KillerKing93/Transformers-InferenceServer-OpenAPI

1#!/usr/bin/env python2# -*- coding: utf-8 -*-3"""4Utility functions for AI Marketplace Platform5 6Functions:7- haversine_distance: Calculate distance between two coordinates8- sort_by_distance: Sort items by distance from user location9- extract_location_query: Parse location from natural language query10"""11 12import math13import re14from typing import List, Dict, Any, Optional, Tuple15 16 17def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:18    """19    Calculate the great circle distance between two points on Earth.20    Uses the Haversine formula.21 22    Args:23        lat1, lon1: Latitude and longitude of point 1 (degrees)24        lat2, lon2: Latitude and longitude of point 2 (degrees)25 26    Returns:27        Distance in kilometers28 29    Example:30        >>> distance = haversine_distance(-6.2088, 106.8456, -6.9175, 107.6191)31        >>> print(f"{distance:.2f} km")  # Jakarta to Bandung32        126.78 km33    """34    # Earth radius in kilometers35    R = 6371.036 37    # Convert degrees to radians38    lat1_rad = math.radians(lat1)39    lon1_rad = math.radians(lon1)40    lat2_rad = math.radians(lat2)41    lon2_rad = math.radians(lon2)42 43    # Differences44    dlat = lat2_rad - lat1_rad45    dlon = lon2_rad - lon1_rad46 47    # Haversine formula48    a = math.sin(dlat / 2)**2 + math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(dlon / 2)**249    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))50 51    distance = R * c52    return distance53 54 55def sort_by_distance(56    items: List[Dict[str, Any]],57    user_lat: float,58    user_lon: float,59    lat_key: str = "latitude",60    lon_key: str = "longitude"61) -> List[Dict[str, Any]]:62    """63    Sort a list of items by distance from user location.64    Adds 'distance_km' field to each item.65 66    Args:67        items: List of dictionaries containing location data68        user_lat: User's latitude69        user_lon: User's longitude70        lat_key: Key name for latitude in items (default: "latitude")71        lon_key: Key name for longitude in items (default: "longitude")72 73    Returns:74        Sorted list with distance_km added to each item75 76    Example:77        >>> suppliers = [78        ...     {"name": "Supplier A", "latitude": -6.2, "longitude": 106.8},79        ...     {"name": "Supplier B", "latitude": -6.9, "longitude": 107.6}80        ... ]81        >>> sorted_suppliers = sort_by_distance(suppliers, -6.2088, 106.8456)82        >>> print(sorted_suppliers[0]["distance_km"])83    """84    for item in items:85        if lat_key in item and lon_key in item:86            item["distance_km"] = haversine_distance(87                user_lat, user_lon,88                item[lat_key], item[lon_key]89            )90        else:91            item["distance_km"] = float('inf')  # Put items without location at the end92 93    # Sort by distance94    sorted_items = sorted(items, key=lambda x: x["distance_km"])95    return sorted_items96 97 98def extract_location_query(query: str) -> Optional[str]:99    """100    Extract city/location from natural language query.101 102    Args:103        query: Natural language query string104 105    Returns:106        Extracted location string or None107 108    Example:109        >>> extract_location_query("laptop gaming di Jakarta")110        'Jakarta'111        >>> extract_location_query("cari laptop Jakarta Selatan")112        'Jakarta Selatan'113    """114    # Common Indonesian location patterns115    patterns = [116        r'\b(?:di|dekat|sekitar|area)\s+([A-Z][a-zA-Z\s]+?)(?:\s|$|,)',117        r'\b([A-Z][a-zA-Z\s]+?)\s+(?:Selatan|Utara|Timur|Barat|Pusat)\b',118        r'\b(Jakarta|Bandung|Surabaya|Medan|Semarang|Makassar|Palembang|Tangerang|Depok|Bekasi|Bogor)\b'119    ]120 121    for pattern in patterns:122        match = re.search(pattern, query, re.IGNORECASE)123        if match:124            return match.group(1).strip()125 126    return None127 128 129def format_price_idr(price: float) -> str:130    """131    Format price as Indonesian Rupiah.132 133    Args:134        price: Price in Rupiah135 136    Returns:137        Formatted string (e.g., "Rp 10.000.000")138 139    Example:140        >>> format_price_idr(10000000)141        'Rp 10.000.000'142    """143    # Format with thousand separators (dot for Indonesian style)144    price_str = f"{int(price):,}".replace(",", ".")145    return f"Rp {price_str}"146 147 148def build_ai_context(products: List[Dict[str, Any]], user_query: str) -> str:149    """150    Build context string for AI with product information.151 152    Args:153        products: List of product dictionaries with supplier info154        user_query: User's original query155 156    Returns:157        Formatted context string for AI prompt158 159    Example:160        >>> products = [{"name": "Laptop ASUS", "price": 10000000, ...}]161        >>> context = build_ai_context(products, "laptop gaming")162        >>> print(context)163    """164    if not products:165        return f"""User query: {user_query}166 167Available products: None found. Inform the user that no products match their criteria and suggest alternatives."""168 169    product_list = []170    for idx, prod in enumerate(products, 1):171        supplier_name = prod.get("supplier_name", "Unknown")172        distance = prod.get("distance_km", "N/A")173        distance_str = f"{distance:.1f} km" if isinstance(distance, (int, float)) else distance174 175        product_info = f"""{idx}. {prod['name']}176   - Price: {format_price_idr(prod['price'])}177   - Stock: {prod['stock_quantity']} units178   - Category: {prod.get('category', 'N/A')}179   - Supplier: {supplier_name} ({distance_str} from user)180   - Description: {prod.get('description', 'No description')[:100]}..."""181 182        product_list.append(product_info)183 184    context = f"""User query: {user_query}185 186Available products (sorted by distance):187 188{chr(10).join(product_list)}189 190Instructions:1911. Recommend products based on user's needs and budget1922. Prioritize nearby suppliers (lower distance_km)1933. Explain why each recommendation fits the user's requirements1944. Mention stock availability1955. Provide price comparison if multiple options exist1966. Use natural, conversational Indonesian or English based on user's language"""197 198    return context199