Update app.py

app.py

@@ -115,7 +115,7 @@ def train_model(X,labels):
         return
     train_data = lgb.Dataset(X, label=labels)
     num_round = 10
-    param = {'num_leaves':30, 'objective': 'binary', 'metric' : 'auc', 'lambda_l2': 1.}
+    param = {'num_leaves':50, 'objective': 'binary', 'metric' : 'auc', 'lambda_l2': 0.1, 'max_depth':5}
     bst = lgb.train(param, train_data, num_round)
     in_sample_preds = bst.predict(X)
     in_sample_score = np.corrcoef([in_sample_preds,np.array(labels)])[0][1]
@@ -177,7 +177,12 @@ if st.session_state.name:
     setup_user()
     
     st.subheader(f"Let's start {st.session_state.name}")
-    my_bar = st.progress(min(st.session_state.count/40,1.))
+    c1,c2 = st.columns(2)
+    my_bar = c1.progress(min(st.session_state.count/40,1.))
+    p_liked = (st.session_state.pos / st.session_state.count) if st.session_state.count else 0
+    c2.metric('%age liked so far',f'{p_liked:.1%}')
+
+    
     filename = get_filename()
     
     
@@ -190,12 +195,17 @@ if st.session_state.name:
     c1,c2 = cc2.columns(2)
     if st.session_state.count>30 and st.session_state.pos > 5 and st.session_state.neg > 5:
         c1.button('Train',on_click=train,args=[])
+        if st.session_state.count == 31:
+            st.balloons()
+            st.toast('Ready for training')
     c2.button('Start over',on_click=cleanup,args=[])
     if 'preds' in st.session_state:
         
         cc1.write('Here is our guess')
-        cc1.metric("Probability you will like the person above", f'{st.session_state.pred:.1%}')
-    
+        c1,c2 = cc1.columns(2)
+        c1.metric("Probability you will like", f'{st.session_state.pred:.1%}')
+        c2.metric("Overall model accuracy", f'{st.session_state.score:.0%}')
+        
         best,worst = get_extremes()
         
         cc2.subheader('Predicted best')
@@ -210,7 +220,7 @@ if st.session_state.name:
             c.metric("", f'{pred:.0%}')
             c.image(get_s3_url(file), width = 100)
 
-        cc1.metric("Overall model accuracy", f'{st.session_state.score:.0%}')
+        
 
         cc1.subheader('Where you confused me')