"""
Hotspot Detector Tool

Analyzes git history to detect frequently changed files (hotspots).
High churn files warrant extra scrutiny during review.
"""

from typing import Any
from langchain_core.tools import tool
from pydantic import BaseModel, Field


class HotspotResult(BaseModel):
    """Result from hotspot detection."""
    file_path: str
    change_frequency: int = 0  # Number of changes in recent period
    recent_authors: list[str] = Field(default_factory=list)
    churn_score: float = 0.0  # 0.0 to 1.0, higher = more volatile
    is_hotspot: bool = False


class HotspotAnalysisResult(BaseModel):
    """Aggregated hotspot analysis for all files."""
    hotspots: list[HotspotResult] = Field(default_factory=list)
    overall_churn_score: float = 0.0
    recommendation: str = ""


@tool
def hotspot_detector_tool(
    file_paths: list[str],
    repo_root: str = ".",
    days_lookback: int = 30,
) -> dict[str, Any]:
    """
    Detect code hotspots by analyzing git history.
    
    Files that change frequently may indicate architectural issues or
    areas of high risk that need careful review.
    
    Args:
        file_paths: List of file paths to analyze.
        repo_root: Root directory of the git repository.
        days_lookback: Number of days to look back in git history.
        
    Returns:
        Dictionary with hotspot analysis including churn scores and recommendations.
    """
    # TODO: Implement actual git history analysis using GitPython
    # For now, return placeholder analysis
    
    hotspots: list[HotspotResult] = []
    
    for file_path in file_paths:
        # Placeholder: simulate hotspot detection
        # In production, use: git log --follow --oneline --since="30 days ago" -- <file>
        
        hotspot = HotspotResult(
            file_path=file_path,
            change_frequency=0,
            recent_authors=[],
            churn_score=0.0,
            is_hotspot=False,
        )
        
        # Heuristic: certain paths are typically hotspots
        if any(pattern in file_path for pattern in ["/config/", "/settings/", "constants"]):
            hotspot.change_frequency = 15
            hotspot.churn_score = 0.7
            hotspot.is_hotspot = True
            hotspot.recent_authors = ["multiple"]
        
        hotspots.append(hotspot)
    
    # Calculate overall churn
    total_churn = sum(h.churn_score for h in hotspots)
    overall_churn = total_churn / len(hotspots) if hotspots else 0.0
    
    # Generate recommendation
    recommendation = ""
    if overall_churn > 0.5:
        recommendation = "High churn detected. Consider refactoring or adding tests."
    elif overall_churn > 0.25:
        recommendation = "Moderate churn. Extra review scrutiny recommended."
    
    return HotspotAnalysisResult(
        hotspots=[h.model_dump() for h in hotspots],
        overall_churn_score=overall_churn,
        recommendation=recommendation,
    ).model_dump()


# Export the tool for LangGraph
HotspotDetectorTool = hotspot_detector_tool
