"""
Processing pipeline wrapper for the Tesseract++ system
Wraps Main.py functionality for web API usage
"""

import os
import sys
import json
import tempfile
import shutil
from pathlib import Path
from typing import Dict, Any, Optional
import threading
from typing import Callable

# Headless matplotlib backend before Main (and its plotting modules) load.
os.environ.setdefault("MPLBACKEND", "Agg")
import matplotlib
matplotlib.use("Agg")

# Progress tracking (thread-safe via GIL for simple dict writes)
_progress: Dict[str, str] = {}

def set_progress(key: str, stage: str):
    _progress[key] = stage

def get_progress(key: str) -> str:
    return _progress.get(key, "")

def clear_progress(key: str):
    _progress.pop(key, None)

# Add required paths
base_path = Path(__file__).parent.parent.parent.parent  # Back to Tesseract++ root
sys.path.insert(0, str(base_path))
sys.path.insert(0, str(base_path / "Models" / "Text_Models"))
sys.path.insert(0, str(base_path / "Models" / "Interpreter"))
sys.path.insert(0, str(base_path / "Models" / "Door_Models"))
sys.path.insert(0, str(base_path / "utils"))

# Import main processing functions
import Main

class TimeoutException(Exception):
    """Custom exception for processing timeout"""
    pass

class ProcessingPipeline:
    """
    Wrapper for the Tesseract++ processing pipeline
    """

    def __init__(self):
        self.base_path = Path(__file__).parent.parent.parent.parent
        self.input_images_dir = self.base_path / "Input_Images"
        self.results_dir = self.base_path / "Results"
        self.temp_dir = self.base_path / "temp_processing"

        # Create temp directory if not exists
        self.temp_dir.mkdir(exist_ok=True)

        # Verify model weights exist
        self._verify_models()

    def _verify_models(self):
        """Verify all required model weights are present"""
        weights_dir = self.base_path / "Model_weights"
        required_weights = [
            "craft_mlt_25k.pth",
            "None-VGG-BiLSTM-CTC.pth",
            "door_mdl_32.pth"
        ]

        for weight_file in required_weights:
            weight_path = weights_dir / weight_file
            if not weight_path.exists():
                raise FileNotFoundError(f"Required model weight not found: {weight_path}")

    def get_cached_result(self, image_name: str) -> Optional[Dict[str, Any]]:
        """
        Check for and return pre-computed results for an image.

        Args:
            image_name: Image filename (e.g. "FF part 1upE.png")

        Returns:
            Processing result dict if cached, None otherwise.
        """
        image_stem = Path(image_name).stem
        json_dir = self.results_dir / "Json" / image_stem
        post_pruning_json = json_dir / f"{image_stem}_post_pruning.json"
        pre_pruning_json = json_dir / f"{image_stem}_pre_pruning.json"

        if not post_pruning_json.exists():
            return None

        with open(post_pruning_json, 'r') as f:
            graph_data = json.load(f)

        stats = self._calculate_statistics(graph_data)

        # Load pre-pruning graph if available
        pre_pruning_graph = None
        if pre_pruning_json.exists():
            with open(pre_pruning_json, 'r') as f:
                pre_pruning_graph = json.load(f)
                pre_nodes = len(pre_pruning_graph.get("nodes", []))
                post_nodes = len(graph_data.get("nodes", []))
                stats["pruning_reduction"] = round(
                    (1 - post_nodes / pre_nodes) * 100, 2
                ) if pre_nodes > 0 else 0

        return {
            "graph_json": graph_data,
            "pre_pruning_graph_json": pre_pruning_graph,
            "stats": stats,
            "image_name": image_name
        }

    def has_cached_result(self, image_name: str) -> bool:
        """Check if a cached result exists for an image."""
        image_stem = Path(image_name).stem
        post_pruning_json = self.results_dir / "Json" / image_stem / f"{image_stem}_post_pruning.json"
        return post_pruning_json.exists()

    def process_image(self, image_path: str, image_name: str, timeout: int = 180, progress_key: str = None) -> Dict[str, Any]:
        """
        Process a floorplan image through the Tesseract++ pipeline

        Args:
            image_path: Path to the image file
            image_name: Original image filename
            timeout: Processing timeout in seconds

        Returns:
            Dictionary containing processing results
        """
        # Create a unique temp folder for this processing session
        import uuid
        session_id = str(uuid.uuid4())
        session_dir = self.temp_dir / session_id
        session_dir.mkdir(exist_ok=True)

        # Copy image to Input_Images temporarily if not already there
        input_image_path = self.input_images_dir / image_name
        image_was_copied = False

        # Track exception from thread
        thread_exception = [None]

        try:
            if not input_image_path.exists():
                shutil.copy2(image_path, input_image_path)
                image_was_copied = True

            # Redirect outputs to session directory
            original_cwd = os.getcwd()
            os.chdir(self.base_path)

            # Progress callback
            def on_progress(stage: str):
                if progress_key:
                    set_progress(progress_key, stage)

            # Run the main processing pipeline with timeout
            def run_processing():
                try:
                    Main.make_graph(image_name, progress_callback=on_progress)
                except Exception as e:
                    thread_exception[0] = e

            # Use threading for timeout control
            thread = threading.Thread(target=run_processing)
            thread.daemon = True
            thread.start()
            thread.join(timeout)

            if thread.is_alive():
                raise TimeoutException(f"Processing exceeded {timeout} seconds")

            if thread_exception[0] is not None:
                raise thread_exception[0]

            # Extract results
            image_name_no_ext = Path(image_name).stem

            # Find the generated JSON files
            json_dir = self.results_dir / "Json" / image_name_no_ext
            post_pruning_json = json_dir / f"{image_name_no_ext}_post_pruning.json"
            pre_pruning_json = json_dir / f"{image_name_no_ext}_pre_pruning.json"

            # Read the post-pruning graph
            if not post_pruning_json.exists():
                raise FileNotFoundError(f"Processing completed but output not found: {post_pruning_json}")

            with open(post_pruning_json, 'r') as f:
                graph_data = json.load(f)

            # Calculate statistics
            stats = self._calculate_statistics(graph_data)

            # Load pre-pruning graph if available
            pre_pruning_graph = None
            if pre_pruning_json.exists():
                with open(pre_pruning_json, 'r') as f:
                    pre_pruning_graph = json.load(f)
                    pre_nodes = len(pre_pruning_graph.get("nodes", []))
                    post_nodes = len(graph_data.get("nodes", []))
                    stats["pruning_reduction"] = round((1 - post_nodes / pre_nodes) * 100, 2) if pre_nodes > 0 else 0

            result = {
                "graph_json": graph_data,
                "pre_pruning_graph_json": pre_pruning_graph,
                "stats": stats,
                "session_id": session_id,
                "image_name": image_name
            }

            # Clean up Results for uploaded (non-example) images
            if image_was_copied:
                self._cleanup_results(image_name_no_ext)

            return result

        finally:
            # Cleanup
            os.chdir(original_cwd)

            if progress_key:
                clear_progress(progress_key)

            # Remove copied image if it was temporary
            if image_was_copied and input_image_path.exists():
                input_image_path.unlink()

            # Clean up session directory
            if session_dir.exists():
                shutil.rmtree(session_dir, ignore_errors=True)

    def _cleanup_results(self, image_stem: str):
        """Clean up Results subdirectories for non-example (uploaded) images."""
        results_subdirs = [
            "Json", "Plots/connective_plots", "Plots/door_detect",
            "Plots/flood_fill", "Plots/graph_plots", "Plots/interpreter_detect",
            "Plots/room_subnodes", "Plots/smart_fill", "Plots/text_detection",
            "Plots/test_plots", "Time&Meta/Text files"
        ]
        for subdir in results_subdirs:
            result_path = self.results_dir / subdir / image_stem
            if result_path.exists() and result_path.is_dir():
                shutil.rmtree(result_path, ignore_errors=True)
            # Also check for timer info text files
            timer_file = self.results_dir / "Time&Meta" / "Text files" / f"{image_stem}_timer_info.txt"
            if timer_file.exists():
                timer_file.unlink(missing_ok=True)

    def _calculate_statistics(self, graph_data: Dict[str, Any]) -> Dict[str, Any]:
        """Calculate graph statistics"""
        nodes = graph_data.get("nodes", [])
        edges = graph_data.get("edges", [])

        # Count nodes by type
        node_types = {}
        for node in nodes:
            node_type = node.get("type", "unknown")
            node_types[node_type] = node_types.get(node_type, 0) + 1

        return {
            "total_nodes": len(nodes),
            "total_edges": len(edges),
            "node_types": node_types
        }

    def get_example_images(self) -> list:
        """Get list of available example images"""
        images = []
        for img_path in sorted(self.input_images_dir.glob("*.png"))[:4]:
            images.append({
                "name": img_path.name,
                "size_kb": img_path.stat().st_size / 1024
            })
        return images