Upload 3 files

app.py

@@ -4,6 +4,7 @@ import numpy as np
 import pandas as pd
 import plotly.express as px
 
+
 # Load the models
 scaler = joblib.load('scaler.joblib')
 model = joblib.load('best_rf.joblib')
@@ -44,7 +45,11 @@ def scale_output(prediction_matrix):
 
 # Streamlit App
 def main():
-    st.title("User distribution prediction")
+    st.title("Consumer liking score distribution prediction")
+    st.markdown("Please adjust the values of 21 sensory attributes (assumed to have the range from 1 - 10), and see the liking score distribution below")
+    
+    with st.expander("Feature importance heatmap of sensory attributes on liking score"):
+        st.image("/Users/carlosito/Documents/vsCode/exercise-MMR/mmr-corr.png")
     
     col1, col2, col3 = st.columns(3)
     
@@ -91,12 +96,11 @@ def main():
     handout_pred_df.columns = [str(i) for i in range(1,10)]
     handout_pred_df['prod'] = 'prediction'
     handout_pred_df = handout_pred_df.melt('prod')
-    # handout_pred_df['type'] = 'handout_test'
+
+    # st.write(f"prediction: {handout_prediction  }")
+    st.write(f"sum of probability: {np.round(handout_prediction.sum(), 2)}")
+    
     
-    # st.write(f"select data: {data_input  }")
-    # st.write(f"scaled select data: {scaled_data_input  }")
-    st.write(f"prediction: {handout_prediction  }")
-    st.write(f"sum of probability: {handout_prediction.sum()}")
     
     fig = px.histogram(data_frame=handout_pred_df, x= 'variable',y='value', nbins= 9,
                      text_auto=True, title="Probability prediction of liking score from sensory attributes",

best_rf.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c9628ee04279903b0435981063ef25122a1a573ba8d79a34f2f801cdfcf96713
+oid sha256:ac8b3eb99b91d7c52fe16cc49c678835f36ede0fece460be9d8e3a7eed228c08
 size 1775335