update app.py

app.py

@@ -1,15 +1,18 @@
 import gradio as gr
 import cv2
-class_names = ['Kharbandhi Lhakhang','Milarepa Lhakhang','Pasakha Lhakhang']
+import tensorflow as tf
+import numpy as np
+
+class_names = ['Kharbandhi Lhakhang', 'Milarepa Lhakhang', 'Pasakha Lhakhang']
 img_height, img_width = 256, 256
 model = tf.keras.models.load_model('CSR_models.h5')
-image_output_class=class_names[np.argmax(predictions)]
+
 def greet(sample_image):
-    sample_image_resized= cv2.resize(sample_image, (img_height,img_width))
-    sample_image=np.expand_dims(sample_image_resized,axis=0)
-    # Use the model for predictions
-    predictions = model.predict(sample_image)
-    image_output_class=class_names[np.argmax(predictions)]
+    sample_image_resized = cv2.resize(sample_image, (img_height, img_width))
+    sample_image_expanded = np.expand_dims(sample_image_resized, axis=0)
+    predictions = model.predict(sample_image_expanded)
+    image_output_class = class_names[np.argmax(predictions)]
+    return image_output_class
 
 demo = gr.Interface(fn=greet, inputs="image", outputs="text")
 demo.launch()