Upload app.py

app.py

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+import gradio as gr
+import spaces
+from cellpose import models
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import tempfile
+from PIL import Image, ImageDraw
+import io
+from huggingface_hub import hf_hub_download
+import base64 
+
+HF_REPO_ID = "myang4218/cellposemodel"
+MODEL_OPTIONS = {
+    "Hemocytometer Model": "hemocytometermodel.npy",
+    "General Model": "generalmodel.npy"
+}
+
+loaded_models = {}
+
+# ---- mobile-safe size limits (aggressive for Safari) ----
+MAX_SIDE = 1024          
+MAX_PIXELS = 1024 * 1024
+
+
+def safe_resize(image_np):
+    """
+    Downscale image to fit within MAX_SIDE and MAX_PIXELS while
+    preserving aspect ratio. Works for RGB / RGBA / grayscale.
+    """
+    h, w = image_np.shape[:2]
+    total = h * w
+
+    if max(h, w) <= MAX_SIDE and total <= MAX_PIXELS:
+        return image_np
+
+    # compute scale 
+    scale_side = MAX_SIDE / max(h, w)
+    scale_pixels = (MAX_PIXELS / total) ** 0.5
+    scale = min(scale_side, scale_pixels)
+
+    new_w = max(1, int(w * scale))
+    new_h = max(1, int(h * scale))
+
+    return cv2.resize(image_np, (new_w, new_h), interpolation=cv2.INTER_AREA)
+
+
+def draw_exclusion_overlay(image_np, left_width_pct, top_width_pct):
+    
+    h, w = image_np.shape[:2]
+    
+    # Convert to PIL for drawing
+    img_pil = Image.fromarray(image_np)
+    draw = ImageDraw.Draw(img_pil, 'RGBA')
+    
+    # Calculate pixel widths from percentages
+    left_px = int(w * left_width_pct / 100)
+    top_px = int(h * top_width_pct / 100)
+    
+    # Draw overlays for exclusion zones
+    if left_px > 0:
+        # Left exclusion zone
+        draw.rectangle(
+            [(0, 0), (left_px, h)],
+            fill=(255, 0, 0, 80)  # Semi-transparent red
+        )
+        # border line
+        draw.line([(left_px, 0), (left_px, h)], fill=(255, 0, 0, 255), width=3)
+    
+    if top_px > 0:
+        # Top exclusion zone
+        draw.rectangle(
+            [(0, 0), (w, top_px)],
+            fill=(255, 0, 0, 80)  # Semi-transparent red
+        )
+        # border line
+        draw.line([(0, top_px), (w, top_px)], fill=(255, 0, 0, 255), width=3)
+    
+    return np.array(img_pil)
+
+
+def apply_stereological_exclusion(masks, left_width_pct, top_width_pct):
+    h, w = masks.shape
+    
+    # Calculate pixel widths from percentages
+    left_px = int(w * left_width_pct / 100)
+    top_px = int(h * top_width_pct / 100)
+    
+    filtered_masks = masks.copy()
+    cell_ids = np.unique(masks)
+    cell_ids = cell_ids[cell_ids > 0]
+    
+    excluded_cells = []
+    included_cells = []
+    
+    for cell_id in cell_ids:
+        cell_mask = (masks == cell_id)
+        
+        # Get cell boundary coordinates
+        rows, cols = np.where(cell_mask)
+        
+        # Check if cell touches left exclusion zone
+        touches_left = np.any(cols < left_px) if left_px > 0 else False
+        
+        # Check if cell touches top exclusion zone
+        touches_top = np.any(rows < top_px) if top_px > 0 else False
+        
+        # Exclude if touching left or top
+        if touches_left or touches_top:
+            filtered_masks[cell_mask] = 0
+            excluded_cells.append(cell_id)
+        else:
+            included_cells.append(cell_id)
+    
+    # Renumber remaining cells
+    unique_ids = np.unique(filtered_masks)
+    unique_ids = unique_ids[unique_ids > 0]
+    
+    renumbered_masks = np.zeros_like(filtered_masks)
+    for new_id, old_id in enumerate(unique_ids, start=1):
+        renumbered_masks[filtered_masks == old_id] = new_id
+    
+    return renumbered_masks, len(excluded_cells), len(included_cells)
+
+
+def classify_cells_by_blueness(image_np, masks, threshold_bias):
+    """
+    Classify cells as dead (blue) or alive using an adaptive Otsu threshold
+    on per-cell blueness scores, with a user bias to fine-tune.
+
+    Args:
+        image_np:        RGB image array
+        masks:           Cellpose segmentation masks
+        threshold_bias:  Slider value -50..+50; shifts Otsu threshold up/down.
+                         Negative = more cells classified dead (looser).
+                         Positive = fewer cells classified dead (stricter).
+                         0 = pure Otsu (fully automatic).
+
+    Returns:
+        dead_count, alive_count, colored_overlay, otsu_threshold, final_threshold
+    """
+
+    if len(image_np.shape) == 2:
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_GRAY2RGB)
+    elif len(image_np.shape) == 3 and image_np.shape[2] == 4:
+        image_np = cv2.cvtColor(image_np, cv2.COLOR_RGBA2RGB)
+
+    hsv = cv2.cvtColor(image_np, cv2.COLOR_RGB2HSV)
+
+    hue        = hsv[:, :, 0].astype(np.float32)
+    saturation = hsv[:, :, 1].astype(np.float32)
+
+    # Raw blueness: hue proximity to 115° × saturation
+    hue_distance = np.minimum(np.abs(hue - 115), 180 - np.abs(hue - 115))
+    hue_score    = np.maximum(0, 1 - hue_distance / 65)
+    blueness     = hue_score * (saturation / 255.0)
+
+    # --- Compute per-cell mean blueness scores ---
+    cell_ids = np.unique(masks)
+    cell_ids = cell_ids[cell_ids > 0]
+
+    if len(cell_ids) == 0:
+        blank = image_np.copy()
+        return 0, 0, blank, 0.0, 0.0
+
+    cell_scores = np.array([np.mean(blueness[masks == cid]) for cid in cell_ids])
+
+    # --- Otsu on the distribution of per-cell scores ---
+    # cv2.threshold expects uint8; scale 0-1 → 0-255
+    scores_u8 = (np.clip(cell_scores, 0, 1) * 255).astype(np.uint8)
+
+    if scores_u8.max() == scores_u8.min():
+        # All cells identical → Otsu is undefined; use midpoint
+        otsu_threshold = float(scores_u8[0]) / 255.0
+    else:
+        # Reshape to a single-column image so cv2.threshold works
+        thresh_val, _ = cv2.threshold(
+            scores_u8.reshape(-1, 1), 0, 255,
+            cv2.THRESH_BINARY + cv2.THRESH_OTSU
+        )
+        otsu_threshold = thresh_val / 255.0
+
+    # --- Apply user bias: slider -50..+50 maps to ±0.20 shift ---
+    bias = (threshold_bias / 50.0) * 0.20
+    final_threshold = float(np.clip(otsu_threshold + bias, 0.0, 1.0))
+
+    # --- Classify ---
+    dead_cells  = [cid for cid, s in zip(cell_ids, cell_scores) if s > final_threshold]
+    alive_cells = [cid for cid, s in zip(cell_ids, cell_scores) if s <= final_threshold]
+
+    # --- Colored overlay ---
+    overlay = image_np.copy().astype(np.float32)
+    for cid in dead_cells:
+        overlay[masks == cid] = [255, 0, 0]
+    for cid in alive_cells:
+        overlay[masks == cid] = [0, 255, 0]
+
+    alpha        = 0.4
+    final_overlay = (1 - alpha) * image_np.astype(np.float32) + alpha * overlay
+    final_overlay = np.clip(final_overlay, 0, 255).astype(np.uint8)
+
+    return len(dead_cells), len(alive_cells), final_overlay, otsu_threshold, final_threshold
+
+
+def measure_confluency(masks, image_np):
+    tot_pixels = image_np.shape[0] * image_np.shape[1]
+    cell_pixels = np.count_nonzero(masks)
+    confluency = cell_pixels / tot_pixels * 100
+    return confluency
+    
+def filter_mask_by_size(masks, minimum_pixels):
+    filtered_masks = masks.copy()
+    cell_ids = np.unique(masks)
+    cell_ids = cell_ids[cell_ids > 0]
+
+    removed_count = 0
+    
+    for cell_id in cell_ids:
+        cell_mask = (masks == cell_id)
+        cell_pixels = np.count_nonzero(cell_mask)
+        if cell_pixels < minimum_pixels:
+            filtered_masks[cell_mask] = 0
+            removed_count += 1
+
+    unique_ids = np.unique(filtered_masks)
+    unique_ids = unique_ids[unique_ids > 0]
+
+    renumbered_masks = np.zeros_like(filtered_masks)
+    for new_id, old_id in enumerate(unique_ids, start=1):
+        renumbered_masks[filtered_masks == old_id] = new_id
+
+    return renumbered_masks, removed_count
+
+
+def filter_mask_by_maxsize(masks, maximum_pixels):
+    filtered_masks = masks.copy()
+    cell_ids = np.unique(masks)
+    cell_ids = cell_ids[cell_ids > 0]
+
+    removed_count = 0
+    for cell_id in cell_ids:
+        cell_mask = (masks == cell_id)
+        cell_pixels = np.count_nonzero(cell_mask)
+        if cell_pixels > maximum_pixels:
+            filtered_masks[cell_mask] = 0
+            removed_count += 1
+
+    unique_ids = np.unique(filtered_masks)
+    unique_ids = unique_ids[unique_ids > 0]
+
+    renumbered_masks = np.zeros_like(filtered_masks)
+    for new_id, old_id in enumerate(unique_ids, start=1):
+        renumbered_masks[filtered_masks == old_id] = new_id
+
+    return renumbered_masks, removed_count
+
+
+def rec_min_size(masks, q=25):
+    ids = np.unique(masks)
+    ids = ids[ids > 0]
+    if len(ids) == 0:
+        return 0
+    sizes = np.array([np.count_nonzero(masks == cid) for cid in ids])
+    return int(round(np.percentile(sizes, q)))
+
+
+def toggle_stereological_mode(use_stereology):
+    """Show/hide stereological controls based on checkbox"""
+    return gr.update(visible=use_stereology)
+
+
+def update_exclusion_preview(image, left_width, top_width):
+    """Update the preview image with exclusion zone overlay"""
+    if image is None:
+        return None
+    
+    image_np = np.array(image)
+    overlay = draw_exclusion_overlay(image_np, left_width, top_width)
+    return Image.fromarray(overlay)
+
+
+@spaces.GPU
+def run_segmentation(image, model_choice, min_cell_size, max_cell_size, 
+                     use_stereology, left_exclusion, top_exclusion):
+    image_np = np.array(image)
+    image_np = safe_resize(image_np)
+    
+    try:
+        model_filename = MODEL_OPTIONS[model_choice]
+        model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=model_filename)
+
+        if model_filename in loaded_models:
+            model = loaded_models[model_filename]
+        else:
+            model = models.CellposeModel(gpu=True, pretrained_model=model_path)
+            loaded_models[model_filename] = model
+
+        # Process image format to RGB
+        if len(image_np.shape) == 2:
+            processed_image_np = cv2.cvtColor(image_np, cv2.COLOR_GRAY2RGB)
+        elif len(image_np.shape) == 3 and image_np.shape[2] == 4:
+            processed_image_np = cv2.cvtColor(image_np, cv2.COLOR_RGBA2RGB)
+        else:
+            processed_image_np = image_np
+
+        # Run Cellpose segmentation
+        masks_raw, flows, styles = model.eval(processed_image_np, diameter=None, channels=[0, 0])
+
+        ids = np.unique(masks_raw)
+        ids = ids[ids > 0]
+
+        sizes = np.array([np.count_nonzero(masks_raw == cid) for cid in ids])
+
+        print("num_cells:", len(ids))
+        print("mean:", sizes.mean() if len(sizes) > 0 else 0)
+        print("median:", np.median(sizes) if len(sizes) > 0 else 0)
+        print("p90:", np.percentile(sizes, 90) if len(sizes) > 0 else 0)
+        print("max:", sizes.max() if len(sizes) > 0 else 0)
+        
+        # Compute recommendation from RAW masks 
+        recommend_min = rec_min_size(masks_raw)
+
+        # If user sets slider to 0, use the recommendation
+        min_used = recommend_min if (min_cell_size == 0) else int(min_cell_size)
+
+        # Apply filters
+        masks = masks_raw.copy()
+        removed_small = 0
+        removed_large = 0
+
+        if min_used > 0:
+            masks, removed_small = filter_mask_by_size(masks, min_used)
+
+        if max_cell_size > 0:
+            masks, removed_large = filter_mask_by_maxsize(masks, int(max_cell_size))
+
+        # Apply stereological exclusion if enabled
+        excluded_count = 0
+        if use_stereology:
+            masks, excluded_count, included_count = apply_stereological_exclusion(
+                masks, left_exclusion, top_exclusion
+            )
+        
+        filter_msg = ""
+        if removed_small:
+            filter_msg += f"Removed {removed_small} small objects (< {min_used} pixels).\n"
+        if removed_large:
+            filter_msg += f"Removed {removed_large} large objects (> {int(max_cell_size)} pixels).\n"
+        if use_stereology and excluded_count > 0:
+            filter_msg += f"Stereological exclusion: {excluded_count} cells excluded (touching left/top zones).\n"
+
+        cell_count = len(np.unique(masks)) - 1
+        confluency = measure_confluency(masks, processed_image_np)
+
+        # Create a basic segmentation overlay (without viability)
+        segmentation_overlay = processed_image_np.copy().astype(np.float32)
+        if masks.max() > 0:
+            np.random.seed(42)  # For consistent random colors
+            colors = np.random.randint(0, 255, size=(masks.max() + 1, 3))
+            colors[0] = [0, 0, 0]
+            colored_mask = colors[masks]
+            alpha = 0.4
+            segmentation_overlay = (1 - alpha) * segmentation_overlay + alpha * colored_mask
+        segmentation_overlay = np.clip(segmentation_overlay, 0, 255).astype(np.uint8)
+        
+        # Add exclusion zone overlay if stereology is enabled
+        if use_stereology:
+            segmentation_overlay = draw_exclusion_overlay(segmentation_overlay, left_exclusion, top_exclusion)
+
+        info_msg = ""
+        if filter_msg:
+            info_msg += filter_msg
+        info_msg += f"Segmentation complete! Found {cell_count} cells.\n"
+        info_msg += f"Confluency: {confluency:.1f}%\n"
+        if use_stereology:
+            info_msg += f"Stereological counting enabled (Left: {left_exclusion}%, Top: {top_exclusion}%)\n"
+        info_msg += "Now adjust the Blue Threshold for viability assessment."
+
+        return (
+            cell_count,
+            Image.fromarray(segmentation_overlay),
+            info_msg,
+            gr.update(visible=True),
+            pack_array(masks),
+            pack_array(processed_image_np),
+            confluency,
+            gr.update(value=recommend_min),  # update slider display to recommended
+        )
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return (
+            0,
+            None,
+            f"Error during segmentation: {str(e)}",
+            gr.update(visible=False),
+            None,
+            None,
+            0.0,
+            gr.update(),
+        )
+
+
+def update_viability_realtime(threshold_bias, stored_masks, stored_image_np):
+    # avoid unpacking None (e.g. slider moved before segmentation)
+    if stored_masks is None or stored_image_np is None:
+        return None, 0, 0, 0.0, "Please run segmentation first."
+
+    stored_masks    = unpack_array(stored_masks)
+    stored_image_np = unpack_array(stored_image_np)
+
+    try:
+        dead_count, alive_count, viability_overlay_np, otsu_thresh, final_thresh = \
+            classify_cells_by_blueness(stored_image_np, stored_masks, threshold_bias)
+
+        total_count       = alive_count + dead_count
+        viability_percent = (alive_count / total_count * 100) if total_count > 0 else 0.0
+        confluency        = measure_confluency(stored_masks, stored_image_np)
+
+        overlay_image = Image.fromarray(viability_overlay_np)
+        info_msg  = f"Total cells: {total_count}\nLive (green): {alive_count}\nDead (red): {dead_count}\n"
+        info_msg += f"Viability: {viability_percent:.1f}%\nConfluency: {confluency:.1f}%\n"
+        info_msg += f"Otsu threshold (auto): {otsu_thresh:.4f}\n"
+        info_msg += f"Final threshold (with bias): {final_thresh:.4f}\n"
+        info_msg += f"Sensitivity bias: {threshold_bias:+d}"
+
+        return overlay_image, alive_count, dead_count, viability_percent, info_msg
+
+    except Exception as e:
+        return None, 0, 0, 0.0, f"Error updating viability: {str(e)}"
+
+
+def pack_array(arr):
+    pil = Image.fromarray(arr.astype(np.uint8))
+    buf = io.BytesIO()
+    pil.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def unpack_array(data):
+    return np.array(Image.open(io.BytesIO(data)))
+
+
+def save_tab_result(cell_count, confluency, viab_percent):
+    """Package per-tab results into a dict for Tab 5 summary."""
+    return {
+        "cell_count": float(cell_count) if cell_count is not None else None,
+        "confluency": float(confluency) if confluency is not None else None,
+        "viab_percent": float(viab_percent) if viab_percent is not None else None,
+    }
+
+
+def compute_summary(r1, r2, r3, r4):
+    """Average cell count, confluency, and viability across tabs that have data."""
+    all_results = [r1, r2, r3, r4]
+    valid = [(i + 1, r) for i, r in enumerate(all_results)
+             if r is not None and r.get("cell_count") is not None]
+
+    if not valid:
+        return (
+            0.0, 0.0, 0.0,
+            "No data yet — run segmentation in at least one tab, then click Refresh Summary."
+        )
+
+    avg_count  = sum(r["cell_count"]   for _, r in valid) / len(valid)
+    avg_conf   = sum(r["confluency"]   for _, r in valid) / len(valid)
+    avg_viab   = sum(r["viab_percent"] for _, r in valid) / len(valid)
+
+    lines = [f"Tab {tab_num}: {r['cell_count']:.0f} cells | "
+             f"{r['confluency']:.1f}% confluency | "
+             f"{r['viab_percent']:.1f}% viability"
+             for tab_num, r in valid]
+    lines.append(f"\nAverages ({len(valid)} tab{'s' if len(valid) > 1 else ''}):")
+    lines.append(f"  Cell count:  {avg_count:.1f}")
+    lines.append(f"  Confluency:  {avg_conf:.1f}%")
+    lines.append(f"  Viability:   {avg_viab:.1f}%")
+
+    return avg_count, avg_conf, avg_viab, "\n".join(lines)
+
+
+# ---------------------------------------------------------------------------
+# Tab builder — creates one full analysis tab and wires all event handlers.
+# Returns the per-tab gr.State used by Tab 5 to collect summary data.
+# ---------------------------------------------------------------------------
+def build_tab(tab_index, masks_state, image_state, result_state):
+    with gr.Tab(f"Tab {tab_index}"):
+        gr.Markdown("Run segmentation")
+
+        with gr.Row():
+            with gr.Column():
+                img_input = gr.Image(
+                    type="pil",
+                    label="Microscopy image",
+                    image_mode="RGB",
+                    height=512
+                )
+
+                model_dropdown = gr.Dropdown(
+                    choices=list(MODEL_OPTIONS.keys()),
+                    label="Select Model",
+                    value="Hemocytometer Model"
+                )
+
+                min_size_slider = gr.Slider(
+                    minimum=0,
+                    maximum=500,
+                    value=0,
+                    step=10,
+                    label="Minimum Cell Size (pixels). Leave at zero for automated recommendation",
+                )
+
+                max_size_slider = gr.Slider(
+                    minimum=0,
+                    maximum=10000,
+                    value=10000,
+                    step=10,
+                    label="Maximum Cell Size (pixels)",
+                )
+
+                gr.Markdown("### Stereological Counting")
+                use_stereo = gr.Checkbox(
+                    label="Enable Stereological Counting",
+                    value=False,
+                    info="Use unbiased stereological rules for cell counting"
+                )
+
+                with gr.Group(visible=False) as stereo_controls:
+                    gr.Markdown("""
+                    **Stereological Counting Rules:**
+                    - Cells touching LEFT or TOP exclusion zones are EXCLUDED
+                    - Cells touching RIGHT or BOTTOM edges are INCLUDED
+                    - This provides unbiased counting for quantification
+                    """)
+
+                    excl_preview = gr.Image(
+                        type="pil",
+                        label="Exclusion Zone Preview (Red = Excluded)",
+                        height=500
+                    )
+
+                    left_excl = gr.Slider(
+                        minimum=0,
+                        maximum=50,
+                        value=10,
+                        step=1,
+                        label="Left Exclusion Width (%)",
+                        info="Width of left exclusion zone"
+                    )
+
+                    top_excl = gr.Slider(
+                        minimum=0,
+                        maximum=50,
+                        value=10,
+                        step=1,
+                        label="Top Exclusion Width (%)",
+                        info="Width of top exclusion zone"
+                    )
+
+                segment_btn = gr.Button("🔬 Run Segmentation", variant="primary", size="lg")
+
+            with gr.Column():
+                cell_count_out = gr.Number(label="Total Cells Detected", precision=0)
+                confluency_out = gr.Number(label="Confluency (%)", precision=1)
+                overlay_out    = gr.Image(type="pil", label="Segmentation Result")
+                info_out       = gr.Textbox(label="Processing Info", lines=4)
+
+        with gr.Group(visible=False) as viability_section:
+            gr.Markdown("### Viability Assessment (Trypan Blue)")
+            gr.Markdown(
+                "The threshold is set **automatically per image** using Otsu's method. "
+                "Use the sensitivity slider to fine-tune if needed."
+            )
+
+            with gr.Row():
+                with gr.Column():
+                    sensitivity = gr.Slider(
+                        minimum=-50,
+                        maximum=50,
+                        value=0,
+                        step=1,
+                        label="Sensitivity",
+                        info="← more dead cells  ·  auto (0)  ·  fewer dead cells →"
+                    )
+                with gr.Column():
+                    live_count_out = gr.Number(label="Live Cells (Green)", precision=0)
+                    dead_count_out = gr.Number(label="Dead Cells (Red)", precision=0)
+
+            viab_overlay     = gr.Image(type="pil", label="Viability Assessment (Green=Live, Red=Dead)")
+            viab_percent_out = gr.Number(label="Viability (%)", precision=1)
+            viab_info        = gr.Textbox(label="Analysis Results", lines=6)
+
+        # ---- Event handlers ------------------------------------------------
+
+        use_stereo.change(
+            fn=toggle_stereological_mode,
+            inputs=[use_stereo],
+            outputs=[stereo_controls]
+        )
+
+        img_input.change(
+            fn=update_exclusion_preview,
+            inputs=[img_input, left_excl, top_excl],
+            outputs=[excl_preview]
+        )
+        left_excl.change(
+            fn=update_exclusion_preview,
+            inputs=[img_input, left_excl, top_excl],
+            outputs=[excl_preview]
+        )
+        top_excl.change(
+            fn=update_exclusion_preview,
+            inputs=[img_input, left_excl, top_excl],
+            outputs=[excl_preview]
+        )
+
+        segment_btn.click(
+            fn=run_segmentation,
+            inputs=[img_input, model_dropdown, min_size_slider, max_size_slider,
+                    use_stereo, left_excl, top_excl],
+            outputs=[cell_count_out, overlay_out, info_out, viability_section,
+                     masks_state, image_state, confluency_out, min_size_slider]
+        ).then(
+            fn=update_viability_realtime,
+            inputs=[sensitivity, masks_state, image_state],
+            outputs=[viab_overlay, live_count_out, dead_count_out, viab_percent_out, viab_info]
+        ).then(
+            fn=save_tab_result,
+            inputs=[cell_count_out, confluency_out, viab_percent_out],
+            outputs=[result_state]
+        )
+
+        sensitivity.change(
+            fn=update_viability_realtime,
+            inputs=[sensitivity, masks_state, image_state],
+            outputs=[viab_overlay, live_count_out, dead_count_out, viab_percent_out, viab_info]
+        ).then(
+            fn=save_tab_result,
+            inputs=[cell_count_out, confluency_out, viab_percent_out],
+            outputs=[result_state]
+        )
+
+
+# ---------------------------------------------------------------------------
+# Gradio interface
+# ---------------------------------------------------------------------------
+with gr.Blocks(
+    title="CellposeCellCounter",
+    theme=gr.themes.Soft(),
+) as demo:
+    gr.Markdown("# CellposeCellCounter")
+    gr.Markdown("For accurate cell confluency, crop the image to display only desired area. Note that some image file types are not yet supported. PNG and JPEG are preferred.")
+
+    # Shared mask/image state (one pair per tab so tabs don't clobber each other)
+    masks_states  = [gr.State(value=None) for _ in range(4)]
+    image_states  = [gr.State(value=None) for _ in range(4)]
+    result_states = [gr.State(value=None) for _ in range(4)]
+
+    # Build Tabs 1–4 with a loop
+    for i in range(4):
+        build_tab(i + 1, masks_states[i], image_states[i], result_states[i])
+
+    # -------------------------------------------------------------------------
+    # Tab 5 — Summary
+    # -------------------------------------------------------------------------
+    with gr.Tab("Tab 5 — Summary"):
+        gr.Markdown("## Average Results Across All Tabs")
+        gr.Markdown(
+            "Run segmentation (and optionally adjust the blue threshold) in one or more tabs, "
+            "then click **Refresh Summary** to see the averages."
+        )
+
+        refresh_btn = gr.Button("🔄 Refresh Summary", variant="primary", size="lg")
+
+        with gr.Row():
+            avg_count_out = gr.Number(label="Avg Cell Count",      precision=1)
+            avg_conf_out  = gr.Number(label="Avg Confluency (%)",  precision=1)
+            avg_viab_out  = gr.Number(label="Avg Viability (%)",   precision=1)
+
+        summary_box = gr.Textbox(label="Per-Tab Breakdown", lines=10)
+
+        refresh_btn.click(
+            fn=compute_summary,
+            inputs=result_states,   # list of 4 gr.State components
+            outputs=[avg_count_out, avg_conf_out, avg_viab_out, summary_box]
+        )
+
+    # Instructions
+    with gr.Accordion("Instructions", open=False):
+        gr.Markdown("""
+        ### How to use:
+
+        1. **Upload and Segment**:
+            - Upload your microscopy image.
+            - Select a Cellpose model (e.g., "Hemocytometer Model" for suspension culture).
+            - **(Optional)** Enable Stereological Counting for unbiased quantification.
+            - Click "Run Segmentation".
+
+        2. **Stereological Counting** (Optional):
+            - Check "Enable Stereological Counting" to use unbiased counting rules.
+            - Adjust the Left and Top exclusion zone widths using the sliders.
+            - Preview shows excluded areas in red.
+            - **Counting Rules**:
+                - Cells touching LEFT or TOP exclusion zones are EXCLUDED
+                - Cells touching RIGHT or BOTTOM edges are INCLUDED
+                - This ensures unbiased, systematic counting
+
+        3. **Analysis Results**:
+            - **Cell Count**: Total number of detected cells (after exclusions if using stereology)
+            - **Confluency**: Percentage of image area covered by cells
+
+        4. **Real-time Viability Assessment (Trypan Blue)**:
+            - After segmentation, the viability section will become visible.
+            - Adjust the **"Blue Threshold (%)"** slider in real-time.
+            - **Lower values (10-20%)** are more sensitive.
+            - **Higher values (30-50%)** are more selective.
+            - Green cells = Live, Red cells = Dead.
+
+        5. **Summary (Tab 5)**:
+            - After running segmentation in one or more tabs, switch to **Tab 5**.
+            - Click **Refresh Summary** to compute averages across all completed tabs.
+            - Displays average cell count, confluency, and viability, plus a per-tab breakdown.
+
+        6. **Interpreting Results**:
+            - The app displays total, live, and dead cell counts, viability percentage, and confluency.
+            - If stereological counting is enabled, excluded cells are noted in the processing info.
+        """)
+
+if __name__ == "__main__":
+    demo.launch()