import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

st.title('Huayra app')

df = pd.read_csv(r'CBT.csv')
df1 = df.drop('UserID',axis=1)

st.write("# <center><span style='font-size:24px'>Here is the DataFrame after cleaning</span></center>", unsafe_allow_html=True)
st.dataframe(df1)

st.image('xgboost log error.png')
st.image('classfication auc.png')

st.write("Higher AUC values indicate that the model performs better in distinguishing between positive and negative samples.")
st.write("We get an AUC of **1** for the training set and **0.9985630707839712** for the test set. This indicates that the model performs very well.")
st.image('precision recall curve.png')
st.image('receiver operating character example.png')

st.write("The P-R Curve (Precision-Recall Curve) is a curve used to evaluate the performance of a binary classification model, which shows the precision of the model under different recall rates.") 

st.write("Ideally, the P-R curve should be as close to the upper right as possible, which means that the model maintains a high precision at high recall.")
st.image('predicted values.png')