Update app.py

app.py

@@ -1,49 +1,50 @@
-import tensorflow as tf
-import gradio as gr
-import numpy as np
-import cv2
-import os
-
-classes = ["Abyssinian", "Bengal", "Birman", "Bombay", "British Shorthair", "Egyptian Mau", "Maine Coon", "Persian", "Ragdoll", "Russian Blue", "Siamese", "Sphynx"]
-example_images = ["examples/" + f for f in os.listdir("examples")]
-
-img_size = 400
-model = tf.keras.models.load_model("CatClassifier.keras")
-
-def model_predict(image):
-    image = cv2.resize(image, (img_size, img_size))
-    image = np.expand_dims(image, axis=0)
-
-    predictions = model.predict(image)
-    predictions = predictions[0]
-
-    predicted_class_index = np.argmax(predictions)
-    predicted_class = classes[predicted_class_index]
-    pred_dict = {}
-
-    for i in range(len(classes)):
-        pred_dict[classes[i]] = predictions[i]
-
-    return predicted_class, pred_dict
-
-
-def predict_breed(image):
-    if image is None:
-        return "Please attach an image first!", None
-    
-    return model_predict(image)
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-          image_input = gr.Image(label="Cat Image")
-          run_button = gr.Button(variant="primary")
-          examples = gr.Examples(example_images,inputs=image_input)
-        with gr.Column():
-          breed_output = gr.Text(label="Predicted Breed", interactive=False)
-          predict_labels = gr.Label(label="Class Probabilties")  
-          
-    run_button.click(fn=predict_breed, inputs=image_input, outputs=[breed_output, predict_labels])
-
-if __name__ == "__main__":
+import tensorflow as tf
+from tensorflow import keras
+import gradio as gr
+import numpy as np
+import cv2
+import os
+
+classes = ["Abyssinian", "Bengal", "Birman", "Bombay", "British Shorthair", "Egyptian Mau", "Maine Coon", "Persian", "Ragdoll", "Russian Blue", "Siamese", "Sphynx"]
+example_images = ["examples/" + f for f in os.listdir("examples")]
+
+img_size = 400
+model = tf.keras.models.load_model("CatClassifier.keras")
+
+def model_predict(image):
+    image = cv2.resize(image, (img_size, img_size))
+    image = np.expand_dims(image, axis=0)
+
+    predictions = model.predict(image)
+    predictions = predictions[0]
+
+    predicted_class_index = np.argmax(predictions)
+    predicted_class = classes[predicted_class_index]
+    pred_dict = {}
+
+    for i in range(len(classes)):
+        pred_dict[classes[i]] = predictions[i]
+
+    return predicted_class, pred_dict
+
+
+def predict_breed(image):
+    if image is None:
+        return "Please attach an image first!", None
+    
+    return model_predict(image)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+          image_input = gr.Image(label="Cat Image")
+          run_button = gr.Button(variant="primary")
+          examples = gr.Examples(example_images,inputs=image_input)
+        with gr.Column():
+          breed_output = gr.Text(label="Predicted Breed", interactive=False)
+          predict_labels = gr.Label(label="Class Probabilties")  
+          
+    run_button.click(fn=predict_breed, inputs=image_input, outputs=[breed_output, predict_labels])
+
+if __name__ == "__main__":
     demo.launch()