move model files outside the folder

app.py

@@ -1,5 +1,32 @@
 import gradio as gr
-from model import predict
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+from segmentation import predict as segmentation_predict
+from depth_estimation import predict as depth_estimation_predict
+
+def predict(image, color_map):
+	# inference
+
+	mask_image = segmentation_predict(image)
+
+	segmented_image = Image.composite(
+		image,
+		Image.new("RGB", image.size, (0, 0, 0)),
+		mask_image.convert("L")
+	)
+
+	depth_image = depth_estimation_predict(segmented_image)
+
+	# apply matplotlib colormap (e.g., viridis)
+	depth_array = np.array(depth_image)  # Convert PIL image to NumPy array
+	colormap = plt.get_cmap(color_map)   # Choose a colormap
+	depth_colored = colormap(depth_array[:, :, 0] / 255.0)  # Normalize and apply colormap
+	depth_colored = (depth_colored * 255).astype(np.uint8)  # Convert to RGB (discard alpha)
+
+	depth_colored = Image.fromarray(depth_colored)
+
+	return depth_colored
 
 color_maps = [
     'viridis', 'plasma', 'inferno', 'magma', 'cividis',

model/__init__.py

@@ -1,28 +0,0 @@
-from PIL import Image
-import numpy as np
-import matplotlib.pyplot as plt
-from segmentation import predict as segmentation_predict
-from depth_estimation import predict as depth_estimation_predict
-
-def predict(image, color_map):
-	# inference
-
-	mask_image = segmentation_predict(image)
-
-	segmented_image = Image.composite(
-		image,
-		Image.new("RGB", image.size, (0, 0, 0)),
-		mask_image.convert("L")
-	)
-
-	depth_image = depth_estimation_predict(segmented_image)
-
-	# apply matplotlib colormap (e.g., viridis)
-	depth_array = np.array(depth_image)  # Convert PIL image to NumPy array
-	colormap = plt.get_cmap(color_map)   # Choose a colormap
-	depth_colored = colormap(depth_array[:, :, 0] / 255.0)  # Normalize and apply colormap
-	depth_colored = (depth_colored * 255).astype(np.uint8)  # Convert to RGB (discard alpha)
-
-	depth_colored = Image.fromarray(depth_colored)
-
-	return depth_colored