Update app.py

app.py

@@ -1393,6 +1393,16 @@ Given two compounds and predict the RT in different condition
 
 
 def predict_separate(smile_1, smile_2, input_eluent, input_speed, input_column):
+    if input_speed==None:
+        out_put='Please input Speed!'
+        return out_put
+    if input_speed==0:
+        out_put='Speed cannot be 0!'
+        return out_put
+    if input_eluent==None:
+        out_put='Please input eluent!'
+        return out_put
+
     speed = []
     eluent = []
     smiles=[smile_1,smile_2]
@@ -1475,8 +1485,7 @@ def predict_separate(smile_1, smile_2, input_eluent, input_speed, input_column):
         pred, h_graph = model(data_atom_bond.to(device), data_bond_angle.to(device))
 
         y_pred.append(pred.detach().cpu().data.numpy() / speed[i])
-    if input_speed==0:
-        out_put='Speed cannot be 0!'
+
     else:
         Sp=cal_prob(y_pred)
         output_1=f'For smile_1,\n the predicted value is: {str(np.round(y_pred[0][0][1],3))}\n'
@@ -1487,6 +1496,15 @@ def predict_separate(smile_1, smile_2, input_eluent, input_speed, input_column):
 
 
 def column_recommendation(smile_1, smile_2, input_eluent, input_speed):
+    if input_speed==None:
+        out_put='Please input Speed!'
+        return out_put
+    if input_speed==0:
+        out_put='Speed cannot be 0!'
+        return out_put
+    if input_eluent==None:
+        out_put='Please input eluent!'
+        return out_put
     speed = []
     eluent = []
     Prediction = []
@@ -1588,7 +1606,7 @@ def column_recommendation(smile_1, smile_2, input_eluent, input_speed):
 
 
 if __name__=='__main__':
-     model_card = f"""
+    model_card = f"""
     ## Description\n
     It is a app for predicting retention times in HPLC and recommend the best HPLC column type for chromatographic enantioseparation.\n\n
     Input:\n