backend/services/response_formatter.py

"""
Response Formatter Service

Handles formatting of query results into citations, charts, GeoJSON layers, and raw data for the frontend.
Separates presentation logic from execution logic.
"""

from typing import List, Dict, Any, Optional
import uuid

class ResponseFormatter:
    @staticmethod
    def generate_citations(tables: List[str], features: Optional[List[Dict]] = None) -> List[str]:
        """Generates readable citations based on table names and returned features."""
        citations = []
        processed = set()
        
        # Check explicit table list
        for table in tables:
            table = table.lower()
            if table in processed: continue
            
            if "universit" in table:
                citations.append("Universities Data (OpenStreetMap, 2024)")
            elif "school" in table or "education" in table:
                citations.append("Education Facilities (OpenStreetMap, 2024)")
            elif "hospital" in table or "health" in table:
                citations.append("Health Facilities (OpenStreetMap, 2024)")
            elif "airport" in table:
                citations.append("Airports Data (OpenStreetMap, 2024)")
            elif "road" in table:
                citations.append("Road Network (OpenStreetMap, 2024)")
            elif "population" in table or "census" in table:
                citations.append("Panama Census Data (INEC, 2023)")
            elif "admin" in table or "boundar" in table:
                if "Admin Boundaries" not in processed:
                    citations.append("Panama Administrative Boundaries (HDX COD-AB, 2021)")
                    processed.add("Admin Boundaries")
                continue 
            
            processed.add(table)
            
        # Fallback check on features if no specific tables cited but admin data returned
        if not citations and features:
             if any(k.startswith("adm") for k in features[0].get("properties", {}).keys()):
                citations.append("Panama Administrative Boundaries (HDX COD-AB, 2021)")
                
        return list(set(citations))

    @staticmethod
    def generate_chart_data(sql: str, features: List[Dict]) -> Optional[Dict[str, Any]]:
        """
        Generates Chart.js compatible data structure if the query looks aggregative.
        """
        if not features:
            return None
            
        # Heuristic: If GROUP BY or ORDER BY ... LIMIT is used, likely suitable for charting
        # Or if explicitly requested via intent (logic handled in caller, but we check SQL signature here too)
        
        # Try to find string (label) and number (value) in properties
        try:
            chart_items = []
            x_key = "name"
            y_key = "value"
            x_label = "Feature"
            y_label = "Value"

            # 1. Analyze properties to find X (Label) and Y (Value)
            if features:
                sample_props = features[0].get("properties", {})
                
                # Exclude system keys
                valid_keys = [k for k in sample_props.keys() if k not in ["geom", "geometry", "style", "layer_name", "layer_id", "choropleth", "fillColor", "color"]]
                
                # Find Y (Value) - First numeric column
                for k in valid_keys:
                    if isinstance(sample_props[k], (int, float)) and not k.endswith("_id") and not k.endswith("_code"):
                        y_key = k
                        y_label = k.replace("_", " ").title()
                        if "sqkm" in k: y_label = "Area (km²)"
                        elif "pop" in k: y_label = "Population"
                        elif "count" in k: y_label = "Count"
                        break
                
                # Find X (Label) - First string column (excluding IDs if possible)
                for k in valid_keys:
                    if isinstance(sample_props[k], str) and "name" in k:
                        x_key = k
                        x_label = k.replace("_", " ").title().replace("Name", "").strip() or "Region"
                        break
            
            # 2. Build Data
            for f in features:
                props = f.get("properties", {})
                label = props.get(x_key)
                value = props.get(y_key)
                
                if label is not None and value is not None:
                     chart_items.append({"name": str(label), "value": value})
            
            if chart_items:
                # auto-sort descending
                chart_items.sort(key=lambda x: x["value"], reverse=True)
                
                return {
                    "type": "bar",
                    "title": f"{y_label} by {x_label}",
                    "data": chart_items[:15], # Limit to top 15 for readability
                    "xKey": "name",
                    "yKey": "value",
                    "xAxisLabel": x_label,
                    "yAxisLabel": y_label
                }
        except Exception as e:
            print(f"Error generating chart data: {e}")
            return None
            
        return None

    @staticmethod
    def prepare_raw_data(features: List[Dict]) -> List[Dict]:
        """Cleans feature properties for display in the raw data table."""
        raw_data = []
        if not features:
            return raw_data
            
        for f in features:
            props = f.get("properties", {}).copy()
            # Serialize
            props = ResponseFormatter._serialize_properties(props)
            
            # Remove system/visual properties
            for key in ["geom", "geometry", "style", "layer_name", "layer_id", "choropleth", "fillColor", "color"]:
                props.pop(key, None)
            raw_data.append(props)
            
        return raw_data

    @staticmethod
    def format_geojson_layer(query: str, geojson: Dict[str, Any], features: List[Dict], layer_name: str, layer_emoji: str = "📍", point_style: Optional[str] = None, admin_levels: Optional[List[str]] = None) -> tuple[Dict[str, Any], str, str]:
        """
        styles the GeoJSON layer and generates metadata (ID, Name, Choropleth).
        
        Args:
            point_style: "icon" for emoji markers, "circle" for simple colored circles, None for auto-detect
        """
        
        # 0. Serialize properties to avoid datetime errors
        if features:
            for f in features:
                if "properties" in f:
                    f["properties"] = ResponseFormatter._serialize_properties(f["properties"])

        # 2. Random/Distinct Colors
        # Palette of distinct colors (avoiding pure blue which is default)
        palette = [
            "#E63946", # Red
            "#F4A261", # Orange
            "#2A9D8F", # Teal
            "#E9C46A", # Yellow
            "#9C6644", # Brown
            "#D62828", # Dark Red
            "#8338EC", # Purple
            "#3A86FF", # Blue-ish (but distinct)
            "#FB5607", # Orange-Red
            "#FF006E", # Pink
        ]
        
        # Deterministic color based on query hash to keep it stable for same query
        color_idx = abs(hash(query)) % len(palette)
        layer_color = palette[color_idx]

        # Choropleth Logic
        # 1. Identify valid numeric column
        choropleth_col = None
        if features:
            sample = features[0].get("properties", {})
            valid_numerics = [
                k for k, v in sample.items() 
                if isinstance(v, (int, float)) 
                and k not in ["layer_id", "style"] 
                and not k.endswith("_code") 
                and not k.endswith("_id")
            ]
            
            # Prioritize 'population', 'area', 'count'
            priority_cols = ["population", "pop", "count", "num", "density", "area_sqkm", "area"]
            
            for p in priority_cols:
                matches = [c for c in valid_numerics if p in c]
                if matches:
                    choropleth_col = matches[0]
                    break
            
            # Fallback to first numeric
            if not choropleth_col and valid_numerics:
                choropleth_col = valid_numerics[0]

        # 2. Enable if appropriate
        if choropleth_col:
            # Check if values actually vary
            values = [f["properties"].get(choropleth_col, 0) for f in features]
            if len(set(values)) > 1:
                geojson["properties"]["choropleth"] = {
                    "enabled": True, 
                    "palette": "viridis",
                    "column": choropleth_col,
                    "scale": "log" if "pop" in choropleth_col or "density" in choropleth_col else "linear"
                }
        else:
             # Apply random color if NOT a choropleth
             geojson["properties"]["style"] = {
                 "color": layer_color,
                 "fillColor": layer_color,
                 "opacity": 0.8,
                 "fillOpacity": 0.4
             }
        
        layer_id = str(uuid.uuid4())[:8]
        geojson["properties"]["layer_name"] = layer_name
        geojson["properties"]["layer_id"] = layer_id
        
        # Add Point Marker Configuration
        # Use pointStyle to determine whether to show icon or circle
        marker_icon = None
        marker_style = "circle"  # default
        
        if point_style == "icon":
            # Use emoji icon for categorical POI
            marker_icon = layer_emoji
            marker_style = "icon"
        elif point_style == "circle":
            # Use simple circle for large datasets or density viz
            marker_icon = None
            marker_style = "circle"
        else:
            # Auto-detect: default to icon for now (backward compatibility)
            marker_icon = layer_emoji
            marker_style = "icon"
        
        geojson["properties"]["pointMarker"] = {
            "icon": marker_icon,
            "style": marker_style,
            "color": layer_color,
            "size": 32
        }
        
        return geojson, layer_id, layer_name

    @staticmethod
    def generate_data_summary(features: List[Dict]) -> str:
        """Generates a text summary of the features for the LLM explanation context."""
        if features:
            sample_names = []
            for f in features[:5]:
                props = f.get("properties", {})
                name = props.get("adm3_name") or props.get("adm2_name") or props.get("adm1_name") or props.get("name") or "Feature"
                area = props.get("area_sqkm")
                if area:
                    sample_names.append(f"{name} ({float(area):.1f} km²)")
                else:
                    sample_names.append(name)
            return f"Found {len(features)} features. Sample: {', '.join(sample_names)}"
            return f"Found {len(features)} features. Sample: {', '.join(sample_names)}"
        else:
            return "No features found matching the query."

    @staticmethod
    def _serialize_properties(properties: Dict[str, Any]) -> Dict[str, Any]:
        """Recursively converts datetime/date objects to strings for JSON serialization."""
        from datetime import datetime, date
        
        serialized = {}
        for k, v in properties.items():
            if isinstance(v, (datetime, date)):
                serialized[k] = v.isoformat()
            elif isinstance(v, dict):
                serialized[k] = ResponseFormatter._serialize_properties(v)
            elif isinstance(v, list):
                serialized[k] = [
                    x.isoformat() if isinstance(x, (datetime, date)) else x 
                    for x in v
                ]
            else:
                serialized[k] = v
        return serialized