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import streamlit as st
import plotly.express as px
import numpy as np
import plotly.graph_objects as go
from sklearn.metrics import r2_score
from collections import OrderedDict
import pickle
import json
import streamlit as st
import plotly.express as px
import numpy as np
import plotly.graph_objects as go
from sklearn.metrics import r2_score
import pickle
import json
import pandas as pd
import statsmodels.api as sm
from sklearn.metrics import mean_absolute_percentage_error
import sys
from utilities import (set_header,
initialize_data,
load_local_css,
create_channel_summary,
create_contribution_pie,
create_contribuion_stacked_plot,
create_channel_spends_sales_plot,
format_numbers,
channel_name_formating,
load_authenticator)
def plot_actual_vs_predicted(date, y, predicted_values, model):
fig = go.Figure()
fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='#6c757d')))
fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='#FF3A3B')))
# Calculate MAPE
mape = mean_absolute_percentage_error(y, predicted_values)
# Calculate AdjR2 # Assuming X is your feature matrix
adjr2 = model.rsquared_adj
# Create a table to display the metrics
metrics_table = pd.DataFrame({
'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
'Value': [mape, model.rsquared, adjr2]
})
fig.update_layout(
xaxis=dict(title='Date'),
yaxis=dict(title='Value'),
xaxis_tickangle=-30
)
#metrics_table.set_index(['Metric'],inplace=True)
return metrics_table, fig
X=pd.read_csv('actual_data.csv')
y=X['total_prospect_id']
date=X['date']
X=X.drop(['total_prospect_id','date','Unnamed: 0'],axis=1)
print(X.columns)
original_stdout = sys.stdout
sys.stdout = open('temp_stdout.txt', 'w')
# Perform linear regression
model = sm.OLS(y, X).fit()
sys.stdout.close()
sys.stdout = original_stdout
st.set_page_config(layout='wide')
load_local_css('styles.css')
set_header()
st.title('Analysis of Result')
st.write(model.summary(yname='Prospects'))
st.subheader('Actual vs Predicted Plot')
metrics_table,fig = plot_actual_vs_predicted(date, y, model.predict(X), model)
st.plotly_chart(fig,use_container_width=True)
#st.plotly_chart(fig)
# Display the metrics table
metrics_table=np.round(metrics_table,2)
metrics_table_html = metrics_table.to_html(index=False, escape=False)
# Display the metrics table in Streamlit as HTML
#st.subheader('Model Metrics')
#st.markdown(metrics_table_html, unsafe_allow_html=True)
# st.subheader('Model Metrics')
# st.table(metrics_table)
custom_css = """
<style>
table {
width: 80%; /* Adjust the table width as needed */
border-collapse: collapse;
}
th, td {
padding: 8px;
text-align: left;
border-bottom: 1px solid #ddd;
}
</style>
"""
# Display the metrics table in Streamlit as HTML with custom CSS
st.subheader('Model Metrics')
st.markdown(custom_css, unsafe_allow_html=True)
st.markdown(metrics_table_html, unsafe_allow_html=True)
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