Small aesthetics fix

app.py

@@ -75,14 +75,14 @@ plt.tight_layout()
 col1.pyplot(fig)
 
 fig2, ax = plt.subplots()
-sns.distplot(df["upvote_ratio"], ax=ax)
+sns.distplot(df["upvote_ratio"], ax=ax, kde=False)
 ax.set_ylabel("Number of posts")
 plt.tight_layout()
 col2.pyplot(fig2)
 
 # Performance metrics
 st.markdown("## Performance Metrics")
-st.markdown("The model achieved the below scores on the test set. Please keep the effect of the sample weights in mind as explained in the Github repository. These reduce for example the R2 score from 0.75 to roughly 0. However, despite these low scores, the model is more useful in practice as it provides a meaningful lower bound estimate of the likes to be received as opposed to overestimating every post by up to 1500")
+st.markdown("The model achieved the below scores on the test set. Please keep the effect of the sample weights in mind as explained in the Github repository. These reduce for example the R2 score from 0.75 to roughly 0.05. However, despite these low scores, the model is more useful in practice as it provides a meaningful lower bound estimate of the likes to be received as opposed to overestimating every post by up to 1500")
 st.dataframe(df_metrics)
 
 # Prediction error plots