Update app.py

app.py

@@ -1,37 +1,3 @@
-# examples = [["Examples/img1.png"], ["Examples/img2.png"],["Examples/img3.png"], ["Examples/img4.png"]]
-
-# import gradio as gr
-# import tensorflow as tf
-# import numpy as np
-# from tensorflow.keras.models import load_model
-
-# HEIGHT, WIDTH = 224, 224
-# IMG_SIZE = 224
-# model = load_model('Models/best_model1.h5')
-
-# def classify_image(inp):
-#     NUM_CLASSES = 2
-#     labels = ['Cat', 'Dog']
-#     inp = tf.image.resize(inp, [IMG_SIZE, IMG_SIZE])
-#     inp = inp.numpy().reshape((-1, IMG_SIZE, IMG_SIZE, 3))
-#     inp = tf.keras.applications.vgg16.preprocess_input(inp)
-#     prediction = model.predict(inp).flatten()
-#     return {labels[i]: float(prediction[i]) for i in range(NUM_CLASSES)}  # Fixed: return floats
-
-# image = gr.Image(height=HEIGHT, width=WIDTH, label='Input')
-# label = gr.Label(num_top_classes=2)
-
-# examples = [["Examples/img1.png"], ["Examples/img2.png"],["Examples/img3.png"], ["Examples/img4.png"]]
-
-# gr.Interface(
-#     fn=classify_image, 
-#     inputs=image, 
-#     outputs=label, 
-#     title='Smart Pet Classifier',
-#     examples=examples
-# ).launch(debug=False)
-
-
 
 import gradio as gr
 import tensorflow as tf
@@ -64,5 +30,5 @@ gr.Interface(
     inputs=image, 
     outputs=label, 
     title='Smart Pet Classifier',
-    # examples=examples
+    examples=examples
 ).launch(debug=False)