Refactor sketch recognition app: disable oneDNN optimizations and update model path

app.py

@@ -1,4 +1,6 @@
-# import dependencies
+import os
+os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"  # Disable oneDNN optimizations
+
 import gradio as gr
 import tensorflow as tf
 import cv2
@@ -24,23 +26,17 @@ img_size = 28
 labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
 
 # load model (trained on MNIST dataset)
-model = tf.keras.models.load_model("./sketch_recognition_numbers_model.h5")
+model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5")
 
 # prediction function for sketch recognition
 def predict(img):
-
-  # image shape: 28x28x1
-  img = cv2.resize(img, (img_size, img_size))
-  img = img.reshape(1, img_size, img_size, 1)
-
-  # model predictions
-  preds = model.predict(img)[0]
-
-  # return the probability for each classe
-  return {label: float(pred) for label, pred in zip(labels, preds)}
+    img = cv2.resize(img, (img_size, img_size))
+    img = img.reshape(1, img_size, img_size, 1)
+    preds = model.predict(img)[0]
+    return {label: float(pred) for label, pred in zip(labels, preds)}
 
 # top 3 of classes
-label = gr.outputs.Label(num_top_classes=3)
+label = gr.Label(num_top_classes=3)
 
 # open Gradio interface for sketch recognition
 interface = gr.Interface(fn=predict, inputs="sketchpad", outputs=label, title=title, description=head, article=ref)