Update app.py

app.py

@@ -3,6 +3,7 @@ import json
 import numpy as np
 import streamlit as st
 import onnxruntime as rt
+import streamlit.components.v1 as components
 from sklearn.preprocessing import StandardScaler
 from sklearn.ensemble import RandomForestClassifier
 
@@ -177,7 +178,7 @@ elif st.session_state.question_no == 10:
     out = modelSession.run(None, {input_name: test_data})[1]
     # out = model.predict([st.session_state.pregnancies, st.session_state.glucose, st.session_state.bp, st.session_state.bmi, st.session_state.dpf])
     out = softmax(list(out[0].values()))[1]
-    out = (out - 0.44)*33.3333
+    out = (out - 0.45)*50
     out = 0 if out < 0 else 1 if out > 1 else out
     risk = "low" if out <= 0.3 else "medium" if out <= 0.6 else "high" if out <= 0.9 else "very high"
 
@@ -192,11 +193,11 @@ elif st.session_state.question_no == 10:
         st.rerun()  # Reloads the app to reflect the changes
 
     if st.button("Return to homepage"):
-        st.markdown("""
+        components.html("""
                 <script>
                     window.location.href = "https://supreme-shrestha.github.io/LifeGlyc/";
                 </script>
-        """, unsafe_allow_html = True)
+        """, height = 0,)