mapmorph_agents/web_search_tools.py

"""Web search tools for MapMorph agents."""

import re
from typing import Dict, Any, List
from agents import function_tool

# Curated database of popular brand colors
# Format: brand_name: [primary_color, secondary_color, ...]
BRAND_COLOR_DATABASE = {
    # Tech companies
    "google": ["#4285F4", "#34A853", "#FBBC05", "#EA4335"],
    "facebook": ["#1877F2", "#F0F2F5"],
    "meta": ["#0668E1", "#F0F2F5"],
    "twitter": ["#1DA1F2", "#14171A", "#657786"],
    "x": ["#000000", "#FFFFFF"],
    "instagram": ["#E4405F", "#FCAF45", "#833AB4"],
    "linkedin": ["#0A66C2", "#313335"],
    "youtube": ["#FF0000", "#282828", "#FFFFFF"],
    "spotify": ["#1DB954", "#191414"],
    "apple": ["#000000", "#A6A6A6", "#F5F5F7"],
    "microsoft": ["#00A4EF", "#F25022", "#7FBA00", "#FFB900"],
    "amazon": ["#FF9900", "#146EB4"],
    "netflix": ["#E50914", "#000000"],
    "slack": ["#4A154B", "#36C5F0", "#2EB67D", "#ECB22E"],
    "zoom": ["#2D8CFF", "#FFFFFF"],
    "dropbox": ["#0061FF", "#1E1919"],
    "github": ["#24292E", "#FFFFFF"],
    "gitlab": ["#FC6D26", "#554488"],

    # Brands
    "nike": ["#000000", "#FFFFFF"],
    "adidas": ["#000000", "#FFFFFF"],
    "coca-cola": ["#F40009", "#FFFFFF"],
    "pepsi": ["#004B93", "#E32934", "#FFFFFF"],
    "starbucks": ["#00704A", "#FFFFFF"],
    "mcdonald's": ["#FFC72C", "#DA291C"],
    "mcdonals": ["#FFC72C", "#DA291C"],  # Common misspelling
    "subway": ["#00923F", "#FBAF3F"],
    "kfc": ["#E4002B", "#FFFFFF"],
    "ikea": ["#0051BA", "#FFCC00"],
    "walmart": ["#0071CE", "#FFC220"],
    "target": ["#CC0000", "#FFFFFF"],
    "lego": ["#F6EC35", "#D11013", "#000000"],

    # Automotive
    "ferrari": ["#DC0000", "#000000"],
    "lamborghini": ["#FFB81C", "#000000"],
    "bmw": ["#1C69D4", "#FFFFFF"],
    "audi": ["#BB0A30", "#000000"],
    "toyota": ["#EB0A1E", "#FFFFFF"],
    "tesla": ["#E82127", "#000000"],

    # Fashion & Luxury
    "gucci": ["#00693E", "#CE1126"],
    "louis vuitton": ["#5B401F", "#FFFFFF"],
    "chanel": ["#000000", "#FFFFFF"],
    "hermes": ["#FF7A00", "#000000"],
    "prada": ["#000000", "#FFFFFF"],

    # Airlines
    "delta": ["#003A70", "#C8102E"],
    "united": ["#0066CC", "#FFFFFF"],
    "american airlines": ["#0078D2", "#C8102E"],
    "southwest": ["#304CB2", "#FFBF27"],
}


@function_tool
def search_brand_colors(brand_name: str) -> Dict[str, Any]:
    """Search for a brand's official color palette.

    This tool searches a curated database of popular brand colors and returns
    hex color codes. Use this when users mention brand names and want to apply
    brand colors to the map.

    Examples of when to use this:
    - "Use Nike's brand colors"
    - "Apply Coca-Cola colors to the map"
    - "Make it look like the Spotify brand"
    - "Use Apple's colors for the water"

    Args:
        brand_name: Name of the brand to search for (e.g., "Nike", "Coca-Cola", "Spotify")

    Returns:
        Dictionary containing:
        - brand: Brand name
        - colors: List of hex color codes found
        - primary_color: Main brand color (if identifiable)
        - found: Whether the brand was found in the database
        - source: Information about where colors were found
    """
    # Normalize brand name for search
    normalized_name = brand_name.lower().strip()

    # Search in database
    if normalized_name in BRAND_COLOR_DATABASE:
        colors = BRAND_COLOR_DATABASE[normalized_name]
        return {
            "brand": brand_name,
            "colors": colors,
            "primary_color": colors[0] if colors else None,
            "found": True,
            "source": "Curated brand color database",
            "color_count": len(colors)
        }

    # Check for partial matches
    for db_brand, colors in BRAND_COLOR_DATABASE.items():
        if normalized_name in db_brand or db_brand in normalized_name:
            return {
                "brand": brand_name,
                "colors": colors,
                "primary_color": colors[0] if colors else None,
                "found": True,
                "source": f"Curated database (matched '{db_brand}')",
                "color_count": len(colors)
            }

    # Brand not found
    return {
        "brand": brand_name,
        "colors": [],
        "primary_color": None,
        "found": False,
        "source": "Not found in database",
        "suggestion": f"Brand '{brand_name}' not found. Try a different brand or specify colors directly."
    }


@function_tool
def extract_colors_from_text(text: str) -> List[str]:
    """Extract hex color codes from text.

    Use this helper tool to parse hex color codes from web search results,
    documentation, or user input.

    Args:
        text: Text containing hex color codes (e.g., "#FF5733, #C70039, #900C3F")

    Returns:
        List of valid hex color codes found in the text
    """
    # Regex pattern for hex colors: # followed by 3 or 6 hex digits
    hex_pattern = r'#([A-Fa-f0-9]{6}|[A-Fa-f0-9]{3})\b'

    matches = re.findall(hex_pattern, text)

    # Normalize 3-digit hex to 6-digit
    normalized = []
    for match in matches:
        if len(match) == 3:
            # Expand #RGB to #RRGGBB
            normalized.append(f"#{match[0]}{match[0]}{match[1]}{match[1]}{match[2]}{match[2]}")
        else:
            normalized.append(f"#{match}")

    # Remove duplicates while preserving order
    seen = set()
    unique_colors = []
    for color in normalized:
        color_upper = color.upper()
        if color_upper not in seen:
            seen.add(color_upper)
            unique_colors.append(color_upper)

    return unique_colors


@function_tool
def suggest_layer_mapping_for_brand(
    brand_colors: List[str],
    brand_name: str = ""
) -> Dict[str, str]:
    """Suggest which brand colors should be applied to which map layers.

    Use this tool after finding a brand's colors to intelligently map them
    to appropriate map layers based on color psychology and aesthetics.

    Args:
        brand_colors: List of hex color codes from the brand palette
        brand_name: Optional brand name for context

    Returns:
        Dictionary mapping layer IDs to suggested colors:
        {
            "water": "#HEXCODE",
            "earth": "#HEXCODE",
            "buildings": "#HEXCODE",
            ...
        }
    """
    if not brand_colors:
        return {"error": "No brand colors provided"}

    suggestions = {}

    # Analyze colors by their properties
    def color_brightness(hex_color: str) -> float:
        """Calculate perceived brightness (0-1) of a hex color."""
        hex_color = hex_color.lstrip('#')
        r, g, b = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
        # Perceived brightness formula
        return (0.299 * r + 0.587 * g + 0.114 * b) / 255

    def is_blue_ish(hex_color: str) -> bool:
        """Check if color is blue-ish."""
        hex_color = hex_color.lstrip('#')
        r, g, b = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
        return b > r and b > g

    def is_green_ish(hex_color: str) -> bool:
        """Check if color is green-ish."""
        hex_color = hex_color.lstrip('#')
        r, g, b = int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
        return g > r and g > b

    # Sort colors by brightness
    sorted_colors = sorted(brand_colors, key=color_brightness)
    darkest = sorted_colors[0]
    lightest = sorted_colors[-1]

    # Find blue-ish colors for water
    blue_colors = [c for c in brand_colors if is_blue_ish(c)]
    if blue_colors:
        suggestions["water"] = blue_colors[0]
    elif len(brand_colors) >= 2:
        # Use second color if no blue found
        suggestions["water"] = brand_colors[1] if len(brand_colors) > 1 else brand_colors[0]

    # Find green-ish colors for parks/nature
    green_colors = [c for c in brand_colors if is_green_ish(c)]
    if green_colors:
        suggestions["landuse"] = green_colors[0]

    # Use lightest color for earth/background
    suggestions["earth"] = lightest

    # Use darkest or primary color for buildings
    suggestions["buildings"] = darkest if len(brand_colors) > 2 else brand_colors[0]

    # Use light color for roads
    if color_brightness(lightest) > 0.7:
        suggestions["roads"] = lightest
    else:
        suggestions["roads"] = "#FFFFFF"  # Default to white if no light color

    return suggestions