"""
Response Builder - Standardized Response Formatting

This module provides utilities for formatting detection results into
standardized response formats for API and UI consumption.
"""

import base64
import cv2
import numpy as np
from typing import Dict, List, Optional, Any
from PIL import Image


def build_detection_response(
    analysis: Dict,
    image: Image.Image,
    annotated_image: Optional[np.ndarray] = None,
    confidence_threshold: float = 0.35,
    line_thickness: int = 2,
    enable_clip: bool = False,
    enable_ocr: bool = True,
    enable_blip: bool = False,
    blip_scope: Optional[str] = None,
    ocr_only: bool = False,
    include_annotated_image: bool = True
) -> Dict:
    """
    Build standardized detection response for API/UI
    
    Args:
        analysis: Detection analysis results from DetectionService or OCR handler
        image: Original PIL Image
        annotated_image: Optional annotated image (numpy array, RGB)
        confidence_threshold: Confidence threshold used
        enable_clip: Whether CLIP classification was enabled
        enable_ocr: Whether OCR was enabled
        enable_blip: Whether BLIP was enabled
        blip_scope: BLIP scope ("icons" or "all")
        ocr_only: Whether this was OCR-only mode
        include_annotated_image: Whether to include base64-encoded annotated image
        
    Returns:
        Standardized response dictionary with detections, metadata, and parameters
    """
    # Extract detections
    detections = analysis.get("detections", [])
    
    # Build type distribution if CLIP is enabled
    type_counts = None
    if enable_clip and not ocr_only:
        type_counts = build_type_distribution(detections)
    
    # Prepare response
    response = {
        "success": True,
        "detections": detections,
        "total_detections": len(detections),
        "image_size": analysis.get("image_size", {"width": image.width, "height": image.height}),
        "parameters": {
            "confidence_threshold": confidence_threshold,
            "line_thickness": line_thickness,
            "enable_clip": enable_clip if not ocr_only else False,
            "enable_ocr": enable_ocr if not ocr_only else False,
            "enable_blip": enable_blip if not ocr_only else False,
            "blip_scope": blip_scope if enable_blip and not ocr_only else None,
            "ocr_only": ocr_only
        },
        "type_distribution": type_counts
    }
    
    # Add annotated image if requested
    if include_annotated_image and annotated_image is not None:
        # Encode as base64 PNG
        img_bgr = cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR)
        ok, png_bytes = cv2.imencode(".png", img_bgr)
        if ok:
            annotated_b64 = base64.b64encode(png_bytes.tobytes()).decode("ascii")
            response["annotated_image"] = {
                "mime": "image/png",
                "base64": annotated_b64
            }
    
    return response


def build_type_distribution(detections: List[Dict]) -> Dict[str, int]:
    """
    Build element type distribution from detections
    
    Args:
        detections: List of detection dictionaries with class_name field
        
    Returns:
        Dictionary mapping class names to counts
    """
    type_counts = {}
    for det in detections:
        class_name = det.get("class_name", "")
        if class_name:  # Only count if class_name is not empty
            type_counts[class_name] = type_counts.get(class_name, 0) + 1
    return type_counts


def format_summary_text(
    detections: List[Dict],
    parameters: Dict,
    ocr_only: bool = False
) -> str:
    """
    Format detection results as markdown summary text for Gradio UI
    
    Args:
        detections: List of detection dictionaries
        parameters: Detection parameters used
        ocr_only: Whether this was OCR-only mode
        
    Returns:
        Markdown-formatted summary string
    """
    lines = []
    
    if ocr_only:
        lines.append("**OCR-only mode**")
        lines.append(f"**Total OCR texts:** {len(detections)}")
    else:
        lines.append(f"**Total detections:** {len(detections)}")
    
    lines.append("")
    lines.append("**Settings:**")
    lines.append(f"- Confidence threshold: {parameters.get('confidence_threshold', 0.35):.2f}")
    
    enable_clip = parameters.get('enable_clip', False)
    enable_ocr = parameters.get('enable_ocr', True)
    enable_blip = parameters.get('enable_blip', False)
    blip_scope = parameters.get('blip_scope')
    line_thickness = parameters.get('line_thickness')
    
    lines.append(f"- CLIP classification: {'✅ Enabled' if enable_clip else '❌ Disabled'}")
    lines.append(f"- OCR text extraction: {'✅ Enabled' if enable_ocr or ocr_only else '❌ Disabled'}")
    if line_thickness is not None:
        lines.append(f"- Box line thickness: {line_thickness}")
    
    blip_text = f"- BLIP description: {'✅ Enabled' if enable_blip else '❌ Disabled'}"
    if enable_blip and blip_scope:
        scope_display = "All elements" if blip_scope == "all" else "Only image & button"
        blip_text += f" (scope: {scope_display})"
    lines.append(blip_text)
    
    # Add type distribution if CLIP is enabled
    if enable_clip and not ocr_only and len(detections) > 0:
        type_counts = build_type_distribution(detections)
        if type_counts:
            lines.append("")
            lines.append("**Element types:**")
            for typ, count in sorted(type_counts.items(), key=lambda x: -x[1]):
                lines.append(f"- {typ}: {count}")
    
    return "\n".join(lines)


def build_ocr_only_response(
    detections: List[Dict],
    image_width: int,
    image_height: int,
    annotated_image: Optional[np.ndarray] = None,
    confidence_threshold: float = 0.35,
    line_thickness: int = 2
) -> Dict:
    """
    Build response specifically for OCR-only mode
    
    Args:
        detections: List of OCR detections
        image_width: Original image width
        image_height: Original image height
        annotated_image: Optional annotated image (numpy array, RGB)
        confidence_threshold: Confidence threshold (for consistency in response)
        
    Returns:
        OCR-only response dictionary
    """
    response = {
        "success": True,
        "detections": detections,
        "total_detections": len(detections),
        "image_size": {"width": image_width, "height": image_height},
        "parameters": {
            "confidence_threshold": confidence_threshold,
            "line_thickness": line_thickness,
            "enable_clip": False,
            "enable_ocr": False,  # Not using standard OCR flow
            "enable_blip": False,
            "blip_scope": None,
            "ocr_only": True
        },
        "type_distribution": None
    }
    
    # Add annotated image if provided
    if annotated_image is not None:
        img_bgr = cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR)
        ok, png_bytes = cv2.imencode(".png", img_bgr)
        if ok:
            annotated_b64 = base64.b64encode(png_bytes.tobytes()).decode("ascii")
            response["annotated_image"] = {
                "mime": "image/png",
                "base64": annotated_b64
            }
    
    return response


def build_simplified_response(
    analysis: Dict,
    image: Image.Image,
    annotated_image: Optional[np.ndarray] = None,
    confidence_threshold: float = 0.35,
    line_thickness: int = 2,
    enable_clip: bool = False,
    enable_ocr: bool = True,
    enable_blip: bool = False,
    blip_scope: Optional[str] = None,
    ocr_only: bool = False
) -> Dict:
    """
    Build simplified detection response for API/UI with format:
    {
      "detections": {
        "icon 0": {"type": "text", "bbox": [x1, y1, x2, y2], "interactivity": false, "content": "..."},
        "icon 1": {"type": "icon", "bbox": [x1, y1, x2, y2], "interactivity": true, "content": "..."}
      },
      "annotated_image": {"mime": "image/png", "base64": "..."}
    }
    
    Args:
        analysis: Detection analysis results from DetectionService or OCR handler
        image: Original PIL Image
        annotated_image: Optional annotated image (numpy array, RGB)
        confidence_threshold: Confidence threshold used
        enable_clip: Whether CLIP classification was enabled
        enable_ocr: Whether OCR was enabled
        enable_blip: Whether BLIP was enabled
        blip_scope: BLIP scope ("icons" or "all")
        ocr_only: Whether this was OCR-only mode
        
    Returns:
        Simplified response dictionary with detections dict and annotated_image
    """
    # Extract detections
    detections = analysis.get("detections", [])
    image_width = analysis.get("image_size", {}).get("width", image.width)
    image_height = analysis.get("image_size", {}).get("height", image.height)
    
    # Interactive element types (buttons, inputs, icons, navigation, list items)
    interactive_types = {"button", "input", "icon", "navigation", "list_item"}
    
    # Build simplified detections dict
    simplified_detections = {}
    for idx, det in enumerate(detections):
        # Get bounding box and normalize to 0-1 coordinates
        box = det.get("box", {})
        x1 = box.get("x1", 0) / image_width
        y1 = box.get("y1", 0) / image_height
        x2 = box.get("x2", 0) / image_width
        y2 = box.get("y2", 0) / image_height
        
        # Get type from CLIP classification
        element_type = det.get("class_name", "")
        if not element_type:
            # Fallback: if no CLIP classification, default to "text" if has text, else "icon"
            element_type = "text" if det.get("text", "").strip() else "icon"
        
        # Determine interactivity based on type
        is_interactive = element_type in interactive_types
        
        # Fuse text and description into content
        text = det.get("text", "").strip()
        description = det.get("description", "").strip()
        
        # Content priority: text first, then description
        if text:
            content = text
        elif description:
            content = description
        else:
            content = ""
        
        # Build simplified detection entry
        simplified_detections[f"icon {idx}"] = {
            "type": element_type,
            "bbox": [round(x1, 4), round(y1, 4), round(x2, 4), round(y2, 4)],
            "interactivity": is_interactive,
            "content": content
        }
    
    # Build response
    response = {
        "detections": simplified_detections
    }
    
    # Add annotated image if provided
    if annotated_image is not None:
        img_bgr = cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR)
        ok, png_bytes = cv2.imencode(".png", img_bgr)
        if ok:
            annotated_b64 = base64.b64encode(png_bytes.tobytes()).decode("ascii")
            response["annotated_image"] = {
                "mime": "image/png",
                "base64": annotated_b64
            }
    
    return response