Update app.py

app.py

@@ -6,6 +6,7 @@ from PIL import Image
 import albumentations as A
 from albumentations.pytorch import ToTensorV2
 import numpy as np
+import shutil
 
 
 model = UNet(
@@ -38,7 +39,7 @@ def process_image(image):
     return mask_pred[0, 0, :, :].numpy()
     
 
-demo = gr.Interface(
+interface_image = gr.Interface(
     fn=process_image,
     title="Histapathology segmentation",
     inputs=[
@@ -64,7 +65,44 @@ demo = gr.Interface(
     #     os.path.join(os.path.dirname(__file__), "images/logo.png"),
     #     os.path.join(os.path.dirname(__file__), "images/tower.jpg"),
     # ],
+)
+
+def process_slide(slide_path):
+    if not slide_path.endswith("zip"):
+        slide = openslide.OpenSlide(os.path.join(path, image_path))
+    else: # mrxs slide files 
+        shutil.unpack_archive(slide_path, "cache_mrxs")
+
+        files = os.listdir("cache_mrxs")
+        slide_name = [file for file in files if file.endswith("mrxs")][0]
+        slide = openslide.OpenSlide(os.path.join("cache_mrxs", slide_name))
+
+    slide.get_thumbnail((512, 512))
+    
+
 
+    return slide
+
+
+interface_slide = gr.Interface(
+    fn=process_slide,
+    inputs=[
+        gr.File(
+            label="Input slide file (input zip for `.mrxs` files)",            
+        )
+    ],
+    outputs=[
+        gr.Image(
+            label="Model Prediction",
+            image_mode="RGB",
+            height=400,
+            width=400,
+        )
+    ],
 )
 
+
+demo = gr.TabbedInterface([interface_image, interface_slide], ["Image-to-Mask", "Slide-to-Mask"])
+
 demo.launch()
+