Update app.py

app.py

@@ -142,6 +142,13 @@ def classify_cells_by_blueness(image_np, masks, blue_threshold):
     final_overlay = np.clip(final_overlay, 0, 255).astype(np.uint8)
     
     return len(dead_cells), len(alive_cells), final_overlay
+    
+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
+
 
 @spaces.GPU
 def run_segmentation_editor(editor_data, model_choice):
@@ -176,7 +183,6 @@ def run_segmentation_editor(editor_data, model_choice):
         masks, flows, styles = model.eval(processed_image_np, diameter=None, channels=[0, 0])
         
         cell_count = len(np.unique(masks)) - 1
-        
         # Create a basic segmentation overlay (without viability)
         segmentation_overlay = processed_image_np.copy().astype(np.float32)
         if masks.max() > 0:
@@ -249,8 +255,7 @@ def run_segmentation_manual(image, model_choice, crop_coords):
         # Run Cellpose
         masks, flows, styles = model.eval(processed_image_np, diameter=None, channels=[0, 0])
         
-        cell_count = len(np.unique(masks)) - 1
-                
+        cell_count = len(np.unique(masks)) - 1    
         # Create a basic segmentation overlay
         segmentation_overlay = processed_image_np.copy().astype(np.float32)
         if masks.max() > 0:
@@ -427,7 +432,7 @@ with gr.Blocks(title="Cell Viability Counter with Trypan Blue Analysis", theme=g
             outputs=[cell_count_output2, overlay_output2, info_output2, viability_section2, masks_state, image_state]
         ).then(  # Chain the initial viability assessment after segmentation
             fn=update_viability_realtime,
-            inputs=[blue_threshold2, masks_state, image_state],
+            inputs=[blue_threshold2, masks_state, image_state], 
             outputs=[viability_overlay2, live_count_output2, dead_count_output2, viability_percent_output2, viability_info2]
         )