Update app.py

app.py

@@ -1,5 +1,38 @@
 # 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
 import numpy as np
@@ -10,19 +43,22 @@ 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
+    if len(prediction) == 1:
+        dog_prob = float(prediction[0])
+        return {labels[0]: 1 - dog_prob, labels[1]: dog_prob}
+    else:
+        return {labels[i]: float(prediction[i]) for i in range(len(labels))}
 
 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,