Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import numpy as np
-from keras.preprocessing import image
+from tensorflow.keras.preprocessing import image
 from keras.models import load_model
 
 
@@ -32,14 +32,24 @@ def predict_breed(image_file):
 def main():
     st.title('Dog Breed Classification')
     uploaded_file = st.file_uploader('Choose an image of a dog', type=['jpg', 'jpeg', 'png'])
-    
     if uploaded_file is not None:
         image_file = uploaded_file.name
         with open(image_file, 'wb') as f:
             f.write(uploaded_file.getbuffer())
         predicted_class = predict_breed(image_file)
         st.image(uploaded_file, caption=f'Predicted class: {predicted_class}', use_column_width=True)
-        
-        
+
+    # Add a button to trigger image upload
+    if st.button('Upload Image'):
+        uploaded_file = st.file_uploader('Choose an image of a dog', type=['jpg', 'jpeg', 'png'])
+        if uploaded_file is not None:
+            image_file = uploaded_file.name
+            with open(image_file, 'wb') as f:
+                f.write(uploaded_file.getbuffer())
+            predicted_class = predict_breed(image_file)
+            st.image(uploaded_file, caption=f'Predicted class: {predicted_class}', use_column_width=True)
+            
+
 if __name__ == '__main__':
     main()
+