Update app.py

app.py

@@ -21,6 +21,10 @@ try:
 except ImportError:
     YOLO_AVAILABLE = False
 
+# Configuration
+IMAGE_FOLDER = "./imgs"
+CSV_FILE = "train.csv"
+
 # Category names mapping (0-27)
 CATEGORY_NAMES = {
     0: "Nucleoplasm", 1: "Nuclear membrane", 2: "Nucleoli",
@@ -60,13 +64,13 @@ def extract_image_id(filename):
             return name_without_ext.replace(color, '')
     return name_without_ext
 
-def load_csv_file(csv_file):
-    """Load and parse CSV file."""
-    if csv_file is None:
-        return "No CSV file uploaded", ""
+def load_csv_data():
+    """Auto-load CSV file."""
+    if not os.path.exists(CSV_FILE):
+        return
     
     try:
-        state.csv_data = pd.read_csv(csv_file.name)
+        state.csv_data = pd.read_csv(CSV_FILE)
         state.image_categories = {}
         
         for _, row in state.csv_data.iterrows():
@@ -79,36 +83,31 @@ def load_csv_file(csv_file):
                 'indices': category_indices,
                 'names': category_names
             }
-        
-        return f"✅ Loaded CSV with {len(state.csv_data)} entries", ""
     except Exception as e:
-        return f"❌ Error: {str(e)}", ""
+        print(f"Could not load CSV: {e}")
 
-def scan_folder(folder_path):
-    """Scan folder for images."""
-    if not folder_path or not os.path.exists(folder_path):
-        return None, "❌ Folder not found", "0 images"
-    
-    if not os.path.isdir(folder_path):
-        return None, "❌ Not a directory", "0 images"
+def scan_folder():
+    """Auto-scan folder for images."""
+    if not os.path.exists(IMAGE_FOLDER) or not os.path.isdir(IMAGE_FOLDER):
+        return None
     
     try:
         extensions = {'.png', '.jpg', '.jpeg', '.tif', '.tiff', '.bmp'}
         state.image_files = []
         
-        for f in sorted(Path(folder_path).iterdir()):
+        for f in sorted(Path(IMAGE_FOLDER).iterdir()):
             if f.suffix.lower() in extensions:
                 state.image_files.append(str(f))
         
         if len(state.image_files) == 0:
-            return None, "⚠️ No images found", "0 images"
+            return None
         
         # Generate gallery
         gallery_items = [(img, os.path.basename(img)) for img in state.image_files]
-        
-        return gallery_items, f"✅ Found {len(state.image_files)} images", f"{len(state.image_files)} images"
+        return gallery_items
     except Exception as e:
-        return None, f"❌ Error: {str(e)}", "0 images"
+        print(f"Scan error: {e}")
+        return None
 
 def prepare_image_for_yolo(image):
     """Convert grayscale to RGB for YOLO."""
@@ -124,7 +123,7 @@ def prepare_image_for_yolo(image):
 def select_image_from_gallery(evt: gr.SelectData):
     """Handle image selection from gallery."""
     if not state.image_files or evt.index >= len(state.image_files):
-        return None, "❌ Invalid selection", "", gr.update(choices=[])
+        return None, "Invalid selection", "", gr.update(choices=[])
     
     state.selected_image = state.image_files[evt.index]
     
@@ -147,9 +146,9 @@ def select_image_from_gallery(evt: gr.SelectData):
         # Show original image
         fig = create_visualization(show_numbers=False)
         
-        return fig, f"✅ {os.path.basename(state.selected_image)}", categories_text, gr.update(choices=[])
+        return fig, f"Loaded: {os.path.basename(state.selected_image)}", categories_text, gr.update(choices=[])
     except Exception as e:
-        return None, f"❌ Load failed: {str(e)}", "", gr.update(choices=[])
+        return None, f"Load failed: {str(e)}", "", gr.update(choices=[])
 
 def get_image_categories():
     """Get category information for selected image."""
@@ -160,7 +159,7 @@ def get_image_categories():
     categories = state.image_categories.get(img_id)
     
     if categories:
-        result = "🏷️ Image Categories\n" + "=" * 30 + "\n"
+        result = "Image Categories\n" + "=" * 30 + "\n"
         for idx, name in zip(categories['indices'], categories['names']):
             result += f"[{idx}] {name}\n"
         return result
@@ -169,10 +168,10 @@ def get_image_categories():
 def run_cellpose_segmentation(model_type, diameter, use_gpu):
     """Run Cellpose segmentation."""
     if state.current_image is None:
-        return None, "⚠️ No image selected", gr.update(choices=[])
+        return None, "No image selected", gr.update(choices=[])
     
     if not CELLPOSE_AVAILABLE:
-        return None, "❌ Cellpose not installed", gr.update(choices=[])
+        return None, "Cellpose not installed", gr.update(choices=[])
     
     try:
         with warnings.catch_warnings():
@@ -204,20 +203,20 @@ def run_cellpose_segmentation(model_type, diameter, use_gpu):
             )
             
             if state.masks is None or state.masks.max() == 0:
-                return None, "⚠️ No cells detected", gr.update(choices=[])
+                return None, "No cells detected", gr.update(choices=[])
             
             return finalize_segmentation()
             
     except Exception as e:
-        return None, f"❌ Error: {str(e)}", gr.update(choices=[])
+        return None, f"Error: {str(e)}", gr.update(choices=[])
 
 def run_yolo_segmentation(model_path, confidence, iou, use_gpu):
     """Run YOLO segmentation."""
     if state.current_image is None:
-        return None, "⚠️ No image selected", gr.update(choices=[])
+        return None, "No image selected", gr.update(choices=[])
     
     if not YOLO_AVAILABLE:
-        return None, "❌ YOLO not installed", gr.update(choices=[])
+        return None, "YOLO not installed", gr.update(choices=[])
     
     try:
         with warnings.catch_warnings():
@@ -244,12 +243,12 @@ def run_yolo_segmentation(model_path, confidence, iou, use_gpu):
             state.masks = yolo_results_to_masks(results[0])
             
             if state.masks is None or state.masks.max() == 0:
-                return None, "⚠️ No objects detected", gr.update(choices=[])
+                return None, "No objects detected", gr.update(choices=[])
             
             return finalize_segmentation()
             
     except Exception as e:
-        return None, f"❌ Error: {str(e)}", gr.update(choices=[])
+        return None, f"Error: {str(e)}", gr.update(choices=[])
 
 def yolo_results_to_masks(result):
     """Convert YOLO results to mask format."""
@@ -283,10 +282,10 @@ def finalize_segmentation():
         # Create cell list
         cell_list = [f"Cell {prop.label} | Area: {prop.area}px²" for prop in state.cell_properties]
         
-        return fig, f"✅ {state.masks.max()} cells detected", gr.update(choices=cell_list)
+        return fig, f"{state.masks.max()} cells detected", gr.update(choices=cell_list)
         
     except Exception as e:
-        return None, f"❌ Error: {str(e)}", gr.update(choices=[])
+        return None, f"Error: {str(e)}", gr.update(choices=[])
 
 def create_visualization(show_numbers=False, highlight_cell=None):
     """Create segmentation visualization."""
@@ -402,15 +401,18 @@ def select_cell(cell_choice):
         # Find cell properties
         for prop in state.cell_properties:
             if prop.label == cell_id:
-                details = f"📊 Cell {cell_id}\n"
-                details += "─" * 25 + "\n"
+                details = f"Cell {cell_id}\n"
+                details += "=" * 25 + "\n"
                 details += f"Area: {prop.area}px²\n"
                 details += f"Centroid: ({prop.centroid[1]:.0f}, {prop.centroid[0]:.0f})\n"
                 details += f"Eccentricity: {prop.eccentricity:.3f}\n"
                 details += f"Solidity: {prop.solidity:.3f}\n"
                 details += f"Intensity: {prop.mean_intensity:.1f}\n"
-                details += f"Major Axis: {prop.major_axis_length:.1f}px\n"
-                details += f"Minor Axis: {prop.minor_axis_length:.1f}px\n"
+                
+                # Add categories if available
+                categories = get_image_categories()
+                if categories:
+                    details += "\n" + categories
                 
                 # Update visualization
                 fig = create_visualization(show_numbers=False, highlight_cell=cell_id)
@@ -418,7 +420,7 @@ def select_cell(cell_choice):
         
         return None, "Cell not found"
     except Exception as e:
-        return None, f"❌ Error: {str(e)}"
+        return None, f"Error: {str(e)}"
 
 def run_segmentation(method, cp_model, diameter, yolo_model, confidence, iou, use_gpu):
     """Run segmentation based on selected method."""
@@ -430,7 +432,7 @@ def run_segmentation(method, cp_model, diameter, yolo_model, confidence, iou, us
 def save_results():
     """Save segmentation results."""
     if state.masks is None:
-        return None, "⚠️ No results to save"
+        return None, "No results to save"
     
     try:
         import tempfile
@@ -451,41 +453,31 @@ def save_results():
                        f"{prop.centroid[0]:.1f},{prop.eccentricity:.3f},"
                        f"{prop.solidity:.3f},{prop.mean_intensity:.1f}\n")
         
-        return [mask_path, csv_path], "✅ Results saved"
+        return [mask_path, csv_path], "Results saved"
     except Exception as e:
-        return None, f"❌ Error: {str(e)}"
+        return None, f"Error: {str(e)}"
+
+# Initialize: Load CSV and scan folder
+load_csv_data()
+initial_gallery = scan_folder()
 
 # Create Gradio interface
 with gr.Blocks(title="Cell Segmentation Tool", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🔬 Cell Segmentation Application")
+    gr.Markdown("# Cell Segmentation Application")
     
     with gr.Row():
-        # LEFT COLUMN - Image Browser
+        # LEFT COLUMN - Image Gallery
         with gr.Column(scale=1):
-            gr.Markdown("### 📁 Image Browser")
-            
-            folder_input = gr.Textbox(
-                label="Folder Path",
-                value="./imgs",
-                placeholder="Enter folder path"
-            )
+            gr.Markdown("### Image Gallery")
             
-            with gr.Row():
-                scan_button = gr.Button("Scan Folder", variant="primary")
-            
-            image_count = gr.Textbox(label="Images Found", value="0 images", interactive=False)
-            
-            csv_file = gr.File(label="Upload CSV (optional)", file_types=[".csv"])
-            csv_status = gr.Textbox(label="CSV Status", interactive=False, visible=False)
-            
-            # Image gallery (thumbnails)
             image_gallery = gr.Gallery(
-                label="Click image to select",
+                value=initial_gallery,
+                label=f"{len(state.image_files)} images" if state.image_files else "No images",
                 show_label=True,
                 elem_id="gallery",
                 columns=1,
                 rows=None,
-                height=400,
+                height=600,
                 object_fit="contain"
             )
             
@@ -493,7 +485,7 @@ with gr.Blocks(title="Cell Segmentation Tool", theme=gr.themes.Soft()) as demo:
         
         # CENTER COLUMN - Image View
         with gr.Column(scale=2):
-            gr.Markdown("### 🖼️ Image View")
+            gr.Markdown("### Image View")
             
             with gr.Row():
                 view_mode = gr.Radio(
@@ -507,7 +499,7 @@ with gr.Blocks(title="Cell Segmentation Tool", theme=gr.themes.Soft()) as demo:
         
         # RIGHT COLUMN - Controls & Results
         with gr.Column(scale=1):
-            gr.Markdown("### ⚙️ Segmentation Settings")
+            gr.Markdown("### Segmentation Settings")
             
             method = gr.Radio(
                 ["Cellpose", "YOLO"],
@@ -532,9 +524,9 @@ with gr.Blocks(title="Cell Segmentation Tool", theme=gr.themes.Soft()) as demo:
             
             use_gpu = gr.Checkbox(label="Use GPU", value=False)
             
-            run_button = gr.Button("🔬 Run Segmentation", variant="primary", size="lg")
+            run_button = gr.Button("Run Segmentation", variant="primary", size="lg")
             
-            gr.Markdown("### 🔬 Detected Cells")
+            gr.Markdown("### Detected Cells")
             
             cell_dropdown = gr.Dropdown(
                 label="Select Cell",
@@ -542,14 +534,14 @@ with gr.Blocks(title="Cell Segmentation Tool", theme=gr.themes.Soft()) as demo:
                 interactive=True
             )
             
-            gr.Markdown("### 📊 Cell Details")
+            gr.Markdown("### Cell Details")
             cell_details = gr.Textbox(
                 label="",
                 lines=12,
                 interactive=False
             )
             
-            save_button = gr.Button("💾 Save Results", variant="secondary")
+            save_button = gr.Button("Save Results", variant="secondary")
             output_files = gr.File(label="Download", file_count="multiple")
     
     # Event handlers
@@ -561,18 +553,6 @@ with gr.Blocks(title="Cell Segmentation Tool", theme=gr.themes.Soft()) as demo:
     
     method.change(toggle_method, inputs=[method], outputs=[cellpose_group, yolo_group])
     
-    scan_button.click(
-        scan_folder,
-        inputs=[folder_input],
-        outputs=[image_gallery, status_text, image_count]
-    )
-    
-    csv_file.change(
-        load_csv_file,
-        inputs=[csv_file],
-        outputs=[csv_status, cell_details]
-    )
-    
     image_gallery.select(
         select_image_from_gallery,
         outputs=[image_display, status_text, cell_details, cell_dropdown]
@@ -606,20 +586,6 @@ with gr.Blocks(title="Cell Segmentation Tool", theme=gr.themes.Soft()) as demo:
         save_results,
         outputs=[output_files, status_text]
     )
-    
-    gr.Markdown("""
-    ### 📝 Instructions:
-    1. Enter folder path and click "Scan Folder"
-    2. Click on image thumbnail in gallery to select
-    3. (Optional) Upload train.csv for category information
-    4. Choose segmentation method (Cellpose or YOLO)
-    5. Configure parameters
-    6. Click "Run Segmentation"
-    7. View results in overlay mode
-    8. Select cells from dropdown to view details and highlight
-    9. Toggle "Show Cell Numbers" to display labels
-    10. Save results to download masks and measurements
-    """)
 
 if __name__ == "__main__":
     demo.launch(share=False)