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README.md

@@ -1,35 +1,35 @@
----
-title: GERD Lightweight ViT Classifier
-emoji: 🏥
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 6.2.0
-app_file: app.py
-pinned: false
----
-
-# GERD Lightweight Vision Transformer Classifier
-
-This is a lightweight Vision Transformer model for GERD classification.
-
-## Model Architecture
-
-- **Image size**: 224×224×3
-- **Patch size**: 8×8
-- **Projection dimension**: 64
-- **Transformer layers**: 4
-- **Attention heads**: 4
-- **MLP head units**: [128, 64]
-
-## Usage
-
-Upload an image and the model will classify it into one of the predefined classes.
-
-## Preprocessing
-
-Images are:
-
-1. Resized to 224×224
-2. Converted to RGB
-3. Normalized to [0, 1] range
+---
+title: GERD Lightweight ViT Classifier
+emoji: 🏥
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.0.0
+app_file: app.py
+pinned: false
+---
+
+# GERD Lightweight Vision Transformer Classifier
+
+This is a lightweight Vision Transformer model for GERD classification.
+
+## Model Architecture
+
+- **Image size**: 224×224×3
+- **Patch size**: 8×8
+- **Projection dimension**: 64
+- **Transformer layers**: 4
+- **Attention heads**: 4
+- **MLP head units**: [128, 64]
+
+## Usage
+
+Upload an image and the model will classify it into one of the predefined classes.
+
+## Preprocessing
+
+Images are:
+
+1. Resized to 224×224
+2. Converted to RGB
+3. Normalized to [0, 1] range

app.py

@@ -11,8 +11,8 @@ import os
 
 
 class Patches(L.Layer):
-    def __init__(self, patch_size):
-        super(Patches, self).__init__()
+    def __init__(self, patch_size, **kwargs):
+        super(Patches, self).__init__(**kwargs)
         self.patch_size = patch_size
 
     def call(self, images):
@@ -30,8 +30,8 @@ class Patches(L.Layer):
 
 
 class PatchEncoder(L.Layer):
-    def __init__(self, num_patches, projection_dim):
-        super(PatchEncoder, self).__init__()
+    def __init__(self, num_patches, projection_dim, **kwargs):
+        super(PatchEncoder, self).__init__(**kwargs)
         self.num_patches = num_patches
         self.projection = L.Dense(units=projection_dim)
         self.position_embedding = L.Embedding(
@@ -158,9 +158,7 @@ iface = gr.Interface(
     outputs=gr.Label(num_top_classes=4, label="Predictions"),
     title=title,
     description=description,
-    examples=examples,
-    allow_flagging="never",
-    theme=gr.themes.Soft()
+    examples=examples
 )
 
 # Launch the interface