import tempfile
from typing import List, Tuple, Optional, Dict, Any

import cv2
import gradio as gr
import numpy as np
import pandas as pd

# -----------------------------
# Global configuration
# -----------------------------

# Maximum allowed image side (pixels) to avoid OOM / heavy CPU usage
# Reduced from 2048 to 1024 for better performance (as in demo.py)
MAX_SIDE = 1024


# -----------------------------
# Utility functions
# -----------------------------

def downscale_bgr(img: np.ndarray) -> Tuple[np.ndarray, float]:
    """Downscale image so that the longest side is <= MAX_SIDE.
    
    Returns
    -------
    img_resized : np.ndarray
        Possibly downscaled BGR image.
    scale : float
        Applied scale factor (<= 1).
    """
    h, w = img.shape[:2]
    max_hw = max(h, w)
    if max_hw <= MAX_SIDE:
        return img, 1.0
    scale = MAX_SIDE / float(max_hw)
    img_resized = cv2.resize(img, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA)
    return img_resized, scale


def normalize_angle(angle: float, size_w: float, size_h: float) -> float:
    """Normalize OpenCV minAreaRect angle to [0, 180) degrees.
    
    OpenCV returns angles depending on whether width < height. We fix it so that
    the *long side* is treated as length and angle is always in [0, 180).
    """
    a = angle
    if size_w < size_h:
        a += 90.0
    a = ((a % 180.0) + 180.0) % 180.0
    return a


# -----------------------------
# Reference object detection
# -----------------------------

def build_foreground_mask(img_bgr: np.ndarray) -> np.ndarray:
    """简单的前景掩码构建（来自demo.py）"""
    h, w = img_bgr.shape[:2]
    
    # 转换到LAB颜色空间
    lab = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2LAB)
    
    # 使用四个角落估计背景颜色
    corner_size = min(h, w) // 10
    corners = [
        lab[:corner_size, :corner_size],
        lab[:corner_size, -corner_size:],
        lab[-corner_size:, :corner_size],
        lab[-corner_size:, -corner_size:]
    ]
    corner_pixels = np.vstack([c.reshape(-1, 3) for c in corners])
    bg_color = np.mean(corner_pixels, axis=0)
    
    # 计算每个像素与背景的距离
    diff = lab.astype(np.float32) - bg_color
    dist = np.sqrt(np.sum(diff * diff, axis=2))
    
    # 使用Otsu阈值分割
    dist_uint8 = np.clip(dist * 3, 0, 255).astype(np.uint8)
    _, mask = cv2.threshold(dist_uint8, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    
    # 形态学处理
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
    
    return mask


def detect_reference(
    img_bgr: np.ndarray,
    mode: str,
    ref_size_mm: Optional[float],
) -> Tuple[float, Optional[Tuple[int, int]], Optional[str], Optional[Tuple[int, int, int, int]]]:
    """检测参考物：左上角第一个物体（简化版）
    
    参数:
        img_bgr: BGR图像
        mode: 参考物模式 ("auto", "coin", "square")
        ref_size_mm: 参考物包围框边长（毫米）
    
    返回:
        px_per_mm: 像素/毫米比例
        ref_center: 参考物中心
        ref_type: 参考物类型
        ref_bbox: 参考物外接矩形
    """
    h, w = img_bgr.shape[:2]

    # 使用简单的前景掩码
    mask = build_foreground_mask(img_bgr)

    # 连通域分析
    num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask)

    # 寻找左上角的参考物
    candidates = []
    min_area = (h * w) // 500  # 最小面积
    max_area = (h * w) // 20   # 最大面积
    
    for i in range(1, num_labels):
        x, y, ww, hh, area = stats[i]
        
        # 面积过滤
        if area < min_area or area > max_area:
            continue
            
        # 位置过滤：必须在左上角区域
        if x > w * 0.4 or y > h * 0.4:
            continue
            
        # 形状过滤：参考物应该接近正方形
        aspect_ratio = max(ww, hh) / (min(ww, hh) + 1e-6)
        if aspect_ratio > 3.0:
            continue

        cx, cy = centroids[i]
        # 按位置排序：越靠近左上角越好
        score = x + y
        candidates.append((score, i, (x, y, ww, hh), area, (int(cx), int(cy))))

    if not candidates:
        # 如果没有找到参考物，使用安全的默认值
        px_per_mm = 4.0
        center = None
        ref_type = None
        bbox = None
        return px_per_mm, center, ref_type, bbox

    # 选择最左上角的候选物
    candidates.sort(key=lambda c: c[0])
    score, label_idx, bbox, area, center = candidates[0]
    
    x, y, ww, hh = bbox

    # 计算像素/毫米比例
    ref_size = ref_size_mm if ref_size_mm and ref_size_mm > 0 else 25.0
    ref_bbox_size_px = max(ww, hh)
    px_per_mm = ref_bbox_size_px / ref_size

    return px_per_mm, center, "square", (x, y, ww, hh)


# -----------------------------
# Segmentation & measurements
# -----------------------------

def build_mask_hsv(
    img_bgr: np.ndarray,
    sample_type: str,
    hsv_low_h: int,
    hsv_high_h: int,
    color_tol: int,
) -> np.ndarray:
    """Build binary mask using HSV thresholds."""
    hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)
    h_channel = hsv[:, :, 0]
    s_channel = hsv[:, :, 1]
    v_channel = hsv[:, :, 2]

    if sample_type == "leaves":
        low_h = int(max(0, hsv_low_h))
        high_h = int(min(179, hsv_high_h))
        # H range
        mask_h = cv2.inRange(h_channel, low_h, high_h)
        # Remove very desaturated or very dark pixels
        mask_s = cv2.inRange(s_channel, 30, 255)
        mask_v = cv2.inRange(v_channel, 30, 255)
        mask = cv2.bitwise_and(mask_h, cv2.bitwise_and(mask_s, mask_v))
    else:
        # seeds / grains: keep non-white pixels
        mask_s = cv2.inRange(s_channel, 20, 255)
        mask_v = cv2.inRange(v_channel, 20, 255)
        mask = cv2.bitwise_and(mask_s, mask_v)

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
    return mask


def segment(
    img_bgr: np.ndarray,
    sample_type: str,
    hsv_low_h: int,
    hsv_high_h: int,
    color_tol: int,
    min_area_px: float,
    max_area_px: float,
) -> List[Dict[str, Any]]:
    """Segment objects and compute basic geometric descriptors.
    采用demo.py的简化分割算法，但保留HSV参数兼容性
    """
    # 使用简单的前景掩码（demo.py方法）
    mask = build_foreground_mask(img_bgr)
    
    # 连通域分析
    num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask)
    
    components: List[Dict[str, Any]] = []
    
    # 按位置排序，跳过第一个（通常是参考物）
    all_objects = []
    for i in range(1, num_labels):
        x, y, ww, hh, area = stats[i]
        
        # 面积过滤
        if area < min_area_px or area > max_area_px:
            continue
        
        cx, cy = centroids[i]
        # 简单的位置评分：从左到右
        score = x + y * 0.1  # 优先考虑x坐标
        all_objects.append((score, i, (x, y, ww, hh), area, (int(cx), int(cy))))
    
    if len(all_objects) == 0:
        return []
    
    # 排序并跳过第一个（参考物）
    all_objects.sort(key=lambda obj: obj[0])
    
    # 简单判断是否跳过第一个对象
    skip_first = False
    if len(all_objects) > 0:
        _, _, (x, y, ww, hh), area, _ = all_objects[0]
        h, w = img_bgr.shape[:2]
        
        # 如果第一个对象在左上角且形状合理，跳过它
        is_topleft = (x < w * 0.3 and y < h * 0.3)
        aspect_ratio = max(ww, hh) / (min(ww, hh) + 1e-6)
        is_reasonable_shape = aspect_ratio < 3.0
        
        skip_first = is_topleft and is_reasonable_shape
    
    # 处理对象
    start_idx = 1 if skip_first else 0
    for obj_data in all_objects[start_idx:]:
        _, label_idx, bbox, area, center = obj_data
        
        # 提取轮廓
        component_mask = (labels == label_idx).astype(np.uint8) * 255
        cnts, _ = cv2.findContours(component_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        
        if len(cnts) == 0:
            continue
        
        cnt = cnts[0]
        
        # 计算几何特征
        rect = cv2.minAreaRect(cnt)
        box = cv2.boxPoints(rect).astype(np.int32)
        
        peri = cv2.arcLength(cnt, True)
        
        # 修复OpenCV minAreaRect的长短轴对应问题（使用PCA）
        # 提取轮廓点
        contour_points = cnt.reshape(-1, 2).astype(np.float32)
        
        # 计算质心
        cx = np.mean(contour_points[:, 0])
        cy = np.mean(contour_points[:, 1])
        
        # 计算协方差矩阵
        centered_points = contour_points - np.array([cx, cy])
        cov_matrix = np.cov(centered_points.T)
        
        # 计算特征值和特征向量
        eigenvalues, eigenvectors = np.linalg.eigh(cov_matrix)
        
        # 按特征值大小排序（降序）
        idx = np.argsort(eigenvalues)[::-1]
        eigenvalues = eigenvalues[idx]
        eigenvectors = eigenvectors[:, idx]
        
        # 主方向（最大特征值对应的特征向量）
        main_direction = eigenvectors[:, 0]
        
        # 投影到主方向和次方向
        proj_main = np.dot(centered_points, main_direction)
        proj_secondary = np.dot(centered_points, eigenvectors[:, 1])
        
        # 计算投影边界
        min_main = np.min(proj_main)
        max_main = np.max(proj_main)
        min_secondary = np.min(proj_secondary)
        max_secondary = np.max(proj_secondary)
        
        # 计算真实的长短轴长度
        length_main = max_main - min_main
        length_secondary = max_secondary - min_secondary
        
        # 确保长轴对应较长的方向，并保存正确的投影边界
        if length_main >= length_secondary:
            w_obb = length_main
            h_obb = length_secondary
            angle = np.arctan2(main_direction[1], main_direction[0]) * 180.0 / np.pi
            # 长轴是主方向
            long_direction = main_direction
            short_direction = eigenvectors[:, 1]
            min_long_proj = min_main
            max_long_proj = max_main
            min_short_proj = min_secondary
            max_short_proj = max_secondary
        else:
            w_obb = length_secondary  
            h_obb = length_main
            secondary_direction = eigenvectors[:, 1]
            angle = np.arctan2(secondary_direction[1], secondary_direction[0]) * 180.0 / np.pi
            # 长轴是次方向
            long_direction = eigenvectors[:, 1]
            short_direction = main_direction
            min_long_proj = min_secondary
            max_long_proj = max_secondary
            min_short_proj = min_main
            max_short_proj = max_main
        
        # 标准化角度到[0, 180)
        angle = ((angle % 180.0) + 180.0) % 180.0
        
        components.append({
            "contour": cnt,
            "rect": rect,
            "box": box,
            "area_px": float(area),
            "peri_px": float(peri),
            "center": (int(cx), int(cy)),  # 使用PCA计算的质心
            "pca_center": (cx, cy),        # 保存精确的PCA质心
            "angle": float(angle),
            "length_px": float(w_obb),
            "width_px": float(h_obb),
            # 保存投影边界信息用于正确的包围框绘制
            "min_long_proj": float(min_long_proj),
            "max_long_proj": float(max_long_proj),
            "min_short_proj": float(min_short_proj),
            "max_short_proj": float(max_short_proj),
        })
    
    return components


def compute_color_metrics(img_bgr: np.ndarray, mask: np.ndarray) -> Tuple[float, float, float, int, int, int, float, float]:
    """Compute mean RGB / HSV and simple color indices in a mask region."""
    mean_bgr = cv2.mean(img_bgr, mask=mask)
    mean_b, mean_g, mean_r = mean_bgr[0], mean_bgr[1], mean_bgr[2]

    rgb = np.array([[[mean_r, mean_g, mean_b]]], dtype=np.uint8)
    hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV)[0, 0]
    h, s, v = int(hsv[0]), int(hsv[1]), int(hsv[2])

    denom = (mean_r + mean_g + mean_b + 1e-6)
    green_index = (2.0 * mean_g - mean_r - mean_b) / denom
    brown_index = (mean_r - mean_b) / denom
    return mean_r, mean_g, mean_b, h, s, v, green_index, brown_index


def compute_metrics(
    img_bgr: np.ndarray,
    components: List[Dict[str, Any]],
    px_per_mm: float,
) -> pd.DataFrame:
    """Compute all morphological + color metrics for each component."""
    rows: List[Dict[str, Any]] = []

    for i, comp in enumerate(components, start=1):
        # 使用新的length_px和width_px字段
        length_mm = comp["length_px"] / px_per_mm
        width_mm = comp["width_px"] / px_per_mm
        area_mm2 = comp["area_px"] / (px_per_mm * px_per_mm)
        perimeter_mm = comp["peri_px"] / px_per_mm

        aspect_ratio = length_mm / (width_mm + 1e-6)
        
        # 计算圆形度 (4π*面积/周长²)
        circularity = (4.0 * np.pi * area_mm2) / (perimeter_mm * perimeter_mm + 1e-6)

        # 计算颜色指标
        mask_single = np.zeros(img_bgr.shape[:2], dtype=np.uint8)
        cv2.drawContours(mask_single, [comp["contour"]], -1, 255, thickness=-1)
        mean_r, mean_g, mean_b, h, s, v, gi, bi = compute_color_metrics(img_bgr, mask_single)

        rows.append(
            {
                "label": f"s{i}",
                "centerX_px": int(comp["center"][0]),
                "centerY_px": int(comp["center"][1]),
                "length_mm": round(length_mm, 2),
                "width_mm": round(width_mm, 2),
                "area_mm2": round(area_mm2, 2),
                "perimeter_mm": round(perimeter_mm, 2),
                "aspect_ratio": round(aspect_ratio, 2),
                "circularity": round(circularity, 3),
                "angle_deg": round(float(comp["angle"]), 1),
                "meanR": int(round(mean_r)),
                "meanG": int(round(mean_g)),
                "meanB": int(round(mean_b)),
                "hue": h,
                "saturation": s,
                "value": v,
                "greenIndex": round(float(gi), 3),
                "brownIndex": round(float(bi), 3),
            }
        )

    if not rows:
        return pd.DataFrame()
    return pd.DataFrame(rows)


def render_overlay(
    img_bgr: np.ndarray,
    px_per_mm: float,
    ref: Tuple[Optional[Tuple[int, int]], Optional[str]],
    components: List[Dict[str, Any]],
    df: pd.DataFrame,
    ref_bbox: Optional[Tuple[int, int, int, int]] = None,
) -> np.ndarray:
    """Draw reference + sample annotations on the image.
    采用demo.py的清晰可视化方法
    """
    out = img_bgr.copy()

    # 绘制参考物（红色矩形框）
    ref_center, ref_type = ref
    if ref_bbox is not None:
        x, y, w, h = ref_bbox
        cv2.rectangle(out, (int(x), int(y)), (int(x + w), int(y + h)), (0, 0, 255), 2)
        cv2.putText(
            out,
            "REF",
            (int(x), int(y) - 10),
            cv2.FONT_HERSHEY_SIMPLEX,
            0.6,
            (0, 0, 255),
            2,
            cv2.LINE_AA,
        )

    # 绘制样品物体（完整标注）
    for i, comp in enumerate(components, start=1):
        # 1. 绘制完整轮廓（蓝色，加粗）
        cv2.drawContours(out, [comp["contour"]], -1, (255, 0, 0), 3)
        
        # 2. 绘制修正后的OBB包围框
        # 使用PCA计算的精确质心
        cx, cy = comp["pca_center"]
        length_px = comp["length_px"]  # 长轴长度
        width_px = comp["width_px"]    # 短轴长度
        angle_deg = comp["angle"]      # 长轴角度（度）
        
        # 转换为弧度
        angle_rad = np.radians(angle_deg)
        
        # 使用实际的投影边界构建包围框
        corners = []
        
        # 获取保存的投影边界
        min_long_proj = comp["min_long_proj"]
        max_long_proj = comp["max_long_proj"]
        min_short_proj = comp["min_short_proj"]
        max_short_proj = comp["max_short_proj"]
        
        # 获取长轴和短轴方向向量
        long_dir = np.array([np.cos(angle_rad), np.sin(angle_rad)])
        short_dir = np.array([-np.sin(angle_rad), np.cos(angle_rad)])
        
        # 使用实际投影边界构建包围框的四个角点
        for long_proj, short_proj in [(max_long_proj, max_short_proj),    # 右上
                                      (min_long_proj, max_short_proj),    # 左上  
                                      (min_long_proj, min_short_proj),    # 左下
                                      (max_long_proj, min_short_proj)]:   # 右下
            # 从质心出发，沿长轴和短轴方向移动到角点
            corner_point = np.array([cx, cy]) + long_proj * long_dir + short_proj * short_dir
            corners.append([int(corner_point[0]), int(corner_point[1])])
        
        # 绘制OBB包围框
        corners = np.array(corners, dtype=np.int32)
        cv2.drawContours(out, [corners], -1, (255, 0, 0), 2)
        
        # 3. 绘制长短轴（包围框的边界线）
        # 计算包围框各边的中点
        edge_mids = []
        for edge_idx in range(4):
            next_edge_idx = (edge_idx + 1) % 4
            mid_x = (corners[edge_idx][0] + corners[next_edge_idx][0]) / 2
            mid_y = (corners[edge_idx][1] + corners[next_edge_idx][1]) / 2
            edge_mids.append((int(mid_x), int(mid_y)))
        
        # 计算各边的长度来确定哪条是长边
        edge_lengths = []
        for edge_idx in range(4):
            next_edge_idx = (edge_idx + 1) % 4
            length = np.sqrt((corners[next_edge_idx][0] - corners[edge_idx][0])**2 + (corners[next_edge_idx][1] - corners[edge_idx][1])**2)
            edge_lengths.append(length)
        
        # 找到最长的边
        max_edge_idx = np.argmax(edge_lengths)
        opposite_edge_idx = (max_edge_idx + 2) % 4
        
        # 绘制长轴（连接最长边的中点和对边中点）
        long_mid1 = edge_mids[max_edge_idx]
        long_mid2 = edge_mids[opposite_edge_idx]
        cv2.line(out, long_mid1, long_mid2, (255, 0, 0), 3)
        
        # 绘制短轴（连接另外两边的中点）
        short_edge1_idx = (max_edge_idx + 1) % 4
        short_edge2_idx = (max_edge_idx + 3) % 4
        short_mid1 = edge_mids[short_edge1_idx]
        short_mid2 = edge_mids[short_edge2_idx]
        cv2.line(out, short_mid1, short_mid2, (255, 0, 0), 2)

        # 4. 绘制中心点和标签
        # 使用PCA计算的精确质心
        label_cx, label_cy = comp["pca_center"]
        cv2.circle(out, (int(label_cx), int(label_cy)), 15, (0, 0, 0), -1)
        cv2.putText(
            out,
            f"s{i}",
            (int(label_cx) - 10, int(label_cy) + 5),
            cv2.FONT_HERSHEY_SIMPLEX,
            0.5,
            (255, 255, 255),
            2,
            cv2.LINE_AA,
        )

    return cv2.cvtColor(out, cv2.COLOR_BGR2RGB)


def analyze(
    image: Optional[np.ndarray],
    sample_type: str,
    expected_count: int,
    ref_mode: str,
    ref_size_mm: float,
    min_area_px: float,
    max_area_px: float,
    color_tol: int,
    hsv_low_h: int,
    hsv_high_h: int,
) -> Tuple[Optional[np.ndarray], pd.DataFrame, Optional[str], List[Dict[str, Any]], Dict[str, Any]]:
    """主分析函数，整合demo.py的优化算法"""
    try:
        if image is None:
            return None, pd.DataFrame(), None, [], {}
        
        # 转换为BGR
        img_rgb = np.array(image)
        img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
        
        # 适度降采样
        img_bgr, scale = downscale_bgr(img_bgr)
        
        # 检测参考物（左上角第一个物体）
        px_per_mm, ref_center, ref_type, ref_bbox = detect_reference(img_bgr, ref_mode, ref_size_mm)
        
        # 分割所有样品
        comps = segment(
            img_bgr,
            sample_type=sample_type,
            hsv_low_h=hsv_low_h,
            hsv_high_h=hsv_high_h,
            color_tol=color_tol,
            min_area_px=min_area_px,
            max_area_px=max_area_px,
        )
        
        # 根据样品类型排序
        if sample_type == "leaves":
            comps.sort(key=lambda c: c["center"][0])
        else:
            comps.sort(key=lambda c: c["center"][1] * 0.3 + c["center"][0] * 0.7)
        
        # 限制数量
        if expected_count and expected_count > 0:
            comps = comps[:int(expected_count)]
        
        # 计算测量指标
        df = compute_metrics(img_bgr, comps, px_per_mm)
        
        # 绘制标注图像
        overlay = render_overlay(
            img_bgr.copy(), 
            px_per_mm, 
            (ref_center, ref_type), 
            comps, 
            df, 
            ref_bbox
        )
        
        # 保存CSV
        csv = df.to_csv(index=False)
        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
        tmp.write(csv.encode("utf-8"))
        tmp.close()
        
        # 转换为JSON
        js = df.to_dict(orient="records")
        
        # 存储状态用于交互修正
        state_dict: Dict[str, Any] = {
            "img_bgr": img_bgr,
            "sample_type": sample_type,
            "px_per_mm": px_per_mm,
            "ref_center": ref_center,
            "ref_type": ref_type,
            "ref_bbox": ref_bbox,
            "components": comps,
            "expected_count": expected_count,
            "ref_size_mm": ref_size_mm,
        }
        # 默认所有组件都是活跃样品
        state_dict["active_indices"] = list(range(len(comps)))

        return overlay, df, tmp.name, js, state_dict
    except Exception as e:
        return None, pd.DataFrame(), None, [{"error": str(e)}], {}


# --- Interactive correction helper ---
def apply_corrections(
    click_event,
    state_dict: Dict[str, Any],
    correction_mode: str,
) -> Tuple[Dict[str, Any], Optional[np.ndarray], pd.DataFrame, Optional[str], List[Dict[str, Any]]]:
    """
    Apply interactive corrections based on a click on the annotated image.
    correction_mode:
      - "none": do nothing
      - "set-ref": treat the clicked object as the new reference
      - "toggle-sample": toggle the clicked object between active/inactive sample
    """
    # If no valid state or no correction requested, do nothing
    if not state_dict or "img_bgr" not in state_dict or correction_mode == "none" or click_event is None:
        return state_dict, None, pd.DataFrame(), None, []

    try:
        # Gradio SelectData usually provides (x, y) in .index
        if hasattr(click_event, "index"):
            x, y = click_event.index
        else:
            # Fallback: assume click_event is a tuple
            x, y = click_event

        img_bgr = state_dict["img_bgr"]
        components: List[Dict[str, Any]] = state_dict.get("components", [])
        if not components:
            return state_dict, None, pd.DataFrame(), None, []

        # Find nearest component center to the click
        min_dist = 1e9
        nearest_idx = -1
        for i, comp in enumerate(components):
            cx, cy = comp["center"]
            d = (cx - x) ** 2 + (cy - y) ** 2
            if d < min_dist:
                min_dist = d
                nearest_idx = i

        if nearest_idx < 0:
            return state_dict, None, pd.DataFrame(), None, []

        px_per_mm = state_dict.get("px_per_mm", 4.0)
        ref_center = state_dict.get("ref_center")
        ref_type = state_dict.get("ref_type", "square")
        ref_bbox = state_dict.get("ref_bbox")
        ref_size_mm = state_dict.get("ref_size_mm", 20.0)
        sample_type = state_dict.get("sample_type", "leaves")

        active_indices = state_dict.get("active_indices", list(range(len(components))))

        if correction_mode == "set-ref":
            # Use this component as the new reference object
            comp = components[nearest_idx]
            box = comp["box"]
            xs = box[:, 0]
            ys = box[:, 1]
            x0, y0 = int(xs.min()), int(ys.min())
            w0, h0 = int(xs.max() - xs.min()), int(ys.max() - ys.min())
            ref_bbox = (x0, y0, w0, h0)
            ref_center = (int(comp["center"][0]), int(comp["center"][1]))

            # Update px_per_mm using the largest side as diameter/side length
            side_px = float(max(w0, h0))
            px_per_mm = max(side_px / (ref_size_mm if ref_size_mm > 0 else 20.0), 1e-6)
            ref_type = "square"

            # Remove this component from active samples (reference is not a sample)
            new_components = []
            for i, c in enumerate(components):
                if i != nearest_idx:
                    new_components.append(c)
            components = new_components
            # Rebuild active_indices to cover all remaining components
            active_indices = list(range(len(components)))

            state_dict["components"] = components
            state_dict["ref_bbox"] = ref_bbox
            state_dict["ref_center"] = ref_center
            state_dict["px_per_mm"] = px_per_mm
            state_dict["ref_type"] = ref_type
            state_dict["active_indices"] = active_indices

        elif correction_mode == "toggle-sample":
            # Toggle this component in/out of the active sample set
            if nearest_idx in active_indices:
                active_indices = [idx for idx in active_indices if idx != nearest_idx]
            else:
                active_indices.append(nearest_idx)
                active_indices = sorted(set(active_indices))
            state_dict["active_indices"] = active_indices

        # Rebuild the list of active components
        active_components = [components[i] for i in active_indices]

        # Recompute metrics and overlay using the updated state
        df = compute_metrics(img_bgr, active_components, px_per_mm)
        overlay = render_overlay(
            img_bgr.copy(),
            px_per_mm,
            (state_dict.get("ref_center"), state_dict.get("ref_type")),
            active_components,
            df,
            state_dict.get("ref_bbox"),
        )
        csv = df.to_csv(index=False)
        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
        tmp.write(csv.encode("utf-8"))
        tmp.close()
        js = df.to_dict(orient="records")

        return state_dict, overlay, df, tmp.name, js
    except Exception:
        # In case of any error, do not break the app; just keep current state
        return state_dict, None, pd.DataFrame(), None, []


with gr.Blocks(theme=gr.themes.Default()) as demo:
    gr.Markdown("# Biological Sample Quantifier (Leaves / Seeds)")
    state = gr.State({})
    with gr.Row():
        with gr.Column(scale=1):
            image = gr.Image(type="numpy", label="Upload image")
            sample_type = gr.Radio(["leaves", "seeds-grains"], value="leaves", label="Sample type")
            expected = gr.Slider(1, 500, value=5, step=1, label="Expected count")
            ref_mode = gr.Radio(["auto", "coin", "square"], value="auto", label="Reference mode")
            ref_size = gr.Slider(1, 100, value=25.0, step=0.1, label="Reference size (mm)")
            min_area = gr.Slider(10, 5000, value=500, step=10, label="Min area (px²)")
            max_area = gr.Slider(1000, 200000, value=50000, step=1000, label="Max area (px²)")
            color_tol = gr.Slider(5, 100, value=40, step=1, label="Color tolerance")
            hsv_low = gr.Slider(0, 179, value=35, step=1, label="HSV H lower (leaves)")
            hsv_high = gr.Slider(0, 179, value=85, step=1, label="HSV H upper (leaves)")
            correction_mode = gr.Radio(
                ["none", "set-ref", "toggle-sample"],
                value="none",
                label="Correction mode (click on image)"
            )
            run = gr.Button("Analyze")
            reset = gr.Button("Reset")
        with gr.Column(scale=2):
            overlay = gr.Image(label="Annotated", interactive=True)
            table = gr.Dataframe(label="Metrics", wrap=True)
            csv_out = gr.File(label="CSV export")
            json_out = gr.JSON(label="JSON preview")
    
    def _analyze(image, sample_type, expected, ref_mode, ref_size, min_area, max_area, color_tol, hsv_low, hsv_high):
        overlay_img, df, csv_path, js, state_dict = analyze(
            image, sample_type, expected, ref_mode, ref_size, min_area, max_area, color_tol, hsv_low, hsv_high
        )
        return overlay_img, df, csv_path, js, state_dict

    run.click(
        _analyze,
        [image, sample_type, expected, ref_mode, ref_size, min_area, max_area, color_tol, hsv_low, hsv_high],
        [overlay, table, csv_out, json_out, state],
    )

    def _reset():
        return None, pd.DataFrame(), None, [], {}

    reset.click(_reset, None, [overlay, table, csv_out, json_out, state])

    def _on_select(evt, current_state, correction_mode):
        # Apply corrections based on a click on the annotated image
        new_state, overlay_img, df, csv_path, js = apply_corrections(evt, current_state or {}, correction_mode)
        # If overlay_img is None, keep the existing outputs unchanged by returning gr.update()
        if overlay_img is None:
            return gr.update(), gr.update(), gr.update(), gr.update(), new_state
        return overlay_img, df, csv_path, js, new_state

    overlay.select(
        _on_select,
        [state, correction_mode],
        [overlay, table, csv_out, json_out, state],
    )

if __name__ == "__main__":
    demo.launch()