Delete app.py

app.py

@@ -1,149 +0,0 @@
-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
-import io
-from huggingface_hub import hf_hub_download
-
-HF_REPO_ID = "myang4218/cellposemodel"
-MODEL_OPTIONS = {
-    "Hemocytometer Model": "hemocytometermodel.npy",
-    "General Model": "generalmodel.npy"
-}
-loaded_models = {}
-
-# Global variable for region coordinates (can remain global if only for info display)
-current_region_coords = None
-
-"""
-# Commented out debugging function
-def debug_editor_data(editor_data):
-    \"\"\"Debug function to understand the structure of ImageEditor data\"\"\"
-    if editor_data is None:
-        return "No data provided"
-    info = f"Type: {type(editor_data)}\n"
-    if isinstance(editor_data, dict):
-        info += f"Keys: {list(editor_data.keys())}\n"
-        for key, value in editor_data.items():
-            info += f"  {key}: {type(value)}\n"
-            if key == 'layers' and value:
-                info += f"    Layers count: {len(value)}\n"
-                for i, layer in enumerate(value):
-                    info += f"    Layer {i}: {type(layer)}\n"
-                    if hasattr(layer, 'size'):
-                        info += f"      Size: {layer.size}\n"
-                        info += f"      Mode: {layer.mode}\n"
-    else: # Direct PIL Image
-        if hasattr(editor_data, 'size'):
-            info += f"Size: {editor_data.size}\n"
-            info += f"Mode: {editor_data.mode}\n"
-    return info
-"""
-
-def extract_region_from_editor(editor_data):
-    """Extract the selected region from ImageEditor data"""
-    if editor_data is None:
-        # print("DEBUG: extract_region_from_editor - editor_data is None")
-        return None, None
-    
-    # print(f"DEBUG: extract_region_from_editor - editor_data type: {type(editor_data)}")
-    
-    if isinstance(editor_data, dict):
-        background = editor_data.get('background')
-        layers = editor_data.get('layers', [])
-        
-        if background is None:
-            # print("DEBUG: extract_region_from_editor - background is None")
-            return None, None
-        
-        background_np = np.array(background)
-        # print(f"DEBUG: extract_region_from_editor - background_np shape: {background_np.shape}")
-        
-        if layers and len(layers) > 0:
-            selection_layer = layers[0]
-            selection_np = np.array(selection_layer)
-            # print(f"DEBUG: extract_region_from_editor - selection_np shape: {selection_np.shape}")
-            
-            if len(selection_np.shape) == 3:
-                if selection_np.shape[2] == 4:  # RGBA
-                    mask = selection_np[:, :, 3] > 0
-                else:  # RGB
-                    mask = np.any(selection_np > 0, axis=2)
-            else:
-                mask = selection_np > 0
-            
-            coords = np.where(mask)
-            if len(coords[0]) > 0:
-                y_min, y_max = coords[0].min(), coords[0].max()
-                x_min, x_max = coords[1].min(), coords[1].max()
-                
-                pad = 5
-                h, w = background_np.shape[:2]
-                y_min = max(0, y_min - pad)
-                y_max = min(h, y_max + pad)
-                x_min = max(0, x_min - pad)
-                x_max = min(w, x_max + pad)
-                
-                region = background_np[y_min:y_max+1, x_min:x_max+1]
-                # print(f"DEBUG: extract_region_from_editor - Extracted region shape: {region.shape}")
-                return region, (x_min, y_min, x_max, y_max)
-        # print("DEBUG: extract_region_from_editor - No selection or empty selection, returning full background.")
-        return background_np, None
-    else: # Assuming it's a direct PIL Image if not dict
-        if hasattr(editor_data, 'size'):
-            image_np = np.array(editor_data)
-            # print(f"DEBUG: extract_region_from_editor - Direct PIL image, shape: {image_np.shape}")
-            return image_np, None
-        else:
-            # print("DEBUG: extract_region_from_editor - editor_data is not dict and no size attribute, returning None.")
-            return None, None
-
-def calculate_blue_intensity(image_np, masks):
-    """Calculate blue intensity for each cell"""
-    # Ensure image is in correct format
-    if len(image_np.shape) == 2:
-        # Convert grayscale to RGB
-        image_np = cv2.cvtColor(image_np, cv2.COLOR_GRAY2RGB)
-    elif len(image_np.shape) == 3 and image_np.shape[2] == 4:
-        # Handle RGBA
-        image_np = cv2.cvtColor(image_np, cv2.COLOR_RGBA2RGB)
-        
-    # Calculate blue intensity for each cell
-    cell_blue_intensities = {}
-    unique_cells = np.unique(masks)
-    
-    # print(f"DEBUG: Found {len(unique_cells)} unique mask values: {unique_cells}")
-    # print(f"DEBUG: Image shape: {image_np.shape}, dtype: {image_np.dtype}")
-    # print(f"DEBUG: Masks shape: {masks.shape}, dtype: {masks.dtype}")
-    
-    for cell_id in unique_cells:
-        if cell_id == 0:  # Skip background
-            continue
-        
-        cell_mask = masks == cell_id
-        cell_pixels = image_np[cell_mask]
-        
-        if len(cell_pixels) > 0:
-            # Calculate mean blue intensity (blue channel is index 2)
-            blue_intensity = np.mean(cell_pixels[:, 2])  # Blue channel
-            cell_blue_intensities[cell_id] = blue_intensity
-            # print(f"DEBUG: Cell {cell_id}: {len(cell_pixels)} pixels, blue intensity: {blue_intensity:.2f}")
-    # print(f"DEBUG: Total cells with blue intensity: {len(cell_blue_intensities)}")
-    return cell_blue_intensities
-
-def create_viability_overlay(image_np, masks, blue_threshold):
-    """Create overlay showing live/dead cells based on blue threshold"""
-    
-    if masks is None or image_np is None:
-        # print("DEBUG: create_viability_overlay - masks or image_np is None, returning None.")
-        return None, 0, 0
-    
-    # print(f"DEBUG: create_viability_overlay called with threshold: {blue_threshold}")
-    # print(f"DEBUG: Image shape: {image_np.shape}, Masks shape: {masks.shape}, Masks max: {masks.max()}")
-    
-    # Ensure image is RGB
-    if len(