelligence Module
Advanced NLU, sentiment analysis, and context-aware response generation
Author: Atom Platform Engineering
Date: November 9, 2025
Version: 3.0.0
"""

import logging
from typing import Dict, List, Optional, Any
import json
from datetime import datetime

# Import AI/ML libraries
try:
    from transformers import pipeline, AutoTokenizer, AutoModel
    import torch
    import spacy
    from sentence_transformers import SentenceTransformer
    from textblob import TextBlob
    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
    AI_AVAILABLE = True
except ImportError as e:
    logging.warning(f"AI libraries not available: {e}")
    AI_AVAILABLE = False

class ConversationIntelligence:
    """Advanced AI-powered conversation intelligence"""

    def __init__(self):
        self.logger = logging.getLogger(__name__)
        self.nlp = None
        self.sentiment_analyzer = None
        self.sentence_model = None
        self.intent_classifier = None

        if AI_AVAILABLE:
            self.initialize_ai_models()

    def initialize_ai_models(self):
        """Initialize AI models for conversation intelligence"""
        try:
            # Load spaCy model for NLP
            self.nlp = spacy.load("en_core_web_sm")

            # Initialize sentiment analyzer
            self.sentiment_analyzer = SentimentIntensityAnalyzer()

            # Load sentence transformer for semantic similarity
            self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')

            # Initialize intent classifier
            self.intent_classifier = pipeline(
                "text-classification",
                model="joeddav/distilbert-base-uncased-go-emotions-student",
                return_all_scores=True
            )

            self.logger.info("AI conversation intelligence models initialized")

        except Exception as e:
            self.logger.error(f"Failed to initialize AI models: {e}")

    def analyze_sentiment(self, text: str) -> Dict[str, float]:
        """Analyze sentiment of text with multiple methods"""
        if not AI_AVAILABLE:
            return {"compound": 0.0, "positive": 0.0, "negative": 0.0, "neutral": 1.0}

        try:
            # VADER sentiment analysis
            vader_scores = self.sentiment_analyzer.polarity_scores(text)

            # TextBlob sentiment analysis
            blob = TextBlob(text)
            blob_sentiment = blob.sentiment

            # Combined sentiment score
            combined_score = {
                "vader_compound": vader_scores['compound'],
                "vader_positive": vader_scores['pos'],
                "vader_negative": vader_scores['neg'],
                "vader_neutral": vader_scores['neu'],
                "textblob_polarity": blob_sentiment.polarity,
                "textblob_subjectivity": blob_sentiment.subjectivity,
                "overall_sentiment": (vader_scores['compound'] + blob_sentiment.polarity) / 2
            }

            return combined_score

        except Exception as e:
            self.logger.error(f"Sentiment analysis failed: {e}")
            return {"error": str(e)}

    def classify_intent(self, text: str) -> List[Dict[str, Any]]:
        """Classify user intent from text"""
        if not AI_AVAILABLE or not self.intent_classifier:
            return [{"label": "neutral", "score": 1.0}]

        try:
            results = self.intent_classifier(text)
            # Return top 3 intents
            top_intents = sorted(results[0], key=lambda x: x['score'], reverse=True)[:3]
            return top_intents

        except Exception as e:
            self.logger.error(f"Intent classification failed: {e}")
            return [{"label": "error", "score": 1.0}]

    def extract_entities(self, text: str) -> List[Dict[str, str]]:
        """Extract named entities from text"""
        if not AI_AVAILABLE or not self.nlp:
            return []

        try:
            doc = self.nlp(text)
            entities = []

            for ent in doc.ents:
                entities.append({
                    "text": ent.text,
                    "label": ent.label_,
                    "start": ent.start_char,
                    "end": ent.end_char
                })

            return entities

        except Exception as e:
            self.logger.error(f"Entity extraction failed: {e}")
            return []

    def calculate_semantic_similarity(self, text1: str, text2: str) -> float:
        """Calculate semantic similarity between two texts"""
        if not AI_AVAILABLE or not self.sentence_model:
            return 0.0

        try:
            embeddings = self.sentence_model.encode([text1, text2])
            similarity = torch.nn.functional.cosine_similarity(
                torch.tensor(embeddings[0]).unsqueeze(0),
                torch.tensor(embeddings[1]).unsqueeze(0)
            )
            return similarity.item()

        except Exception as e:
            self.logger.error(f"Semantic similarity calculation failed: {e}")
            return 0.0

    def generate_context_aware_response(self, message: str, conversation_history: List[Dict]) -> Dict[str, Any]:
        """Generate context-aware response using conversation history"""
        analysis = {
            "timestamp": datetime.now().isoformat(),
            "message": message,
            "sentiment": self.analyze_sentiment(message),
            "intent": self.classify_intent(message),
            "entities": self.extract_entities(message),
            "context_relevance": 0.0,
            "suggested_actions": []
        }

        # Calculate context relevance with previous messages
        if conversation_history:
            recent_messages = conversation_history[-3:]  # Last 3 messages
            relevance_scores = []

            for prev_msg in recent_messages:
                if 'content' in prev_msg:
                    similarity = self.calculate_semantic_similarity(message, prev_msg['content'])
                    relevance_scores.append(similarity)

            if relevance_scores:
                analysis["context_relevance"] = sum(relevance_scores) / len(relevance_scores)

        # Generate suggested actions based on analysis
        if analysis["sentiment"].get("overall_sentiment", 0) < -0.3:
            analysis["suggested_actions"].append("escalate_to_human")
            analysis["suggested_actions"].append("offer_apology")

        if any(entity["label"] in ["PERSON", "ORG"] for entity in analysis["entities"]):
            analysis["suggested_actions"].append("personalize_response")

        return analysis

# Global instance
conversation_intelligence = ConversationIntelligence()
EOF

    log_success "AI Conversation Intelligence module deployed"
}

# Deploy Multi-Modal Enhancement Module
deploy_multi_modal_enhancement() {
    log_phase "📁 Deploying Multi-Modal Enhancement Module..."

    cd "$BACKEND_DIR"

    # Create multi-modal enhancement module
    cat > "multi_modal_enhancement.py" << 'EOF'
"""
Multi-Modal Experience Enhancement Module
Advanced file processing, image analysis, and rich media responses
Author: Atom Platform Engineering
Date: November 9, 2025
Version: 3.0.0
"""

import logging
import os
from typing import Dict, List, Optional, Any
import json
from pathlib import Path

# Import file processing libraries
try:
    from PyPDF2 import PdfReader
    import docx
    from openpyxl import load_workbook
    import pptx
    import pdfplumber
    from PIL import Image, ImageEnhance, ImageFilter
    import cv2
    import pytesseract
    FILE_PROCESSING_AVAILABLE = True
except ImportError as e:
    logging.warning(f"File processing libraries not available: {e}")
    FILE_PROCESSING_AVAILABLE = False

class MultiModalEnhancement:
    """Advanced multi-modal chat experience enhancement"""

    def __init__(self):
        self.logger = logging.getLogger(__name__)
        self.supported_formats = {
            'documents': ['.pdf', '.docx', '.xlsx', '.pptx', '.txt', '.csv'],
            'images': ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff'],
            'audio': ['.mp3', '.wav', '.ogg', '.m4a'],
            'video': ['.mp4', '.avi', '.mov', '.wmv']
        }

    def process_document(self, file_path: str, file_type: str) -> Dict[str, Any]:
        """Process various document types and extract content"""
        if not FILE_PROCESSING_AVAILABLE:
            return {"error": "File processing libraries not available"}

        try:
            result = {
                "file_type": file_type,
                "file_size": os.path.getsize(file_path),
                "content": "",
                "metadata": {},
                "summary": "",
                "pages": 0,
                "word_count": 0
            }

            if file_type == '.pdf':
                # Process PDF files
                with pdfplumber.open(file_path) as pdf:
                    result["pages"] = len(pdf.pages)
                    content = []
                    for page in pdf.pages:
                        page_text = page.extract_text()
                        if page_text:
                            content.append(page_text)

                    result["content"] = "\n".join(content)
                    result["word_count"] = len(result["content"].split())

                    # Extract metadata
                    with open(file_path, 'rb') as f:
                        pdf_reader = PdfReader(f)
                        if pdf_reader.metadata:
                            result["metadata"] = dict(pdf_reader.metadata)

            elif file_type == '.docx':
                # Process Word documents
                doc = docx.Document(file_path)
                content = []
                for paragraph in doc.paragraphs:
                    if paragraph.text.strip():
                        content.append(paragraph.text)

                result["content"] = "\n".join(content)
                result["word_count"] = len(result["content"].split())
                result["pages"] = len([p for p in doc.paragraphs if p.text.strip()])

            elif file_type == '.xlsx':
                # Process Excel files
                workbook = load_workbook(file_path)
                content = []
                for sheet_name in workbook.sheetnames:
                    sheet = workbook[sheet_name]
                    sheet_data = []
                    for row in sheet.iter_rows(values_only=True):
                        row_data = [str(cell) if cell is not None else "" for cell in row]
                        sheet_data.append("\t".join(row_data))
                    content.append(f"Sheet: {sheet_name}\n" + "\n".join(sheet_data))

                result["content"] = "\n\n".join(content)
                result["sheets"] = len(workbook.sheetnames)

            elif file_type == '.pptx':
                # Process PowerPoint files
                presentation = pptx.Presentation(file_path)
                content = []
                for i, slide in enumerate(presentation.slides):
                    slide_content = []
                    for shape in slide.shapes:
                        if hasattr(shape, "text") and shape.text.strip():
                            slide_content.append(shape.text)
                    if slide_content:
                        content.append(f"Slide {i+1}:\n" + "\n".join(slide_content))

                result["content"] = "\n\n".join(content)
                result["slides"] = len(presentation.slides)

            else:
                # Process text files
                with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                    result["content"] = f.read()
                    result["word_count"] = len(result["content"].split())

            # Generate summary
            if result["content"]:
                words = result["content"].split()
                if len(words) > 100:
                    result["summary"] = " ".join(words[:100]) + "..."
                else:
                    result["summary"] = result["content"]

            return result

        except Exception as e:
            self.logger.error(f"Document processing failed: {e}")
            return {"error": str(e)}

    def analyze_image(self, image_path: str) -> Dict[str, Any]:
        """Analyze image content and extract information"""
        if not FILE_PROCESSING_AVAILABLE:
            return {"error": "Image processing libraries not available"}

        try:
            result = {
                "image_path": image_path,
                "file_size": os.path.getsize(image_path),
                "dimensions": "",
                "format": "",
                "text_content": "",
                "objects_detected": [],
                "color_analysis": {},
                "enhancement_suggestions": []
            }

            # Open and analyze image
            with Image.open(image_path) as img:
                result["dimensions"] = f"{img.width}x{img.height}"
                result["format"] = img.format

                # Basic color analysis
                img_rgb = img.convert('RGB')
                pixels = list(img_rgb.getdata())
                total_pixels = len(pixels)

                if total_pixels > 0:
                    # Calculate average color
                    avg_color = [sum(c

	#!/bin/bash

	# ATOM Chat Interface - Phase 3 Complete Deployment
	# Advanced AI-Powered Conversation Intelligence, Multi-Modal Enhancement, and Voice Integration

	set -e # Exit on any error

	# Configuration
	SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
	PROJECT_ROOT="$SCRIPT_DIR"
	BACKEND_DIR="$PROJECT_ROOT/backend"
	LOG_DIR="$PROJECT_ROOT/logs"
	CONFIG_DIR="$PROJECT_ROOT/config"
	PHASE3_DIR="$PROJECT_ROOT/phase3_deployment"
	DEPLOYMENT_LOG="$LOG_DIR/phase3_deployment.log"

	# Colors for output
	RED='\033[0;31m'
	GREEN='\033[0;32m'
	YELLOW='\033[1;33m'
	BLUE='\033[0;34m'
	PURPLE='\033[0;35m'
	CYAN='\033[0;36m'
	NC='\033[0m' # No Color

	# Logging functions
	log_info() {
	echo -e "${BLUE}[INFO]${NC} $1" \| tee -a "$DEPLOYMENT_LOG"
	}

	log_success() {
	echo -e "${GREEN}[SUCCESS]${NC} $1" \| tee -a "$DEPLOYMENT_LOG"
	}

	log_warning() {
	echo -e "${YELLOW}[WARNING]${NC} $1" \| tee -a "$DEPLOYMENT_LOG"
	}

	log_error() {
	echo -e "${RED}[ERROR]${NC} $1" \| tee -a "$DEPLOYMENT_LOG"
	}

	log_phase() {
	echo -e "${PURPLE}[PHASE 3]${NC} $1" \| tee -a "$DEPLOYMENT_LOG"
	}

	log_feature() {
	echo -e "${CYAN}[FEATURE]${NC} $1" \| tee -a "$DEPLOYMENT_LOG"
	}

	# Initialize deployment
	initialize_deployment() {
	log_phase "🚀 Starting Phase 3 Complete Deployment"
	log_info "Project Root: $PROJECT_ROOT"
	log_info "Backend Directory: $BACKEND_DIR"
	log_info "Log Directory: $LOG_DIR"

	# Create necessary directories
	mkdir -p "$LOG_DIR"
	mkdir -p "$PHASE3_DIR"
	mkdir -p "$BACKEND_DIR/phase3_modules"

	# Clear previous deployment log
	> "$DEPLOYMENT_LOG"

	log_success "Deployment environment initialized"
	}

	# Check prerequisites
	check_prerequisites() {
	log_phase "🔍 Checking prerequisites for Phase 3 deployment..."

	# Check Python
	if command -v python3 &> /dev/null; then
	PYTHON_VERSION=$(python3 --version \| cut -d' ' -f2)
	log_success "Python $PYTHON_VERSION found"
	else
	log_error "Python 3 is required but not installed"
	exit 1
	fi

	# Check pip
	if command -v pip3 &> /dev/null; then
	log_success "pip3 found"
	else
	log_error "pip3 is required but not installed"
	exit 1
	fi

	# Check if backend directory exists
	if [ ! -d "$BACKEND_DIR" ]; then
	log_error "Backend directory not found: $BACKEND_DIR"
	exit 1
	fi

	# Check if current chat interface is running
	if curl -s http://localhost:8000/health > /dev/null; then
	log_success "Current Chat Interface Server is running"
	else
	log_warning "Current Chat Interface Server is not running - will deploy new instance"
	fi

	log_success "All prerequisites satisfied"
	}

	# Install Phase 3 advanced dependencies
	install_phase3_dependencies() {
	log_phase "📦 Installing Phase 3 Advanced Dependencies..."

	cd "$BACKEND_DIR"

	log_feature "Installing AI/ML libraries for conversation intelligence..."
	pip3 install transformers torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
	pip3 install sentence-transformers spacy scikit-learn
	pip3 install nltk textblob vaderSentiment

	# Download spaCy model
	log_feature "Downloading spaCy language model..."
	python3 -m spacy download en_core_web_sm

	log_feature "Installing advanced file processing libraries..."
	pip3 install PyPDF2 python-docx openpyxl pptx pdfplumber
	pip3 install Pillow opencv-python-headless pytesseract

	log_feature "Installing advanced voice processing libraries..."
	pip3 install speechrecognition pydub librosa soundfile
	pip3 install gtts pyttsx3 wave audioop

	log_feature "Installing analytics and monitoring libraries..."
	pip3 install prometheus-client matplotlib seaborn plotly
	pip3 install pandas numpy scipy

	log_success "Phase 3 dependencies installed successfully"
	}

	# Deploy AI Conversation Intelligence Module
	deploy_ai_intelligence() {
	log_phase "🧠 Deploying AI Conversation Intelligence Module..."

	cd "$BACKEND_DIR"

	# Create AI conversation intelligence module
	cat > "ai_conversation_intelligence.py" << 'EOF'
	"""
	AI-Powered Conversation Intelligence Module
	Advanced NLU, sentiment analysis, and context-aware response generation

	Author: Atom Platform Engineering
	Date: November 9, 2025
	Version: 3.0.0
	"""

	import logging
	from typing import Dict, List, Optional, Any
	import json
	from datetime import datetime

	# Import AI/ML libraries
	try:
	from transformers import pipeline, AutoTokenizer, AutoModel
	import torch
	import spacy
	from sentence_transformers import SentenceTransformer
	from textblob import TextBlob
	from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
	AI_AVAILABLE = True
	except ImportError as e:
	logging.warning(f"AI libraries not available: {e}")
	AI_AVAILABLE = False

	class ConversationIntelligence:
	"""Advanced AI-powered conversation intelligence"""

	def __init__(self):
	self.logger = logging.getLogger(__name__)
	self.nlp = None
	self.sentiment_analyzer = None
	self.sentence_model = None
	self.intent_classifier = None

	if AI_AVAILABLE:
	self.initialize_ai_models()

	def initialize_ai_models(self):
	"""Initialize AI models for conversation intelligence"""
	try:
	# Load spaCy model for NLP
	self.nlp = spacy.load("en_core_web_sm")

	# Initialize sentiment analyzer
	self.sentiment_analyzer = SentimentIntensityAnalyzer()

	# Load sentence transformer for semantic similarity
	self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')

	# Initialize intent classifier
	self.intent_classifier = pipeline(
	"text-classification",
	model="joeddav/distilbert-base-uncased-go-emotions-student",
	return_all_scores=True
	)

	self.logger.info("AI conversation intelligence models initialized")

	except Exception as e:
	self.logger.error(f"Failed to initialize AI models: {e}")

	def analyze_sentiment(self, text: str) -> Dict[str, float]:
	"""Analyze sentiment of text with multiple methods"""
	if not AI_AVAILABLE:
	return {"compound": 0.0, "positive": 0.0, "negative": 0.0, "neutral": 1.0}

	try:
	# VADER sentiment analysis
	vader_scores = self.sentiment_analyzer.polarity_scores(text)

	# TextBlob sentiment analysis
	blob = TextBlob(text)
	blob_sentiment = blob.sentiment

	# Combined sentiment score
	combined_score = {
	"vader_compound": vader_scores['compound'],
	"vader_positive": vader_scores['pos'],
	"vader_negative": vader_scores['neg'],
	"vader_neutral": vader_scores['neu'],
	"textblob_polarity": blob_sentiment.polarity,
	"textblob_subjectivity": blob_sentiment.subjectivity,
	"overall_sentiment": (vader_scores['compound'] + blob_sentiment.polarity) / 2
	}

	return combined_score

	except Exception as e:
	self.logger.error(f"Sentiment analysis failed: {e}")
	return {"error": str(e)}

	def classify_intent(self, text: str) -> List[Dict[str, Any]]:
	"""Classify user intent from text"""
	if not AI_AVAILABLE or not self.intent_classifier:
	return [{"label": "neutral", "score": 1.0}]

	try:
	results = self.intent_classifier(text)
	# Return top 3 intents
	top_intents = sorted(results[0], key=lambda x: x['score'], reverse=True)[:3]
	return top_intents

	except Exception as e:
	self.logger.error(f"Intent classification failed: {e}")
	return [{"label": "error", "score": 1.0}]

	def extract_entities(self, text: str) -> List[Dict[str, str]]:
	"""Extract named entities from text"""
	if not AI_AVAILABLE or not self.nlp:
	return []

	try:
	doc = self.nlp(text)
	entities = []

	for ent in doc.ents:
	entities.append({
	"text": ent.text,
	"label": ent.label_,
	"start": ent.start_char,
	"end": ent.end_char
	})

	return entities

	except Exception as e:
	self.logger.error(f"Entity extraction failed: {e}")
	return []

	def calculate_semantic_similarity(self, text1: str, text2: str) -> float:
	"""Calculate semantic similarity between two texts"""
	if not AI_AVAILABLE or not self.sentence_model:
	return 0.0

	try:
	embeddings = self.sentence_model.encode([text1, text2])
	similarity = torch.nn.functional.cosine_similarity(
	torch.tensor(embeddings[0]).unsqueeze(0),
	torch.tensor(embeddings[1]).unsqueeze(0)
	)
	return similarity.item()

	except Exception as e:
	self.logger.error(f"Semantic similarity calculation failed: {e}")
	return 0.0

	def generate_context_aware_response(self, message: str, conversation_history: List[Dict]) -> Dict[str, Any]:
	"""Generate context-aware response using conversation history"""
	analysis = {
	"timestamp": datetime.now().isoformat(),
	"message": message,
	"sentiment": self.analyze_sentiment(message),
	"intent": self.classify_intent(message),
	"entities": self.extract_entities(message),
	"context_relevance": 0.0,
	"suggested_actions": []
	}

	# Calculate context relevance with previous messages
	if conversation_history:
	recent_messages = conversation_history[-3:] # Last 3 messages
	relevance_scores = []

	for prev_msg in recent_messages:
	if 'content' in prev_msg:
	similarity = self.calculate_semantic_similarity(message, prev_msg['content'])
	relevance_scores.append(similarity)

	if relevance_scores:
	analysis["context_relevance"] = sum(relevance_scores) / len(relevance_scores)

	# Generate suggested actions based on analysis
	if analysis["sentiment"].get("overall_sentiment", 0) < -0.3:
	analysis["suggested_actions"].append("escalate_to_human")
	analysis["suggested_actions"].append("offer_apology")

	if any(entity["label"] in ["PERSON", "ORG"] for entity in analysis["entities"]):
	analysis["suggested_actions"].append("personalize_response")

	return analysis

	# Global instance
	conversation_intelligence = ConversationIntelligence()
	EOF

	log_success "AI Conversation Intelligence module deployed"
	}

	# Deploy Multi-Modal Enhancement Module
	deploy_multi_modal_enhancement() {
	log_phase "📁 Deploying Multi-Modal Enhancement Module..."

	cd "$BACKEND_DIR"

	# Create multi-modal enhancement module
	cat > "multi_modal_enhancement.py" << 'EOF'
	"""
	Multi-Modal Experience Enhancement Module
	Advanced file processing, image analysis, and rich media responses

	Author: Atom Platform Engineering
	Date: November 9, 2025
	Version: 3.0.0
	"""

	import logging
	import os
	from typing import Dict, List, Optional, Any
	import json
	from pathlib import Path

	# Import file processing libraries
	try:
	from PyPDF2 import PdfReader
	import docx
	from openpyxl import load_workbook
	import pptx
	import pdfplumber
	from PIL import Image, ImageEnhance, ImageFilter
	import cv2
	import pytesseract
	FILE_PROCESSING_AVAILABLE = True
	except ImportError as e:
	logging.warning(f"File processing libraries not available: {e}")
	FILE_PROCESSING_AVAILABLE = False

	class MultiModalEnhancement:
	"""Advanced multi-modal chat experience enhancement"""

	def __init__(self):
	self.logger = logging.getLogger(__name__)
	self.supported_formats = {
	'documents': ['.pdf', '.docx', '.xlsx', '.pptx', '.txt', '.csv'],
	'images': ['.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff'],
	'audio': ['.mp3', '.wav', '.ogg', '.m4a'],
	'video': ['.mp4', '.avi', '.mov', '.wmv']
	}

	def process_document(self, file_path: str, file_type: str) -> Dict[str, Any]:
	"""Process various document types and extract content"""
	if not FILE_PROCESSING_AVAILABLE:
	return {"error": "File processing libraries not available"}

	try:
	result = {
	"file_type": file_type,
	"file_size": os.path.getsize(file_path),
	"content": "",
	"metadata": {},
	"summary": "",
	"pages": 0,
	"word_count": 0
	}

	if file_type == '.pdf':
	# Process PDF files
	with pdfplumber.open(file_path) as pdf:
	result["pages"] = len(pdf.pages)
	content = []
	for page in pdf.pages:
	page_text = page.extract_text()
	if page_text:
	content.append(page_text)

	result["content"] = "\n".join(content)
	result["word_count"] = len(result["content"].split())

	# Extract metadata
	with open(file_path, 'rb') as f:
	pdf_reader = PdfReader(f)
	if pdf_reader.metadata:
	result["metadata"] = dict(pdf_reader.metadata)

	elif file_type == '.docx':
	# Process Word documents
	doc = docx.Document(file_path)
	content = []
	for paragraph in doc.paragraphs:
	if paragraph.text.strip():
	content.append(paragraph.text)

	result["content"] = "\n".join(content)
	result["word_count"] = len(result["content"].split())
	result["pages"] = len([p for p in doc.paragraphs if p.text.strip()])

	elif file_type == '.xlsx':
	# Process Excel files
	workbook = load_workbook(file_path)
	content = []
	for sheet_name in workbook.sheetnames:
	sheet = workbook[sheet_name]
	sheet_data = []
	for row in sheet.iter_rows(values_only=True):
	row_data = [str(cell) if cell is not None else "" for cell in row]
	sheet_data.append("\t".join(row_data))
	content.append(f"Sheet: {sheet_name}\n" + "\n".join(sheet_data))

	result["content"] = "\n\n".join(content)
	result["sheets"] = len(workbook.sheetnames)

	elif file_type == '.pptx':
	# Process PowerPoint files
	presentation = pptx.Presentation(file_path)
	content = []
	for i, slide in enumerate(presentation.slides):
	slide_content = []
	for shape in slide.shapes:
	if hasattr(shape, "text") and shape.text.strip():
	slide_content.append(shape.text)
	if slide_content:
	content.append(f"Slide {i+1}:\n" + "\n".join(slide_content))

	result["content"] = "\n\n".join(content)
	result["slides"] = len(presentation.slides)

	else:
	# Process text files
	with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
	result["content"] = f.read()
	result["word_count"] = len(result["content"].split())

	# Generate summary
	if result["content"]:
	words = result["content"].split()
	if len(words) > 100:
	result["summary"] = " ".join(words[:100]) + "..."
	else:
	result["summary"] = result["content"]

	return result

	except Exception as e:
	self.logger.error(f"Document processing failed: {e}")
	return {"error": str(e)}

	def analyze_image(self, image_path: str) -> Dict[str, Any]:
	"""Analyze image content and extract information"""
	if not FILE_PROCESSING_AVAILABLE:
	return {"error": "Image processing libraries not available"}

	try:
	result = {
	"image_path": image_path,
	"file_size": os.path.getsize(image_path),
	"dimensions": "",
	"format": "",
	"text_content": "",
	"objects_detected": [],
	"color_analysis": {},
	"enhancement_suggestions": []
	}

	# Open and analyze image
	with Image.open(image_path) as img:
	result["dimensions"] = f"{img.width}x{img.height}"
	result["format"] = img.format

	# Basic color analysis
	img_rgb = img.convert('RGB')
	pixels = list(img_rgb.getdata())
	total_pixels = len(pixels)

	if total_pixels > 0:
	# Calculate average color
	avg_color = [sum(c