Update app.py

app.py

@@ -19,15 +19,18 @@ with custom_object_scope({'Addons>F1Score': F1Score}):
 def classify_image(inp):
     np.random.seed(143)
 
-    # Preprocess input
-    inp = inp.reshape((-1, HEIGHT, WIDTH, 3))
+    # Ensure input is resized to expected shape
+    inp = tf.image.resize(inp, [HEIGHT, WIDTH])
+    inp = tf.cast(inp, tf.float32)  # ensure correct dtype for preprocessing
     inp = tf.keras.applications.nasnet.preprocess_input(inp)
+    inp = tf.expand_dims(inp, axis=0)  # make batch dimension
 
     # Prediction
     prediction = model.predict(inp)
-    # Build a dict of label:confidence
+
     return {LABELS[i]: float(f"{prediction[0][i]:.6f}") for i in range(NUM_CLASSES)}
 
+
 # Gradio interface
 iface = gr.Interface(
     fn=classify_image,