Add inference code

inference.py

@@ -0,0 +1,33 @@
+import keras
+img_height = 162
+img_width = 300
+num_classes = 5
+
+# base model for transfer learning
+base_model = keras.applications.DenseNet121(
+    input_shape=(img_height, img_width, 3),
+    include_top=False,
+)
+base_model.trainable = False  # Freeze the base model
+
+model = keras.models.Sequential(
+    [
+        keras.layers.Input((img_height, img_width, 1)),
+        keras.layers.Lambda(
+            lambda x: tf.repeat(
+                x,
+                3,
+                axis=3,
+            )
+        ),  # Convert grayscale to RGB
+        keras.layers.Lambda(keras.applications.densenet.preprocess_input),
+        base_model,
+        keras.layers.GlobalAveragePooling2D(),
+        keras.layers.BatchNormalization(),
+        keras.layers.Dense(256, activation="relu"),
+        keras.layers.Dropout(0.5),
+        keras.layers.Dense(num_classes, activation="softmax"),
+    ]
+)
+# Load the trained weights
+model.load_weights('hf://c2p-cmd/knee_oa_classifier')