Update app.py

app.py

@@ -8,7 +8,7 @@ import tempfile
 from PIL import Image
 import io
 from huggingface_hub import hf_hub_download
-import base64  # needed for mobile ImageEditor base64 handling
+import base64  
 
 HF_REPO_ID = "myang4218/cellposemodel"
 MODEL_OPTIONS = {
@@ -18,7 +18,7 @@ MODEL_OPTIONS = {
 
 loaded_models = {}
 
-# ---- mobile-safe size limits (aggressive for Safari) ----
+# mobile-safe size limits
 MAX_SIDE = 1024          
 MAX_PIXELS = 1024 * 1024
 
@@ -31,11 +31,11 @@ def safe_resize(image_np):
     h, w = image_np.shape[:2]
     total = h * w
 
-    # already small enough
+    
     if max(h, w) <= MAX_SIDE and total <= MAX_PIXELS:
         return image_np
 
-    # compute scale so BOTH constraints are satisfied
+    
     scale_side = MAX_SIDE / max(h, w)
     scale_pixels = (MAX_PIXELS / total) ** 0.5
     scale = min(scale_side, scale_pixels)
@@ -132,7 +132,7 @@ def measure_confluency(masks, image_np):
 def filter_mask_by_size(masks, minimum_pixels):
     filtered_masks = masks.copy()
     cell_ids = np.unique(masks)
-    cell_ids = cell_ids[cell_ids > 0]
+    cell_ids = cell_ids[cell_ids > 0] #subtract background
 
     removed_count = 0
     
@@ -187,7 +187,7 @@ def rec_min_size(masks, q=25):
 
 
 @spaces.GPU
-def run_segmentation(image, model_choice,min_cell_size):
+def run_segmentation(image, model_choice, min_cell_size, max_cell_size):
     image_np = np.array(image)
     image_np = safe_resize(image_np)
     
@@ -213,18 +213,14 @@ def run_segmentation(image, model_choice,min_cell_size):
             processed_image_np = image_np
 
         # Run Cellpose segmentation
-        masks, flows, styles = model.eval(processed_image_np, diameter=None, channels=[0, 0])
+        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())
-        print("median:", np.median(sizes))
-        print("p90:", np.percentile(sizes, 90))
-        print("max:", sizes.max())
+        
         # Compute recommendation from RAW masks 
         recommend_min = rec_min_size(masks_raw)
 
@@ -233,7 +229,7 @@ def run_segmentation(image, model_choice,min_cell_size):
         min_used = recommend_min if (min_cell_size == 0) else int(min_cell_size)
 
         # Apply filters
-        masks = masks_raw
+        masks = masks_raw.copy()
         removed_small = 0
         removed_large = 0
 
@@ -256,7 +252,7 @@ def run_segmentation(image, model_choice,min_cell_size):
         # 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
+            np.random.seed(42)  
             colors = np.random.randint(0, 255, size=(masks.max() + 1, 3))
             colors[0] = [0, 0, 0]
             colored_mask = colors[masks]
@@ -269,11 +265,6 @@ def run_segmentation(image, model_choice,min_cell_size):
             info_msg += filter_msg
         info_msg += f"Segmentation complete! Found {cell_count} cells.\n"
         info_msg += f"Confluency: {confluency:.1f}%\n"
-        if region_coords:
-            info_msg += (
-                f"Processed region: {region_coords[0]},{region_coords[1]} "
-                f"to {region_coords[2]},{region_coords[3]}\n"
-            )
         info_msg += "Now adjust the Blue Threshold for viability assessment."
 
         return (
@@ -423,9 +414,9 @@ with gr.Blocks(
         # Event handlers
         segment_btn1.click(
             fn=run_segmentation,
-            inputs=[img_input, model_dropdown1,min_size_slider1, max_size_slider1],
+            inputs=[img_input, model_dropdown1, min_size_slider1, max_size_slider1],
             outputs=[cell_count_output1, overlay_output1, info_output1, viability_section1, masks_state, image_state, confluency_output1, min_size_slider1]
-        ).then(  # Chain the initial viability assessment after segmentation
+        ).then(
             fn=update_viability_realtime,
             inputs=[blue_threshold1, masks_state, image_state],  # Pass stored state as inputs
             outputs=[viability_overlay1, live_count_output1, dead_count_output1, viability_percent_output1, viability_info1]