Update app.py

app.py

@@ -5,8 +5,7 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
 import skimage
-from skimage import io
-import torch
+from skimage import io, transform
 
 from io_utils import LoadImageD
 
@@ -15,6 +14,17 @@ from io_utils import LoadImageD
 current_img = None
 live_preds = None
 
+def rotate_img(image, angles):
+    """Rotate the input image based on the given angles."""
+    xt, yt, zt = angles
+
+    # Apply rotations (this is just a placeholder, replace with your actual logic)
+    rotated_img = skimage.transform.rotate(image, angle=xt, mode='edge')
+    rotated_img = skimage.transform.rotate(rotated_img, angle=yt, mode='edge')
+    rotated_img = skimage.transform.rotate(rotated_img, angle=zt, mode='edge')
+
+    return rotated_img
+
 def rotate_btn_fn(img, xt, yt, zt, add_bone_cmap=False):
     global current_img
     try:
@@ -26,18 +36,12 @@ def rotate_btn_fn(img, xt, yt, zt, add_bone_cmap=False):
             skimage.io.imsave(input_img_path, img)
         elif isinstance(img, str) and os.path.exists(img):
             input_img_path = img
+            img = skimage.io.imread(input_img_path)
         else:
             raise ValueError("Invalid input image")
 
-        out_img_path = f'data/cached_outputs/{os.path.basename(input_img_path)[:-4]}_{angles}.png'
-
-        # Assuming you have precomputed outputs for the example images
-        if os.path.exists(out_img_path):
-            out_img = skimage.io.imread(out_img_path)
-        else:
-            # Perform your rotation here if the precomputed image doesn't exist
-            # For now, let's just return the input image for demonstration purposes
-            out_img = img
+        # Apply the rotation
+        out_img = rotate_img(img, angles)
 
         if not add_bone_cmap:
             return out_img