Update app.py

app.py

@@ -22,24 +22,24 @@ img_width = 224
 class_names = ['Palm', 'Others']
 model = tf.keras.models.load_model('model')
 
+#Generate_pred = st.button("Generate Prediction")
+
 with st.form("form", clear_on_submit=True):
     uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
-    submitted = st.form_submit_button("UPLOAD!")
+    submitted = st.form_submit_button("Generate prodiction!")
     
     if submitted and uploaded_file is not None:
-        st.write("UPLOADED!")
+        st.write("Predicting!")
         st.image(uploaded_file, width=100)
 
-        Generate_pred = st.button("Generate Prediction")
-        
-        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)]))
+        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)]))
+