updates

app.py

@@ -45,40 +45,40 @@ if uploaded_file is not None:
     file_bytes = BytesIO(uploaded_file.read())
     st.image(file_bytes,use_column_width=True,clamp = True) 
     
-predict_button = st.button("ㅤㅤPredictㅤㅤ")
-if predict_button:
-    img = cv2.imdecode(np.frombuffer(file_bytes.read(), np.uint8), 0)
-    #img=np.array(file_bytes)   
-  
-    if len(img.shape) == 2:
-        img=cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
-    
-    img=cv2.resize(img,(176, 176))
-    if img.max() > 1:
-        img = img / 255.0
-    
-    img = np.expand_dims(img, axis=0)
-    pred=model.predict(img)
-    predict_val = np.argmax(pred, axis=1)
-   
-    if predict_val == 0:
-        probability = pred[0][predict_val]
-        st.write(f"Mildly Demented with prediction probability of {probability}") 
-                 
-    elif predict_val == 1:
-        probability = pred[0][predict_val]
-        st.write(f"Moderately Demented with prediction probability of {probability}") 
-                 
-    elif predict_val == 2:
-        probability = pred[0][predict_val]
-        st.write(f"Not Demented with prediction probability of {probability}") 
-                
-    elif predict_val == 3:
-        probability = pred[0][predict_val]
-        st.write(f"Very Mildly Demented with prediction probability of {probability}") 
-                
-    else:
-       st.write("Error!")
+    predict_button = st.button("ㅤㅤPredictㅤㅤ")
+    if predict_button:
+        img = cv2.imdecode(np.frombuffer(file_bytes.read(), np.uint8), 0)
+        #img=np.array(file_bytes)   
+      
+        if len(img.shape) == 2:
+            img=cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
+        
+        img=cv2.resize(img,(176, 176))
+        if img.max() > 1:
+            img = img / 255.0
+        
+        img = np.expand_dims(img, axis=0)
+        pred=model.predict(img)
+        predict_val = np.argmax(pred, axis=1)
+       
+        if predict_val == 0:
+            probability = pred[0][predict_val]
+            st.warning(f"Mildly Demented with prediction probability of {probability}") 
+                     
+        elif predict_val == 1:
+            probability = pred[0][predict_val]
+            st.warning(f"Moderately Demented with prediction probability of {probability}") 
+                     
+        elif predict_val == 2:
+            probability = pred[0][predict_val]
+            st.warning(f"Not Demented with prediction probability of {probability}") 
+                    
+        elif predict_val == 3:
+            probability = pred[0][predict_val]
+            st.warning(f"Very Mildly Demented with prediction probability of {probability}") 
+                    
+        else:
+           st.warning("Error!")