pounce/backend/app/services/llm_naming.py

"""
LLM-powered naming suggestions for Trends and Forge tabs.
Uses simple prompts for focused tasks - no complex agent loop.
"""
from __future__ import annotations

import json
import re
from typing import Optional

from app.config import get_settings
from app.services.llm_gateway import chat_completions

settings = get_settings()


async def expand_trend_keywords(trend: str, geo: str = "US") -> list[str]:
    """
    Given a trending topic, generate related domain-friendly keywords.
    Returns a list of 5-10 short, brandable keywords.
    """
    prompt = f"""You are a domain naming expert. Given the trending topic "{trend}" (trending in {geo}), 
suggest 8-10 short, memorable keywords that would make good domain names.

Rules:
- Each keyword should be 4-10 characters
- No spaces, hyphens, or special characters
- Mix of: related words, abbreviations, creative variations
- Think like a domain investor looking for valuable names

Return ONLY a JSON array of lowercase strings, nothing else.
Example: ["swiftie", "erastour", "taylormerch", "tswift"]"""

    try:
        res = await chat_completions({
            "model": settings.llm_default_model,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.8,
            "stream": False,
        })
        content = res.get("choices", [{}])[0].get("message", {}).get("content", "")
        
        # Extract JSON array from response
        match = re.search(r'\[.*?\]', content, re.DOTALL)
        if match:
            keywords = json.loads(match.group(0))
            # Filter and clean
            return [
                kw.lower().strip()[:15] 
                for kw in keywords 
                if isinstance(kw, str) and 3 <= len(kw.strip()) <= 15 and kw.isalnum()
            ][:10]
    except Exception as e:
        print(f"LLM keyword expansion failed: {e}")
    
    return []


async def analyze_trend(trend: str, geo: str = "US") -> str:
    """
    Provide a brief analysis of why a trend is relevant for domain investors.
    Returns 2-3 sentences max.
    """
    prompt = f"""You are a domain investing analyst. The topic "{trend}" is currently trending in {geo}.

In 2-3 short sentences, explain:
1. Why this is trending (if obvious)
2. What domain opportunity this presents

Be concise and actionable. No fluff."""

    try:
        res = await chat_completions({
            "model": settings.llm_default_model,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.5,
            "stream": False,
        })
        content = res.get("choices", [{}])[0].get("message", {}).get("content", "")
        # Clean up and limit length
        content = content.strip()[:500]
        return content
    except Exception as e:
        print(f"LLM trend analysis failed: {e}")
    
    return ""


async def generate_brandable_names(
    concept: str,
    style: Optional[str] = None,
    count: int = 15
) -> list[str]:
    """
    Generate brandable domain names based on a concept description.
    
    Args:
        concept: Description like "AI startup for legal documents"
        style: Optional style hint like "professional", "playful", "tech"
        count: Number of names to generate
    
    Returns list of brandable name suggestions (without TLD).
    """
    style_hint = f" The style should be {style}." if style else ""
    
    prompt = f"""You are an expert brand naming consultant. Generate {count} unique, brandable domain names for: "{concept}"{style_hint}

Rules:
- Names must be 4-8 characters (shorter is better)
- Easy to spell and pronounce
- Memorable and unique
- No dictionary words (invented names only)
- Mix of patterns: CVCVC (Zalor), CVCCV (Bento), short words (Lyft)

Return ONLY a JSON array of lowercase strings, nothing else.
Example: ["zenix", "klaro", "voxly", "nimbl", "brivv"]"""

    try:
        res = await chat_completions({
            "model": settings.llm_default_model,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.9,  # Higher creativity
            "stream": False,
        })
        content = res.get("choices", [{}])[0].get("message", {}).get("content", "")
        
        # Extract JSON array
        match = re.search(r'\[.*?\]', content, re.DOTALL)
        if match:
            names = json.loads(match.group(0))
            # Filter and clean
            return [
                name.lower().strip()
                for name in names
                if isinstance(name, str) and 3 <= len(name.strip()) <= 12 and name.isalnum()
            ][:count]
    except Exception as e:
        print(f"LLM brandable generation failed: {e}")
    
    return []


async def generate_similar_names(brand: str, count: int = 12) -> list[str]:
    """
    Generate names similar to an existing brand.
    Useful for finding alternatives or inspired names.
    """
    prompt = f"""You are a brand naming expert. Generate {count} new brandable names INSPIRED BY (but not copying) "{brand}".

The names should:
- Have similar length and rhythm to "{brand}"
- Feel like they belong in the same industry
- Be completely original (not existing brands)
- Be 4-8 characters, easy to spell

Return ONLY a JSON array of lowercase strings, nothing else."""

    try:
        res = await chat_completions({
            "model": settings.llm_default_model,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.85,
            "stream": False,
        })
        content = res.get("choices", [{}])[0].get("message", {}).get("content", "")
        
        match = re.search(r'\[.*?\]', content, re.DOTALL)
        if match:
            names = json.loads(match.group(0))
            return [
                name.lower().strip()
                for name in names
                if isinstance(name, str) and 3 <= len(name.strip()) <= 12 and name.isalnum()
            ][:count]
    except Exception as e:
        print(f"LLM similar names failed: {e}")
    
    return []