#!/bin/bash
# FSS-Mini-RAG Installation Script
# Interactive installer that sets up Python environment and dependencies

set -e  # Exit on any error

# Check for command line arguments
HEADLESS_MODE=false
if [[ "$1" == "--headless" ]]; then
    HEADLESS_MODE=true
    echo "🤖 Running in headless mode - using defaults for automation"
elif [[ "$1" == "--help" || "$1" == "-h" ]]; then
    echo ""
    echo "FSS-Mini-RAG Installation Script"
    echo ""
    echo "Usage:"
    echo "  ./install_mini_rag.sh           # Interactive installation"
    echo "  ./install_mini_rag.sh --headless  # Automated installation for agents/CI"
    echo "  ./install_mini_rag.sh --help      # Show this help"
    echo ""
    echo "Headless mode options:"
    echo "  • Uses existing virtual environment if available"
    echo "  • Selects light installation (Ollama + basic dependencies)"  
    echo "  • Downloads nomic-embed-text model if Ollama is available"
    echo "  • Skips interactive prompts and tests"
    echo "  • Perfect for agent automation and CI/CD pipelines"
    echo ""
    exit 0
fi

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

# Get script directory
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"

# Print colored output
print_header() {
    echo -e "\n${CYAN}${BOLD}=== $1 ===${NC}"
}

print_success() {
    echo -e "${GREEN}✅ $1${NC}"
}

print_warning() {
    echo -e "${YELLOW}⚠️  $1${NC}"
}

print_error() {
    echo -e "${RED}❌ $1${NC}"
}

print_info() {
    echo -e "${BLUE}ℹ️  $1${NC}"
}

# Check if command exists
command_exists() {
    command -v "$1" >/dev/null 2>&1
}

# Check Python version
check_python() {
    print_header "Checking Python Installation"
    
    # Check for python3 first, then python
    local python_cmd=""
    if command_exists python3; then
        python_cmd="python3"
    elif command_exists python; then
        python_cmd="python"
    else
        print_error "Python not found!"
        echo -e "${YELLOW}Please install Python 3.8+ from:${NC}"
        echo "  • https://python.org/downloads"
        echo "  • Or use your system package manager:"
        echo "    - Ubuntu/Debian: sudo apt install python3 python3-pip python3-venv"
        echo "    - macOS: brew install python"
        echo "    - Windows: Download from python.org"
        echo ""
        echo -e "${CYAN}After installing Python, run this script again.${NC}"
        exit 1
    fi
    
    # Check Python version
    local python_version=$($python_cmd -c "import sys; print('.'.join(map(str, sys.version_info[:2])))")
    local major=$(echo $python_version | cut -d. -f1)
    local minor=$(echo $python_version | cut -d. -f2)
    
    if [ "$major" -lt 3 ] || ([ "$major" -eq 3 ] && [ "$minor" -lt 8 ]); then
        print_error "Python $python_version found, but 3.8+ required"
        echo "Please upgrade Python to 3.8 or higher."
        exit 1
    fi
    
    print_success "Found Python $python_version ($python_cmd)"
    export PYTHON_CMD="$python_cmd"
}

# Check if virtual environment exists
check_venv() {
    if [ -d "$SCRIPT_DIR/.venv" ]; then
        print_info "Virtual environment already exists at $SCRIPT_DIR/.venv"
        if [[ "$HEADLESS_MODE" == "true" ]]; then
            print_info "Headless mode: Using existing virtual environment"
            return 0  # Use existing
        else
            echo -n "Recreate it? (y/N): "
            read -r recreate
            if [[ $recreate =~ ^[Yy]$ ]]; then
                print_info "Removing existing virtual environment..."
                rm -rf "$SCRIPT_DIR/.venv"
                return 1  # Needs creation
            else
                return 0  # Use existing
            fi
        fi
    else
        return 1  # Needs creation
    fi
}

# Create virtual environment
create_venv() {
    print_header "Creating Python Virtual Environment"
    
    if ! check_venv; then
        print_info "Creating virtual environment at $SCRIPT_DIR/.venv"
        $PYTHON_CMD -m venv "$SCRIPT_DIR/.venv"
        
        if [ $? -ne 0 ]; then
            print_error "Failed to create virtual environment"
            echo "This might be because python3-venv is not installed."
            echo "Try: sudo apt install python3-venv (Ubuntu/Debian)"
            exit 1
        fi
        
        print_success "Virtual environment created"
    else
        print_success "Using existing virtual environment"
    fi
    
    # Activate virtual environment
    source "$SCRIPT_DIR/.venv/bin/activate"
    print_success "Virtual environment activated"
    
    # Upgrade pip
    print_info "Upgrading pip..."
    pip install --upgrade pip >/dev/null 2>&1
}

# Check Ollama installation
check_ollama() {
    print_header "Checking Ollama (AI Model Server)"
    
    if command_exists ollama; then
        print_success "Ollama is installed"
        
        # Check if Ollama is running
        if curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
            print_success "Ollama server is running"
            return 0
        else
            print_warning "Ollama is installed but not running"
            if [[ "$HEADLESS_MODE" == "true" ]]; then
                print_info "Headless mode: Starting Ollama server automatically"
                start_ollama="y"
            else
                echo -n "Start Ollama now? (Y/n): "
                read -r start_ollama
            fi
            if [[ ! $start_ollama =~ ^[Nn]$ ]]; then
                print_info "Starting Ollama server..."
                ollama serve &
                sleep 3
                if curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
                    print_success "Ollama server started"
                    return 0
                else
                    print_warning "Failed to start Ollama automatically"
                    echo "Please start Ollama manually: ollama serve"
                    return 1
                fi
            else
                return 1
            fi
        fi
    else
        print_warning "Ollama not found"
        echo ""
        echo -e "${CYAN}Ollama provides the best embedding quality and performance.${NC}"
        echo ""
        echo -e "${BOLD}Options:${NC}"
        echo -e "${GREEN}1) Install Ollama automatically${NC} (recommended)"
        echo -e "${YELLOW}2) Manual installation${NC} - Visit https://ollama.com/download"
        echo -e "${BLUE}3) Continue without Ollama${NC} (uses ML fallback)"
        echo ""
        if [[ "$HEADLESS_MODE" == "true" ]]; then
            print_info "Headless mode: Continuing without Ollama (option 3)"
            ollama_choice="3"
        else
            echo -n "Choose [1/2/3]: "
            read -r ollama_choice
        fi
        
        case "$ollama_choice" in
            1|"")
                print_info "Installing Ollama using secure installation method..."
                echo -e "${CYAN}Downloading and verifying Ollama installer...${NC}"
                
                # Secure installation: download, verify, then execute
                local temp_script="/tmp/ollama-install-$$.sh"
                if curl -fsSL https://ollama.com/install.sh -o "$temp_script" && \
                   file "$temp_script" | grep -q "shell script" && \
                   chmod +x "$temp_script" && \
                   "$temp_script"; then
                    rm -f "$temp_script"
                    print_success "Ollama installed successfully"
                    
                    print_info "Starting Ollama server..."
                    ollama serve &
                    sleep 3
                    
                    if curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
                        print_success "Ollama server started"
                        
                        echo ""
                        echo -e "${CYAN}💡 Pro tip: Download an LLM for AI-powered search synthesis!${NC}"
                        echo -e "   Lightweight: ${GREEN}ollama pull qwen3:0.6b${NC} (~500MB, very fast)"
                        echo -e "   Balanced:    ${GREEN}ollama pull qwen3:1.7b${NC} (~1.4GB, good quality)" 
                        echo -e "   Excellent:   ${GREEN}ollama pull qwen3:4b${NC} (~2.5GB, sweet spot for most users)"
                        echo -e "   Maximum:     ${GREEN}ollama pull qwen3:8b${NC} (~5GB, slower but top quality)"
                        echo ""
                        echo -e "${BLUE}🧠 RAG works great with smaller models! 4B is usually perfect.${NC}"
                        echo -e "${BLUE}Creative possibilities: Try mistral for storytelling, qwen2.5-coder for development!${NC}"
                        echo ""
                        
                        return 0
                    else
                        print_warning "Ollama installed but failed to start automatically"
                        echo "Please start Ollama manually: ollama serve"
                        echo "Then re-run this installer"
                        exit 1
                    fi
                else
                    print_error "Failed to install Ollama automatically"
                    echo "Please install manually from https://ollama.com/download"
                    exit 1
                fi
                ;;
            2)
                echo ""
                echo -e "${YELLOW}Manual Ollama installation:${NC}"
                echo "  1. Visit: https://ollama.com/download" 
                echo "  2. Download and install for your system"
                echo "  3. Run: ollama serve"
                echo "  4. Re-run this installer"
                print_info "Exiting for manual installation..."
                exit 0
                ;;
            3)
                print_info "Continuing without Ollama (will use ML fallback)"
                return 1
                ;;
            *)
                print_warning "Invalid choice, continuing without Ollama"
                return 1
                ;;
        esac
    fi
}

# Setup Ollama model based on configuration
setup_ollama_model() {
    # Skip if custom config says to skip
    if [ "$CUSTOM_OLLAMA_MODEL" = "skip" ]; then
        print_info "Skipping Ollama model setup (custom configuration)"
        return 1
    fi
    
    print_header "Ollama Model Setup"
    
    print_info "Checking available Ollama models..."
    
    # Get list of installed models
    local available_models=$(ollama list 2>/dev/null | grep -v "NAME" | awk '{print $1}' | grep -v "^$")
    
    if echo "$available_models" | grep -q "nomic-embed-text"; then
        print_success "nomic-embed-text model already installed"
        local model_info=$(ollama list | grep "nomic-embed-text")
        echo -e "${BLUE}• $model_info${NC}"
        return 0
    fi
    
    if [ -n "$available_models" ]; then
        print_info "Other Ollama models found:"
        echo "$available_models" | sed 's/^/  • /'
        echo ""
    fi
    
    # For custom installations, we already asked. For auto installations, ask now
    local should_download="$CUSTOM_OLLAMA_MODEL"
    if [ -z "$should_download" ] || [ "$should_download" = "auto" ]; then
        echo -e "${CYAN}Model: nomic-embed-text (~270MB)${NC}"
        echo "  • Purpose: High-quality semantic embeddings"
        echo "  • Alternative: System will use ML/hash fallbacks"
        echo ""
        if [[ "$HEADLESS_MODE" == "true" ]]; then
            print_info "Headless mode: Downloading nomic-embed-text model"
            download_model="y"
        else
            echo -n "Download model? [y/N]: "
            read -r download_model
        fi
        should_download=$([ "$download_model" = "y" ] && echo "download" || echo "skip")
    fi
    
    if [ "$should_download" != "download" ]; then
        print_info "Skipping model download"
        echo "  Install later: ollama pull nomic-embed-text"
        return 1
    fi
    
    # Test connectivity and download
    print_info "Testing Ollama connection..."
    if ! curl -s --connect-timeout 5 http://localhost:11434/api/version >/dev/null; then
        print_error "Cannot connect to Ollama server"
        echo "  Ensure Ollama is running: ollama serve"
        echo "  Then install manually: ollama pull nomic-embed-text"
        return 1
    fi
    
    print_info "Downloading nomic-embed-text..."
    echo -e "${BLUE}  Press Ctrl+C to cancel if needed${NC}"
    
    if ollama pull nomic-embed-text; then
        print_success "Model ready"
        return 0
    else
        print_warning "Download failed - will use fallback embeddings"
        return 1
    fi
}

# Get installation preferences with smart defaults
get_installation_preferences() {
    print_header "Installation Configuration"
    
    echo -e "${CYAN}FSS-Mini-RAG can run with different embedding backends:${NC}"
    echo ""
    echo -e "${GREEN}• Ollama${NC} (recommended) - Best quality, local AI server"
    echo -e "${YELLOW}• ML Fallback${NC} - Offline transformers, larger but always works"  
    echo -e "${BLUE}• Hash-based${NC} - Lightweight fallback, basic similarity"
    echo ""
    
    # Smart recommendation based on detected setup
    local recommended=""
    if [ "$ollama_available" = true ]; then
        recommended="light (Ollama detected)"
        echo -e "${GREEN}✓ Ollama detected - light installation recommended${NC}"
    else
        recommended="full (no Ollama)"
        echo -e "${YELLOW}⚠ No Ollama - full installation recommended for better quality${NC}"
    fi
    
    echo ""
    echo -e "${BOLD}Installation options:${NC}"
    echo -e "${GREEN}L) Light${NC} - Ollama + basic deps (~50MB) ${CYAN}← Best performance + AI chat${NC}"
    echo -e "${YELLOW}F) Full${NC}  - Light + ML fallback (~2-3GB) ${CYAN}← RAG-only if no Ollama${NC}"
    echo -e "${BLUE}C) Custom${NC} - Configure individual components"
    echo ""
    
    while true; do
        if [[ "$HEADLESS_MODE" == "true" ]]; then
            # Default to light installation in headless mode
            choice="L"
            print_info "Headless mode: Selected Light installation"
        else
            echo -n "Choose [L/F/C] or Enter for recommended ($recommended): "
            read -r choice
            
            # Default to recommendation if empty
            if [ -z "$choice" ]; then
                if [ "$ollama_available" = true ]; then
                    choice="L"
                else
                    choice="F"  
                fi
            fi
        fi
        
        case "${choice^^}" in
            L)
                export INSTALL_TYPE="light"
                echo -e "${GREEN}Selected: Light installation${NC}"
                break
                ;;
            F)
                export INSTALL_TYPE="full"
                echo -e "${YELLOW}Selected: Full installation${NC}"
                break
                ;;
            C)
                configure_custom_installation
                break
                ;;
            *)
                print_warning "Please choose L, F, C, or press Enter for default"
                ;;
        esac
    done
}

# Custom installation configuration
configure_custom_installation() {
    print_header "Custom Installation Configuration"
    
    echo -e "${CYAN}Configure each component individually:${NC}"
    echo ""
    
    # Base dependencies (always required)
    echo -e "${GREEN}✓ Base dependencies${NC} (lancedb, pandas, numpy, etc.) - Required"
    
    # Ollama model
    local ollama_model="skip"
    if [ "$ollama_available" = true ]; then
        echo ""
        echo -e "${BOLD}Ollama embedding model:${NC}"
        echo "  • nomic-embed-text (~270MB) - Best quality embeddings"
        if [[ "$HEADLESS_MODE" == "true" ]]; then
            print_info "Headless mode: Downloading Ollama model"
            download_ollama="y"
        else
            echo -n "Download Ollama model? [y/N]: "
            read -r download_ollama
        fi
        if [[ $download_ollama =~ ^[Yy]$ ]]; then
            ollama_model="download"
        fi
    fi
    
    # ML dependencies
    echo ""
    echo -e "${BOLD}ML fallback system:${NC}"
    echo "  • PyTorch + transformers (~2-3GB) - Works without Ollama"
    echo "  • Useful for: Offline use, server deployments, CI/CD"
    if [[ "$HEADLESS_MODE" == "true" ]]; then
        print_info "Headless mode: Skipping ML dependencies (keeping light)"
        include_ml="n"
    else
        echo -n "Include ML dependencies? [y/N]: "
        read -r include_ml
    fi
    
    # Pre-download models
    local predownload_ml="skip"
    if [[ $include_ml =~ ^[Yy]$ ]]; then
        echo ""
        echo -e "${BOLD}Pre-download ML models:${NC}"
        echo "  • sentence-transformers model (~80MB)"
        echo "  • Skip: Models download automatically when first used"
        if [[ "$HEADLESS_MODE" == "true" ]]; then
            print_info "Headless mode: Skipping ML model pre-download"
            predownload="n"
        else
            echo -n "Pre-download now? [y/N]: "
            read -r predownload
        fi
        if [[ $predownload =~ ^[Yy]$ ]]; then
            predownload_ml="download"
        fi
    fi
    
    # Set configuration
    if [[ $include_ml =~ ^[Yy]$ ]]; then
        export INSTALL_TYPE="full"
    else
        export INSTALL_TYPE="light"
    fi
    export CUSTOM_OLLAMA_MODEL="$ollama_model"
    export CUSTOM_ML_PREDOWNLOAD="$predownload_ml"
    
    echo ""
    echo -e "${GREEN}Custom configuration set:${NC}"
    echo "  • Base deps: ✓"
    echo "  • Ollama model: $ollama_model"
    echo "  • ML deps: $([ "$INSTALL_TYPE" = "full" ] && echo "✓" || echo "skip")"
    echo "  • ML predownload: $predownload_ml"
}

# Install dependencies with progress
install_dependencies() {
    print_header "Installing Python Dependencies"
    
    if [ "$INSTALL_TYPE" = "light" ]; then
        print_info "Installing core dependencies (~50MB)..."
        echo -e "${BLUE}  Installing: lancedb, pandas, numpy, PyYAML, etc.${NC}"
        
        if pip install -r "$SCRIPT_DIR/requirements.txt" --quiet; then
            print_success "Dependencies installed"
        else
            print_error "Failed to install dependencies"
            echo "Try: pip install -r requirements.txt"
            exit 1
        fi
    else
        print_info "Installing full dependencies (~2-3GB)..."
        echo -e "${YELLOW}  This includes PyTorch and transformers - will take several minutes${NC}"
        echo -e "${BLUE}  Progress will be shown...${NC}"
        
        if pip install -r "$SCRIPT_DIR/requirements-full.txt"; then
            print_success "All dependencies installed"
        else
            print_error "Failed to install dependencies"
            echo "Try: pip install -r requirements-full.txt"
            exit 1
        fi
    fi
    
    print_info "Verifying installation..."
    if python3 -c "import lancedb, pandas, numpy" 2>/dev/null; then
        print_success "Core packages verified"
    else
        print_error "Package verification failed"
        exit 1
    fi
}

# Setup application icon for desktop integration
setup_desktop_icon() {
    print_header "Setting Up Desktop Integration"
    
    # Check if we're in a GUI environment
    if [ -z "$DISPLAY" ] && [ -z "$WAYLAND_DISPLAY" ]; then
        print_info "No GUI environment detected - skipping desktop integration"
        return 0
    fi
    
    local icon_source="$SCRIPT_DIR/assets/Fss_Mini_Rag.png"
    local desktop_dir="$HOME/.local/share/applications"
    local icon_dir="$HOME/.local/share/icons"
    
    # Check if icon file exists
    if [ ! -f "$icon_source" ]; then
        print_warning "Icon file not found at $icon_source"
        return 1
    fi
    
    # Create directories if needed
    mkdir -p "$desktop_dir" "$icon_dir" 2>/dev/null
    
    # Copy icon to standard location
    local icon_dest="$icon_dir/fss-mini-rag.png"
    if cp "$icon_source" "$icon_dest" 2>/dev/null; then
        print_success "Icon installed to $icon_dest"
    else
        print_warning "Could not install icon (permissions?)"
        return 1
    fi
    
    # Create desktop entry
    local desktop_file="$desktop_dir/fss-mini-rag.desktop"
    cat > "$desktop_file" << EOF
[Desktop Entry]
Name=FSS-Mini-RAG
Comment=Fast Semantic Search for Code and Documents
Exec=$SCRIPT_DIR/rag-tui
Icon=fss-mini-rag
Terminal=true
Type=Application
Categories=Development;Utility;TextEditor;
Keywords=search;code;rag;semantic;ai;
StartupNotify=true
EOF
    
    if [ -f "$desktop_file" ]; then
        chmod +x "$desktop_file"
        print_success "Desktop entry created"
        
        # Update desktop database if available
        if command_exists update-desktop-database; then
            update-desktop-database "$desktop_dir" 2>/dev/null
            print_info "Desktop database updated"
        fi
        
        print_info "✨ FSS-Mini-RAG should now appear in your application menu!"
        print_info "   Look for it in Development or Utility categories"
    else
        print_warning "Could not create desktop entry"
        return 1
    fi
    
    return 0
}

# Setup ML models based on configuration  
setup_ml_models() {
    if [ "$INSTALL_TYPE" != "full" ]; then
        return 0
    fi
    
    # Check if we should pre-download
    local should_predownload="$CUSTOM_ML_PREDOWNLOAD"
    if [ -z "$should_predownload" ] || [ "$should_predownload" = "auto" ]; then
        print_header "ML Model Pre-download"
        echo -e "${CYAN}Pre-download ML models for offline use?${NC}"
        echo ""
        echo -e "${BLUE}Model: sentence-transformers/all-MiniLM-L6-v2 (~80MB)${NC}"
        echo "  • Purpose: Offline fallback when Ollama unavailable"
        echo "  • If skipped: Auto-downloads when first needed"
        echo ""
        if [[ "$HEADLESS_MODE" == "true" ]]; then
            print_info "Headless mode: Skipping ML model pre-download"
            download_ml="n"
        else
            echo -n "Pre-download now? [y/N]: "
            read -r download_ml
        fi
        should_predownload=$([ "$download_ml" = "y" ] && echo "download" || echo "skip")
    fi
    
    if [ "$should_predownload" != "download" ]; then
        print_info "Skipping ML model pre-download"
        echo "  Models will download automatically when first used"
        return 0
    fi
    
    print_info "Pre-downloading ML model..."
    echo -e "${BLUE}  This ensures offline availability${NC}"
    
    # Create a simple progress indicator
    python3 -c "
import sys
import threading
import time

# Progress spinner
def spinner():
    chars = '⠋⠙⠹⠸⠼⠴⠦⠧⠇⠏'
    while not spinner.stop:
        for char in chars:
            if spinner.stop:
                break
            sys.stdout.write(f'\r  {char} Downloading model...')
            sys.stdout.flush()
            time.sleep(0.1)

try:
    spinner.stop = False
    spinner_thread = threading.Thread(target=spinner)
    spinner_thread.start()
    
    from sentence_transformers import SentenceTransformer
    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
    
    spinner.stop = True
    spinner_thread.join()
    print('\r✅ ML model ready for offline use                ')
    
except Exception as e:
    spinner.stop = True
    spinner_thread.join()
    print(f'\r❌ Download failed: {e}                ')
    sys.exit(1)
    " 2>/dev/null
    
    if [ $? -eq 0 ]; then
        print_success "ML models ready"
    else
        print_warning "Pre-download failed"
        echo "  Models will auto-download when first needed"
    fi
}

# Test installation
test_installation() {
    print_header "Testing Installation"
    
    print_info "Testing basic functionality..."
    
    # Test import
    if python3 -c "from mini_rag import CodeEmbedder, ProjectIndexer, CodeSearcher; print('✅ Import successful')" 2>/dev/null; then
        print_success "Python imports working"
    else
        print_error "Import test failed"
        return 1
    fi
    
    # Test embedding system
    if python3 -c "
from mini_rag import CodeEmbedder
embedder = CodeEmbedder()
info = embedder.get_embedding_info()
print(f'✅ Embedding system: {info[\"method\"]}')
    " 2>/dev/null; then
        print_success "Embedding system working"
    else
        echo ""
        echo -e "${YELLOW}⚠️  System Check${NC}"
        
        # Smart diagnosis - check what's actually available
        if command_exists ollama && curl -s http://localhost:11434/api/version >/dev/null 2>&1; then
            # Ollama is running, check for models
            local available_models=$(ollama list 2>/dev/null | grep -E "(qwen3|llama|mistral|gemma)" | head -5)
            local embedding_models=$(ollama list 2>/dev/null | grep -E "(embed|bge)" | head -2)
            
            if [[ -n "$available_models" ]]; then
                echo -e "${GREEN}✅ Ollama is running with available models${NC}"
                echo -e "${CYAN}Your setup will work great! The system will auto-select the best models.${NC}"
                echo ""
                echo -e "${BLUE}💡 RAG Performance Tip:${NC} Smaller models often work better with RAG!"
                echo -e "   With context provided, even 0.6B models give good results"
                echo -e "   4B models = excellent, 8B+ = overkill (slower responses)"
            else
                echo -e "${BLUE}Ollama is running but no chat models found.${NC}"
                echo -e "Download a lightweight model: ${GREEN}ollama pull qwen3:0.6b${NC} (fast)"
                echo -e "Or balanced option: ${GREEN}ollama pull qwen3:4b${NC} (excellent quality)"
            fi
        else
            echo -e "${BLUE}Ollama not running or not installed.${NC}"
            echo -e "Start Ollama: ${GREEN}ollama serve${NC}"
            echo -e "Or install from: https://ollama.com/download"
        fi
        
        echo ""
        echo -e "${CYAN}✅ FSS-Mini-RAG will auto-detect and use the best available method.${NC}"
        echo ""
    fi
    
    return 0
}

# Show completion message
show_completion() {
    print_header "Installation Complete!"
    
    echo -e "${GREEN}${BOLD}FSS-Mini-RAG is now installed!${NC}"
    echo ""
    echo -e "${CYAN}Quick Start Options:${NC}"
    echo ""
    echo -e "${GREEN}🎯 TUI (Beginner-Friendly):${NC}"
    echo "     ./rag-tui"
    echo "     # Interactive interface with guided setup"
    echo ""
    echo -e "${BLUE}💻 CLI (Advanced):${NC}"
    echo "     ./rag-mini index /path/to/project"
    echo "     ./rag-mini search /path/to/project \"query\""
    echo "     ./rag-mini status /path/to/project"
    echo ""
    echo -e "${CYAN}Documentation:${NC}"
    echo "  • README.md - Complete technical documentation"
    echo "  • docs/GETTING_STARTED.md - Step-by-step guide"
    echo "  • examples/ - Usage examples and sample configs"
    echo ""
    
    if [ "$INSTALL_TYPE" = "light" ] && ! command_exists ollama; then
        echo -e "${YELLOW}Note: You chose light installation but Ollama isn't running.${NC}"
        echo "The system will use hash-based embeddings (lower quality)."
        echo "For best results, install Ollama from https://ollama.ai/download"
        echo ""
    fi
    
    # Ask if they want to run a test
    echo ""
    echo -e "${BOLD}🧪 Quick Test Available${NC}"
    echo -e "${CYAN}Test FSS-Mini-RAG with a small sample project (takes ~10 seconds)${NC}"
    echo ""
    
    # Ensure output is flushed and we're ready for input
    printf "Run quick test now? [Y/n]: "
    
    # More robust input handling
    if [[ "$HEADLESS_MODE" == "true" ]]; then
        print_info "Headless mode: Skipping interactive test"
        echo -e "${BLUE}You can test FSS-Mini-RAG anytime with: ./rag-tui${NC}"
        show_beginner_guidance
    elif read -r run_test < /dev/tty 2>/dev/null; then
        echo "User chose: '$run_test'"  # Debug output
        if [[ ! $run_test =~ ^[Nn]$ ]]; then
            run_quick_test
            echo ""
            show_beginner_guidance
        else
            echo -e "${BLUE}Skipping test - you can run it later with: ./rag-tui${NC}"
            show_beginner_guidance
        fi
    else
        # Fallback if interactive input fails
        echo ""
        echo -e "${YELLOW}⚠️  Interactive input not available - skipping test prompt${NC}"
        echo -e "${BLUE}You can test FSS-Mini-RAG anytime with: ./rag-tui${NC}"
        show_beginner_guidance
    fi
}

# Note: Sample project creation removed - now indexing real codebase/docs

# Run quick test with sample data
run_quick_test() {
    print_header "Quick Test"
    
    # Ask what to index: code vs docs
    echo -e "${CYAN}What would you like to explore with FSS-Mini-RAG?${NC}"
    echo ""
    echo -e "${GREEN}1) Code${NC} - Index the FSS-Mini-RAG codebase (~50 files)"
    echo -e "${BLUE}2) Docs${NC} - Index the documentation (~10 files)"  
    echo ""
    if [[ "$HEADLESS_MODE" == "true" ]]; then
        print_info "Headless mode: Indexing code by default"
        index_choice="1"
    else
        echo -n "Choose [1/2] or Enter for code: "
        read -r index_choice
    fi
    
    # Determine what to index
    local target_dir="$SCRIPT_DIR"
    local target_name="FSS-Mini-RAG codebase"
    if [[ "$index_choice" == "2" ]]; then
        target_dir="$SCRIPT_DIR/docs"
        target_name="FSS-Mini-RAG documentation"
    fi
    
    # Ensure we're in the right directory and have the right permissions
    if [[ ! -f "./rag-mini" ]]; then
        print_error "rag-mini script not found in current directory: $(pwd)"
        print_info "This might be a path issue. The installer should run from the project directory."
        return 1
    fi
    
    if [[ ! -x "./rag-mini" ]]; then
        print_info "Making rag-mini executable..."
        chmod +x ./rag-mini
    fi
    
    # Index the chosen target
    print_info "Indexing $target_name..."
    echo -e "${CYAN}This will take 10-30 seconds depending on your system${NC}"
    echo ""
    
    if ./rag-mini index "$target_dir"; then
        print_success "✅ Indexing completed successfully!"
        
        echo ""
        print_info "🎯 Launching Interactive Tutorial..."
        echo -e "${CYAN}The TUI has 6 sample questions to get you started.${NC}"
        echo -e "${CYAN}Try the suggested queries or enter your own!${NC}"
        echo ""
        if [[ "$HEADLESS_MODE" != "true" ]]; then
            echo -n "Press Enter to start interactive tutorial: "
            read -r
        fi
        
        # Launch the TUI which has the existing interactive tutorial system
        ./rag-tui.py "$target_dir" || true
        
        echo ""
        print_success "🎉 Tutorial completed!"
        echo -e "${CYAN}FSS-Mini-RAG is working perfectly!${NC}"
        
    else
        print_error "❌ Indexing failed"
        echo ""
        echo -e "${YELLOW}Possible causes:${NC}"
        echo "• Virtual environment not properly activated"
        echo "• Missing dependencies (try: pip install -r requirements.txt)"
        echo "• Path issues (ensure script runs from project directory)"
        echo "• Ollama connection issues (if using Ollama)"
        echo ""
        return 1
    fi
}

# Show beginner-friendly first steps
show_beginner_guidance() {
    print_header "Getting Started - Your First Search"
    
    echo -e "${CYAN}FSS-Mini-RAG is ready! Here's how to start:${NC}"
    echo ""
    echo -e "${GREEN}🎯 For Beginners (Recommended):${NC}"
    echo "   ./rag-tui"
    echo "   ↳ Interactive interface with sample questions"
    echo ""
    echo -e "${BLUE}💻 For Developers:${NC}"
    echo "   ./rag-mini index /path/to/your/project"
    echo "   ./rag-mini search /path/to/your/project \"your question\""
    echo ""
    echo -e "${YELLOW}📚 What can you search for in FSS-Mini-RAG?${NC}"
    echo "   • Technical: \"chunking strategy\", \"ollama integration\", \"indexing performance\""
    echo "   • Usage: \"how to improve search results\", \"why does indexing take long\""
    echo "   • Your own projects: any code, docs, emails, notes, research"
    echo ""
    echo -e "${CYAN}💡 Pro tip:${NC} You can drag ANY text-based documents into a folder"
    echo "   and search through them - emails, notes, research, chat logs!"
}

# Main installation flow
main() {
    echo -e "${CYAN}${BOLD}"
    echo "╔══════════════════════════════════════╗"
    echo "║        FSS-Mini-RAG Installer        ║"
    echo "║   Fast Semantic Search for Code      ║"
    echo "╚══════════════════════════════════════╝"
    echo -e "${NC}"
    
    echo -e "${BLUE}Adaptive installation process:${NC}"
    echo "  • Python environment setup"
    echo "  • Smart configuration based on your system"  
    echo "  • Optional AI model downloads (with consent)"
    echo "  • Testing and verification"
    echo ""
    echo -e "${CYAN}Note: You'll be asked before downloading any models${NC}"
    echo ""
    
    if [[ "$HEADLESS_MODE" == "true" ]]; then
        print_info "Headless mode: Beginning installation automatically"
    else
        echo -n "Begin installation? [Y/n]: "
        read -r continue_install
        if [[ $continue_install =~ ^[Nn]$ ]]; then
            echo "Installation cancelled."
            exit 0
        fi
    fi
    
    # Run installation steps
    check_python
    create_venv
    
    # Check Ollama availability
    ollama_available=false
    if check_ollama; then
        ollama_available=true
    fi
    
    # Get installation preferences with smart recommendations
    get_installation_preferences
    
    # Install dependencies
    install_dependencies
    
    # Setup models based on configuration
    if [ "$ollama_available" = true ]; then
        setup_ollama_model
    fi
    setup_ml_models
    
    # Setup desktop integration with icon
    setup_desktop_icon
    
    if test_installation; then
        show_completion
    else
        print_error "Installation test failed"
        echo "Please check error messages and try again."
        exit 1
    fi
}

# Run main function
main "$@"