cleaning

app.py

@@ -14,8 +14,7 @@ from PIL import Image
 from pycaret.regression import pull,predict_model
 
 
-st.set_page_config(page_title="Customer Value Prediction Model", layout="wide") # , layout="wide"
-print("me")
+st.set_page_config(page_title="Customer Value Prediction Model", layout="wide")
 
 col1,col2 = st.columns([1,2])
 
@@ -78,9 +77,7 @@ elif option3 == 'PandasProfiling':
      with st.expander("See Report"):
           components.html(source_code, height=600, scrolling=True)
 
-
 model_names = [
-     "LGBM",
      "Logistic_Regression",
      "Support_Vector_Machine",
      "Support_Vector_Machine_Optimized",
@@ -88,6 +85,7 @@ model_names = [
      "Neural_Network",
      "Random_Forest",
      "Pycaret_Best",
+     "LGBM",
      "Lasso"
      ]
 
@@ -96,7 +94,8 @@ option = st.selectbox(
      'Select a model to be used',
      model_names
      )
-tr_df=model_value_prediction.important_feat(train_df,option)
+
+tr_df = model_value_prediction.important_feat(train_df,option)
 model = pickle.load(open(option+'.pkl', 'rb'))
 
 st.write("Model Loaded :  ", option)
@@ -105,11 +104,10 @@ train_X,test_X,train_y,dev_X,val_X,dev_y,val_y,test_y= model_value_prediction.pr
 
 model = model_value_prediction.train(tr_df,option)
 y_pred = model_value_prediction.predict(test_X,model,option)
-if option=="LGBM":
-    st.write("LGBM Score:",metrics.mean_squared_error(test_y, y_pred,squared=False))
-elif option=="Pycaret_Best":
+
+if option == "Pycaret_Best":
     predict_model(model)
-    st.write("Pycaret_Best Score:",pull())
+    st.write("RMSLE Score:", pull()['RMSLE'][0])
 else:
-    st.write("RMSLE Score:",metrics.mean_squared_log_error(test_y, y_pred,squared=False))
-    st.write("Poisson Score:",metrics.mean_tweedie_deviance(test_y, y_pred))
+    st.write("RMSLE Score:", metrics.mean_squared_log_error(test_y, y_pred, squared=False))
+    st.write("Poisson Score:", metrics.mean_tweedie_deviance(test_y, y_pred))