"""
Gradio Phase Reconstruction Viewer

Interactive web interface for viewing zarr microscopy data with T/Z navigation.
Based on: docs/examples/visuals/optimize_phase_recon.py
"""

import gradio as gr
import numpy as np
import pandas as pd
from pathlib import Path

from demo_utils import (
    print_data_summary,
    run_optimization_streaming,
    get_plate_metadata,
    load_fov_from_plate,
    extract_2d_slice,
    run_reconstruction_single,
)


# ============================================================================
# CONFIGURATION
# ============================================================================

class Config:
    """Centralized configuration for the phase reconstruction viewer."""

    # Input data path
    INPUT_PATH = Path("data/20x.zarr")

    # Default FOV selection
    DEFAULT_ROW = "A"
    DEFAULT_COLUMN = "1"
    DEFAULT_FIELD = "005029"  # Center FOV

    # Restrict to specific FOVs (center of well A/1 for better quality)
    ALLOWED_FOVS = ['005028', '005029', '005030']

    # Channel selection (only BF channel in concatenated data)
    CHANNEL = 0  # BF is now channel 0 (GFP was filtered out during concatenation)

    # Pixel sizes for 20x objective (override incorrect Zarr metadata)
    PIXEL_SIZE_YX = 0.325  # micrometers
    PIXEL_SIZE_Z = 2.0     # micrometers

    # Reconstruction configuration
    RECON_CONFIG = {
        "wavelength_illumination": 0.45,
        "index_of_refraction_media": 1.3,
        "invert_phase_contrast": False,
        "num_iterations": 10,
        # GPU Configuration (auto-detects GPU for 15-25x speedup)
        # - None: Auto-detect (uses CUDA if available, else CPU)
        # - "cuda": Force GPU usage (requires CUDA-capable device)
        # - "cpu": Force CPU usage (for testing/debugging)
        "device": None,
        # Tiling (not implemented - using full image)
        "use_tiling": False,
    }

    # Optimizable parameters: (optimize_flag, initial_value, learning_rate)
    OPTIMIZABLE_PARAMS = {
        "z_offset": (True, 0.0, 0.01),
        "numerical_aperture_detection": (True, 0.55, 0.001),
        "numerical_aperture_illumination": (True, 0.54, 0.001),
        "tilt_angle_zenith": (True, 0.0, 0.005),
        "tilt_angle_azimuth": (True, 0.0, 0.001),
    }

    # UI slider ranges
    SLIDER_RANGES = {
        "z_offset": (-3.0, 3.0, 0.01),  # ±3 µm (1.5x Z-slice spacing for focus correction)
        "na_detection": (0.05, 0.65, 0.001),  # Max 0.65 to accommodate optimization
        "na_illumination": (0.05, 0.65, 0.001),  # Max 0.65 (but constrained <= NA_detection)
        "tilt_zenith": (0.0, np.pi / 4, 0.005),
        "tilt_azimuth": (0.0, np.pi / 4, 0.001),
    }

    # UI configuration
    IMAGE_HEIGHT = 800
    SERVER_PORT = 12124


# ============================================================================
# GLOBAL STATE INITIALIZATION
# ============================================================================

def initialize_plate_metadata():
    """Load and display plate metadata."""
    print("\n" + "=" * 60)
    print("Loading HCS Plate Metadata...")
    print("=" * 60)

    # Pass allowed FOVs to avoid iterating through all positions
    plate_metadata = get_plate_metadata(Config.INPUT_PATH, Config.ALLOWED_FOVS)

    print(f"Available rows: {plate_metadata['rows']}")
    print(f"Available columns: {plate_metadata['columns']}")
    print(f"Total wells: {len(plate_metadata['wells'])}")

    # Get default well fields (already filtered)
    default_well_key = (Config.DEFAULT_ROW, Config.DEFAULT_COLUMN)
    default_fields = plate_metadata["wells"].get(default_well_key, [])

    print(f"Fields in {Config.DEFAULT_ROW}/{Config.DEFAULT_COLUMN}: {len(default_fields)}")
    print(f"Allowed FOVs: {Config.ALLOWED_FOVS}")
    print("=" * 60 + "\n")

    return plate_metadata, default_fields


def load_default_fov(plate_metadata):
    """Load the default field of view and use correct pixel scales."""
    print(f"Loading default FOV: {Config.DEFAULT_ROW}/{Config.DEFAULT_COLUMN}/{Config.DEFAULT_FIELD}")

    data_xr = load_fov_from_plate(
        plate_metadata["plate"],
        Config.DEFAULT_ROW,
        Config.DEFAULT_COLUMN,
        Config.DEFAULT_FIELD,
        resolution=0,
    )

    print_data_summary(data_xr)

    # Use correct pixel scales from config (20x objective)
    # Note: Zarr metadata may have incorrect values from different magnification
    pixel_scales = (
        Config.PIXEL_SIZE_Z,   # z_scale
        Config.PIXEL_SIZE_YX,  # y_scale
        Config.PIXEL_SIZE_YX,  # x_scale
    )
    print(f"Using pixel scales (Z, Y, X): {pixel_scales} micrometers (from config, 20x objective)")

    return data_xr, pixel_scales


# ============================================================================
# FOV LOADING CALLBACKS
# ============================================================================

def load_selected_fov(field: str, current_z: int, plate_metadata):
    """Load selected FOV and update UI components."""
    try:
        print(f"\nLoading FOV: {Config.DEFAULT_ROW}/{Config.DEFAULT_COLUMN}/{field}")

        # Load new data
        new_data_xr = load_fov_from_plate(
            plate_metadata["plate"],
            Config.DEFAULT_ROW,
            Config.DEFAULT_COLUMN,
            field,
            resolution=0,
        )

        # Use pixel scales from config (not Zarr metadata)
        new_pixel_scales = (Config.PIXEL_SIZE_Z, Config.PIXEL_SIZE_YX, Config.PIXEL_SIZE_YX)

        # Update Z slider
        z_max = new_data_xr.sizes["Z"] - 1
        new_z = min(current_z, z_max)

        print(f"✅ Loaded: {dict(new_data_xr.sizes)}")

        # Get preview image
        preview_image = extract_2d_slice(
            new_data_xr, t=0, c=Config.CHANNEL, z=new_z, normalize=True, verbose=False
        )

        return (
            gr.Slider(maximum=z_max, value=new_z),  # Updated Z slider
            (preview_image, preview_image),  # ImageSlider in preview mode
            new_data_xr,  # Update state
            new_pixel_scales,  # Update state
        )

    except Exception as e:
        print(f"❌ Error loading FOV: {str(e)}")
        import traceback
        traceback.print_exc()
        return (gr.skip(), gr.skip(), gr.skip(), gr.skip())


# ============================================================================
# IMAGE DISPLAY CALLBACKS
# ============================================================================

def get_slice_for_preview(z: int, data_xr_state):
    """Extract slice and show in preview mode (same image twice)."""
    slice_img = extract_2d_slice(
        data_xr_state, t=0, c=Config.CHANNEL, z=int(z), normalize=True, verbose=False
    )
    return (slice_img, slice_img)  # Preview mode: both sides show same image


def update_original_slice_only(z: int, data_xr_state, current_reconstructed_state):
    """
    Update only the left (original) image when Z changes, keep reconstruction on right.

    If no reconstruction exists yet, shows the original on both sides.
    """
    slice_img = extract_2d_slice(
        data_xr_state, t=0, c=Config.CHANNEL, z=int(z), normalize=True, verbose=False
    )

    # If there's a reconstruction, keep it on the right; otherwise show original on both sides
    if current_reconstructed_state is not None:
        return (slice_img, current_reconstructed_state)
    else:
        return (slice_img, slice_img)


# ============================================================================
# RECONSTRUCTION CALLBACKS
# ============================================================================

def run_reconstruction_ui(
    z: int,
    z_offset: float,
    na_det: float,
    na_ill: float,
    tilt_zenith: float,
    tilt_azimuth: float,
    data_xr_state,
    pixel_scales_state,
):
    """
    Run reconstruction with CURRENT slider values (no optimization).

    Uses slider parameters directly for a single fast reconstruction.
    """
    # Extract full Z-stack for timepoint 0 (for reconstruction)
    zyx_stack = data_xr_state.isel(T=0, C=Config.CHANNEL).values

    # Get current Z-slice for comparison (left side of ImageSlider)
    original_normalized = extract_2d_slice(
        data_xr_state, t=0, c=Config.CHANNEL, z=int(z), normalize=True, verbose=False
    )

    # Build parameter dict from slider values
    param_values = {
        "z_offset": z_offset,
        "numerical_aperture_detection": na_det,
        "numerical_aperture_illumination": na_ill,
        "tilt_angle_zenith": tilt_zenith,
        "tilt_angle_azimuth": tilt_azimuth,
    }

    # Run single reconstruction with these parameters
    reconstructed_image = run_reconstruction_single(
        zyx_stack, pixel_scales_state, Config.RECON_CONFIG, param_values
    )

    # Return updated image slider AND reconstructed state
    return (original_normalized, reconstructed_image), reconstructed_image


def run_optimization_ui(
    z: int,
    num_iterations: int,
    z_offset: float,
    na_det: float,
    na_ill: float,
    tilt_zenith: float,
    tilt_azimuth: float,
    data_xr_state,
    pixel_scales_state,
):
    """
    Run OPTIMIZATION and stream updates to UI with iteration caching.

    Uses current slider values as initial guesses, runs full optimization loop.
    Yields progressive updates for ImageSlider, loss plot, status,
    iteration history, iteration slider, and SLIDER UPDATES.
    """
    # Extract full Z-stack for timepoint 0 (for reconstruction)
    zyx_stack = data_xr_state.isel(T=0, C=Config.CHANNEL).values

    # Get current Z-slice for comparison (left side of ImageSlider)
    original_normalized = extract_2d_slice(
        data_xr_state, t=0, c=Config.CHANNEL, z=int(z), normalize=True, verbose=False
    )

    # Build optimizable params with current slider values as initial values
    optimizable_params_with_slider_values = {
        "z_offset": (
            Config.OPTIMIZABLE_PARAMS["z_offset"][0],  # enabled flag
            z_offset,  # initial value from slider
            Config.OPTIMIZABLE_PARAMS["z_offset"][2],  # learning rate
        ),
        "numerical_aperture_detection": (
            Config.OPTIMIZABLE_PARAMS["numerical_aperture_detection"][0],
            na_det,
            Config.OPTIMIZABLE_PARAMS["numerical_aperture_detection"][2],
        ),
        "numerical_aperture_illumination": (
            Config.OPTIMIZABLE_PARAMS["numerical_aperture_illumination"][0],
            na_ill,
            Config.OPTIMIZABLE_PARAMS["numerical_aperture_illumination"][2],
        ),
        "tilt_angle_zenith": (
            Config.OPTIMIZABLE_PARAMS["tilt_angle_zenith"][0],
            tilt_zenith,
            Config.OPTIMIZABLE_PARAMS["tilt_angle_zenith"][2],
        ),
        "tilt_angle_azimuth": (
            Config.OPTIMIZABLE_PARAMS["tilt_angle_azimuth"][0],
            tilt_azimuth,
            Config.OPTIMIZABLE_PARAMS["tilt_angle_azimuth"][2],
        ),
    }

    # Initialize tracking
    loss_history = []
    iteration_cache = []

    # Set raw image once at the start (pin it)
    yield (
        (original_normalized, original_normalized),  # Show raw image on both sides initially
        pd.DataFrame({"iteration": [], "loss": []}),  # Initialize loss plot with empty data
        [],  # Clear iteration history
        gr.skip(),  # Don't update slider yet (avoid min=max=1 error)
        gr.Markdown(value="Starting optimization...", visible=True),
        # Slider updates (5 outputs):
        gr.skip(),  # z_offset
        gr.skip(),  # na_det
        gr.skip(),  # na_ill
        gr.skip(),  # tilt_zenith
        gr.skip(),  # tilt_azimuth
        None,  # No reconstructed image yet
    )

    # Run optimization with streaming (using slider values as initial values)
    for result in run_optimization_streaming(
        zyx_stack,
        pixel_scales_state,
        Config.RECON_CONFIG,
        optimizable_params_with_slider_values,
        num_iterations=num_iterations,
    ):
        # Current iteration number
        n = result["iteration"]

        # Cache iteration result
        iteration_cache.append(
            {
                "iteration": n,
                "reconstructed_image": result["reconstructed_image"],
                "loss": result["loss"],
                "params": result["params"],
                "raw_image": original_normalized,
            }
        )

        # Accumulate loss history (ensure iteration is int for proper x-axis)
        loss_history.append({"iteration": int(n), "loss": result["loss"]})

        # Format iteration info
        info_md = f"**Iteration {n}/{num_iterations}** | Loss: `{result['loss']:.2e}`"

        # Clip optimized parameters to slider ranges (avoid Gradio validation errors)
        # Convert to float to ensure Gradio compatibility
        clipped_params = {
            "z_offset": float(np.clip(
                result["params"].get("z_offset", 0.0),
                Config.SLIDER_RANGES["z_offset"][0],
                Config.SLIDER_RANGES["z_offset"][1],
            )),
            "numerical_aperture_detection": float(np.clip(
                result["params"].get("numerical_aperture_detection", 0.55),
                Config.SLIDER_RANGES["na_detection"][0],
                Config.SLIDER_RANGES["na_detection"][1],
            )),
            "numerical_aperture_illumination": float(np.clip(
                result["params"].get("numerical_aperture_illumination", 0.54),
                Config.SLIDER_RANGES["na_illumination"][0],
                Config.SLIDER_RANGES["na_illumination"][1],
            )),
            "tilt_angle_zenith": float(np.clip(
                result["params"].get("tilt_angle_zenith", 0.0),
                Config.SLIDER_RANGES["tilt_zenith"][0],
                Config.SLIDER_RANGES["tilt_zenith"][1],
            )),
            "tilt_angle_azimuth": float(np.clip(
                result["params"].get("tilt_angle_azimuth", 0.0),
                Config.SLIDER_RANGES["tilt_azimuth"][0],
                Config.SLIDER_RANGES["tilt_azimuth"][1],
            )),
        }

        # Yield updates - update ImageSlider AND sliders with clipped params
        yield (
            (original_normalized, result["reconstructed_image"]),  # Update ImageSlider
            pd.DataFrame(loss_history),  # Loss plot
            iteration_cache,  # Update iteration history state
            gr.Slider(  # Update iteration slider (grows from 1-1 to 1-10)
                minimum=1,
                maximum=n,
                value=n,
                step=1,
                visible=True,
                interactive=True,
            ),
            gr.Markdown(value=info_md, visible=True),  # Show iteration info
            # Update parameter sliders with clipped optimized values:
            clipped_params["z_offset"],
            clipped_params["numerical_aperture_detection"],
            clipped_params["numerical_aperture_illumination"],
            clipped_params["tilt_angle_zenith"],
            clipped_params["tilt_angle_azimuth"],
            result["reconstructed_image"],  # Update reconstructed image state
        )

    # Final yield (keep last state)
    yield (
        gr.skip(),  # Keep last ImageSlider state
        gr.skip(),  # Keep last loss plot
        gr.skip(),  # Keep iteration history
        gr.skip(),  # Keep iteration slider
        gr.Markdown(
            value=f"**Optimization Complete!** Final Loss: `{result['loss']:.2e}`",
            visible=True,
        ),
        gr.skip(),  # Keep z_offset
        gr.skip(),  # Keep na_det
        gr.skip(),  # Keep na_ill
        gr.skip(),  # Keep tilt_zenith
        gr.skip(),  # Keep tilt_azimuth
        gr.skip(),  # Keep reconstructed image state
    )


# ============================================================================
# ITERATION SCRUBBING CALLBACKS
# ============================================================================

def scrub_iterations(iteration_idx: int, history: list):
    """Update display AND parameter sliders when user scrubs to different iteration."""
    if not history or iteration_idx < 1 or iteration_idx > len(history):
        return (gr.skip(),) * 7  # image, info, and 5 parameter values

    # Get selected iteration (convert to 0-indexed)
    selected = history[iteration_idx - 1]

    # Update ImageSlider overlay
    comparison = (selected["raw_image"], selected["reconstructed_image"])

    # Update info display
    info_md = f"**Iteration {selected['iteration']}/{len(history)}** | Loss: `{selected['loss']:.2e}`"

    # Extract parameter values at this iteration and clip to slider ranges
    # Convert to float to ensure Gradio compatibility
    params = selected["params"]
    z_offset = float(np.clip(
        params.get("z_offset", 0.0),
        Config.SLIDER_RANGES["z_offset"][0],
        Config.SLIDER_RANGES["z_offset"][1],
    ))
    na_det = float(np.clip(
        params.get("numerical_aperture_detection", 0.55),
        Config.SLIDER_RANGES["na_detection"][0],
        Config.SLIDER_RANGES["na_detection"][1],
    ))
    na_ill = float(np.clip(
        params.get("numerical_aperture_illumination", 0.54),
        Config.SLIDER_RANGES["na_illumination"][0],
        Config.SLIDER_RANGES["na_illumination"][1],
    ))
    tilt_zenith = float(np.clip(
        params.get("tilt_angle_zenith", 0.0),
        Config.SLIDER_RANGES["tilt_zenith"][0],
        Config.SLIDER_RANGES["tilt_zenith"][1],
    ))
    tilt_azimuth = float(np.clip(
        params.get("tilt_angle_azimuth", 0.0),
        Config.SLIDER_RANGES["tilt_azimuth"][0],
        Config.SLIDER_RANGES["tilt_azimuth"][1],
    ))

    return comparison, info_md, z_offset, na_det, na_ill, tilt_zenith, tilt_azimuth


def clear_iteration_state():
    """Reset iteration state when coordinates change."""
    return (
        [],  # Clear iteration_history
        gr.skip(),  # Don't update slider (avoid min=max error)
        gr.Markdown(value="", visible=False),  # Hide info
    )


# ============================================================================
# UI CONSTRUCTION
# ============================================================================

def create_gradio_interface(plate_metadata, default_fields, data_xr, pixel_scales):
    """Build the Gradio interface with all components and event wiring."""

    with gr.Blocks() as demo:
        gr.Markdown("# WaveOrder")
        gr.Markdown(
            "**Paper:** Chandler T., Ivanov I.E., Hirata-Miyasaki E., et al. \"WaveOrder: Physics-informed ML for auto-tuned multi-contrast computational microscopy from cells to organisms.\" "
            "[arXiv:2412.09775](https://arxiv.org/abs/2412.09775) (2025)\n\n"
            "**GitHub Repository:** [mehta-lab/waveorder](https://github.com/mehta-lab/waveorder)"
        )
        gr.Markdown("---")

        # FOV Selection (top of page)
        with gr.Row():
            fov_dropdown = gr.Dropdown(
                choices=default_fields,
                value=Config.DEFAULT_FIELD,
                label="Field of View",
                info=f"Select FOV from well {Config.DEFAULT_ROW}/{Config.DEFAULT_COLUMN}",
                scale=2,
            )
            load_fov_btn = gr.Button("🔄 Load FOV", variant="secondary", size="sm", scale=1)

        gr.Markdown("---")

        # Two-column layout: Image viewer (left) | Controls (right)
        with gr.Row():
            # LEFT COLUMN: Large ImageSlider (60% width)
            with gr.Column(scale=4):
                # Image viewer
                initial_preview = extract_2d_slice(
                    data_xr,
                    t=0,
                    c=Config.CHANNEL,
                    z=data_xr.sizes["Z"] // 2,
                    normalize=True,
                    verbose=False,
                )

                image_viewer = gr.ImageSlider(
                    label="Raw (left) vs Reconstructed (right) - Drag slider to compare",
                    type="numpy",
                    value=(initial_preview, initial_preview),
                    height=Config.IMAGE_HEIGHT,
                )

                gr.Markdown("---")

                # Section 2: Navigation (below image)
                gr.Markdown("### 🎛️ Navigation")
                z_slider = gr.Slider(
                    minimum=0,
                    maximum=data_xr.sizes["Z"] - 1,
                    value=data_xr.sizes["Z"] // 2,
                    step=1,
                    label="Z-slice",
                    scale=1,
                )

            # RIGHT COLUMN: All controls (40% width)
            with gr.Column(scale=2):
                # Section 3: Reconstruction Parameters
                gr.Markdown("### ⚙️ Reconstruction Parameters")

                # Sliders for optimizable parameters
                z_offset_slider = gr.Slider(
                    minimum=Config.SLIDER_RANGES["z_offset"][0],
                    maximum=Config.SLIDER_RANGES["z_offset"][1],
                    value=Config.OPTIMIZABLE_PARAMS["z_offset"][1],
                    step=Config.SLIDER_RANGES["z_offset"][2],
                    label="Z Offset (μm)",
                    info="Axial focus offset",
                )

                na_det_slider = gr.Slider(
                    minimum=Config.SLIDER_RANGES["na_detection"][0],
                    maximum=Config.SLIDER_RANGES["na_detection"][1],
                    value=Config.OPTIMIZABLE_PARAMS["numerical_aperture_detection"][1],
                    step=Config.SLIDER_RANGES["na_detection"][2],
                    label="NA Detection",
                    info="Numerical aperture of detection objective",
                )

                na_ill_slider = gr.Slider(
                    minimum=Config.SLIDER_RANGES["na_illumination"][0],
                    maximum=Config.SLIDER_RANGES["na_illumination"][1],
                    value=Config.OPTIMIZABLE_PARAMS["numerical_aperture_illumination"][1],
                    step=Config.SLIDER_RANGES["na_illumination"][2],
                    label="NA Illumination",
                    info="Numerical aperture of illumination",
                )

                tilt_zenith_slider = gr.Slider(
                    minimum=Config.SLIDER_RANGES["tilt_zenith"][0],
                    maximum=Config.SLIDER_RANGES["tilt_zenith"][1],
                    value=Config.OPTIMIZABLE_PARAMS["tilt_angle_zenith"][1],
                    step=Config.SLIDER_RANGES["tilt_zenith"][2],
                    label="Tilt Zenith (rad)",
                    info="Zenith angle of illumination tilt",
                )

                tilt_azimuth_slider = gr.Slider(
                    minimum=Config.SLIDER_RANGES["tilt_azimuth"][0],
                    maximum=Config.SLIDER_RANGES["tilt_azimuth"][1],
                    value=Config.OPTIMIZABLE_PARAMS["tilt_angle_azimuth"][1],
                    step=Config.SLIDER_RANGES["tilt_azimuth"][2],
                    label="Tilt Azimuth (rad)",
                    info="Azimuthal angle of illumination tilt",
                )

                # Reset button
                reset_params_btn = gr.Button(
                    "🔄 Reset Parameters", variant="secondary", size="sm"
                )

                gr.Markdown("---")

                # Section 4: Reconstruction Actions
                gr.Markdown("### 🔬 Phase Reconstruction")

                # Number of optimization iterations control
                num_iterations_slider = gr.Slider(
                    minimum=1,
                    maximum=50,
                    value=Config.RECON_CONFIG["num_iterations"],
                    step=1,
                    label="Optimization Iterations",
                    info="Number of gradient descent iterations (more = better quality, slower)",
                )

                with gr.Row():
                    optimize_btn = gr.Button(
                        "⚡ Optimize Parameters", variant="secondary", size="lg"
                    )
                    reconstruct_btn = gr.Button(
                        "🔬 Run Reconstruction", variant="primary", size="lg"
                    )

                gr.Markdown("---")

                # Section 5: Optimization Results
                gr.Markdown("### 📊 Optimization Results")

                loss_plot = gr.LinePlot(
                    x="iteration",
                    y="loss",
                    title="Optimization - Midband Spatial Frequency Loss",
                    height=200,
                    scale=2,
                    value=pd.DataFrame({"iteration": [], "loss": []}),  # Initialize with empty DataFrame structure
                )

                # Iteration scrubbing controls
                iteration_slider = gr.Slider(
                    minimum=1,
                    maximum=1,
                    value=1,
                    step=1,
                    label="View Iteration",
                    info="Scrub through optimization history",
                    interactive=True,  # Always interactive (just hidden until optimization)
                    visible=False,
                )
                iteration_info = gr.Markdown(value="", visible=False)

        # State storage
        iteration_history = gr.State(value=[])
        current_data_xr = gr.State(value=data_xr)
        current_pixel_scales = gr.State(value=pixel_scales)
        current_reconstructed = gr.State(value=None)  # Stores the current reconstructed image

        gr.Markdown("---")

        # Wire all event handlers
        _wire_event_handlers(
            demo,
            fov_dropdown,
            load_fov_btn,
            z_slider,
            image_viewer,
            z_offset_slider,
            na_det_slider,
            na_ill_slider,
            tilt_zenith_slider,
            tilt_azimuth_slider,
            reset_params_btn,
            num_iterations_slider,
            optimize_btn,
            reconstruct_btn,
            loss_plot,
            iteration_slider,
            iteration_info,
            iteration_history,
            current_data_xr,
            current_pixel_scales,
            current_reconstructed,
            plate_metadata,
        )

    return demo


def _wire_event_handlers(
    demo,
    fov_dropdown,
    load_fov_btn,
    z_slider,
    image_viewer,
    z_offset_slider,
    na_det_slider,
    na_ill_slider,
    tilt_zenith_slider,
    tilt_azimuth_slider,
    reset_params_btn,
    num_iterations_slider,
    optimize_btn,
    reconstruct_btn,
    loss_plot,
    iteration_slider,
    iteration_info,
    iteration_history,
    current_data_xr,
    current_pixel_scales,
    current_reconstructed,
    plate_metadata,
):
    """Wire all Gradio event handlers."""

    # FOV loading
    load_fov_btn.click(
        fn=lambda field, z: load_selected_fov(field, z, plate_metadata),
        inputs=[fov_dropdown, z_slider],
        outputs=[z_slider, image_viewer, current_data_xr, current_pixel_scales],
    )

    # Reset parameters to initial values
    def reset_parameters():
        """Reset all reconstruction parameters to their initial config values."""
        return (
            Config.OPTIMIZABLE_PARAMS["z_offset"][1],
            Config.OPTIMIZABLE_PARAMS["numerical_aperture_detection"][1],
            Config.OPTIMIZABLE_PARAMS["numerical_aperture_illumination"][1],
            Config.OPTIMIZABLE_PARAMS["tilt_angle_zenith"][1],
            Config.OPTIMIZABLE_PARAMS["tilt_angle_azimuth"][1],
        )

    reset_params_btn.click(
        fn=reset_parameters,
        inputs=[],
        outputs=[
            z_offset_slider,
            na_det_slider,
            na_ill_slider,
            tilt_zenith_slider,
            tilt_azimuth_slider,
        ],
    )

    # NA slider linking: Ensure NA_illumination <= NA_detection (physical constraint)
    # Only enforce when NA_detection changes (avoid feedback loop)
    def enforce_na_constraint(na_det_value, na_ill_value):
        """When NA_detection decreases below NA_illumination, cap NA_illumination."""
        return min(na_ill_value, na_det_value)

    na_det_slider.change(
        fn=enforce_na_constraint,
        inputs=[na_det_slider, na_ill_slider],
        outputs=[na_ill_slider],
    )

    # Image viewer for Z navigation
    # On load: show preview mode (no reconstruction yet)
    demo.load(
        fn=get_slice_for_preview,
        inputs=[z_slider, current_data_xr],
        outputs=image_viewer,
    )
    # On Z change: update only left (original) image, keep reconstruction on right
    z_slider.change(
        fn=update_original_slice_only,
        inputs=[z_slider, current_data_xr, current_reconstructed],
        outputs=image_viewer,
    )

    # Reconstruction buttons
    optimize_btn.click(
        fn=run_optimization_ui,
        inputs=[
            z_slider,
            num_iterations_slider,
            z_offset_slider,
            na_det_slider,
            na_ill_slider,
            tilt_zenith_slider,
            tilt_azimuth_slider,
            current_data_xr,
            current_pixel_scales,
        ],
        outputs=[
            image_viewer,
            loss_plot,
            iteration_history,
            iteration_slider,
            iteration_info,
            z_offset_slider,
            na_det_slider,
            na_ill_slider,
            tilt_zenith_slider,
            tilt_azimuth_slider,
            current_reconstructed,  # Update reconstructed state
        ],
    )

    reconstruct_btn.click(
        fn=run_reconstruction_ui,
        inputs=[
            z_slider,
            z_offset_slider,
            na_det_slider,
            na_ill_slider,
            tilt_zenith_slider,
            tilt_azimuth_slider,
            current_data_xr,
            current_pixel_scales,
        ],
        outputs=[image_viewer, current_reconstructed],  # Update both viewer and state
    )

    # Iteration scrubbing - updates image AND all parameter sliders
    iteration_slider.change(
        fn=scrub_iterations,
        inputs=[iteration_slider, iteration_history],
        outputs=[
            image_viewer,
            iteration_info,
            z_offset_slider,
            na_det_slider,
            na_ill_slider,
            tilt_zenith_slider,
            tilt_azimuth_slider,
        ],
    )

    # Clear iteration state when Z changes
    z_slider.change(
        fn=clear_iteration_state,
        inputs=[],
        outputs=[iteration_history, iteration_slider, iteration_info],
    )