Update app.py

app.py

@@ -26,20 +26,20 @@ class_names = ['Palm', 'Others']
 model = tf.keras.models.load_model('model')
 Generate_pred = st.button("Generate Prediction")
         
-while(1):
-    if uploaded_file is not None:
-        if Generate_pred:
-            for file in uploaded_file:
-                img = Image.open(file)
-                img_array = img_to_array(img)
-                img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
-                processed_image = preprocess_input(img_array)
-        
-                predictions = model.predict(processed_image)
-                score = predictions[0]
-                st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
+
+if uploaded_file is not None:
+    if Generate_pred:
+        for file in uploaded_file:
+            img = Image.open(file)
+            img_array = img_to_array(img)
+            img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
+            processed_image = preprocess_input(img_array)
+            
+            predictions = model.predict(processed_image)
+            score = predictions[0]
+            st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
     
-    if closeImage:
-        imageContainer.empty()
-        if uploaded_file is not None:
-            del uploaded_file
+if closeImage:
+    imageContainer.empty()
+    if uploaded_file is not None:
+        del uploaded_file