Update app.py

app.py

@@ -20,11 +20,12 @@ model = UNet(
 model.load_state_dict(torch.load("best_model.pth", map_location=torch.device('cpu')))
 model.eval()
 
-def greet(image_path):
+def greet(image):
     
 
     # image = Image.open(image_path).convert("RGB")
-    image = np.array(image) / 255.0
+    # image = np.array(image) / 255.0
+    image = image / 
     image = image.astype(np.float32)
 
     inference_transforms = A.Compose([
@@ -47,11 +48,29 @@ demo = gr.Interface(
     fn=greet,
     title="Histapathology segmentation",
     inputs=[
-        gr.File(label="Input image (512x512)")
+        gr.Image(
+            label="Input image",
+            image_mode="RGB",
+            height=400,
+            type="numpy",
+            witdh=400,
+        )
     ],
     outputs=[
-        gr.File(label="Model Prediction")
+        gr.Image(
+            label="Model Prediction",
+            image_mode="RGB",
+            height=400,
+            witdh=400,
+        )
     ],
+    # examples=[
+    #     os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
+    #     os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
+    #     os.path.join(os.path.dirname(__file__), "images/logo.png"),
+    #     os.path.join(os.path.dirname(__file__), "images/tower.jpg"),
+    # ],
+
 )
 
 demo.launch()