Update app.py

app.py

@@ -1,897 +1,335 @@
 import streamlit as st
-
-import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-import os
-import warnings
-
-warnings.filterwarnings('ignore')
-
-
-# In[90]:
-
-
-# In[91]:
+import joblib
 
 def main():
-#     st.title("FIFA Data visualization")
-
-
-# df = pd.read_csv('./data/international_matches.csv', parse_dates=['date'])
-# # df.tail()
-# #
-
-# # In[92]:
-
-
-# # df.columns
-
-
-# # In[93]:
-
-
-# # df.isnull().sum()
-
-
-# # # PRE-ANALYSIS
-# # The dataset has a lot of blank fields that need to be fixed.
-# # However, before modifying any field, I want to analyze the teams' qualifications on the last FIFA date (June 2022). This is important because, from these qualifications, I will create the inference dataset that enters the machine learning algorithm that predicts the World Cup matches.
-
-# # ### Top 10 FIFA Ranking
-# # Top 10 national teams to date FIFA June 2022.
-# # **ref:** https://www.fifa.com/fifa-world-ranking/men?dateId=id13603
-
-
-# # In[94]:
-
-
-# fifa_rank = df[['date', 'home_team', 'away_team', 'home_team_fifa_rank', 'away_team_fifa_rank']]
-# home = fifa_rank[['date', 'home_team', 'home_team_fifa_rank']].rename(
-#     columns={"home_team": "team", "home_team_fifa_rank": "rank"})
-# away = fifa_rank[['date', 'away_team', 'away_team_fifa_rank']].rename(
-#     columns={"away_team": "team", "away_team_fifa_rank": "rank"})
-# fifa_rank = pd.concat([home, away])
-# # Select each country latest match
-# fifa_rank = fifa_rank.sort_values(['team', 'date'], ascending=[True, False])
-# last_rank = fifa_rank
-# fifa_rank_top10 = fifa_rank.groupby('team').first().sort_values('rank', ascending=True)[0:10].reset_index()
-
-
-# # fifa_rank_top10
-
-
-# # ### Top 10 teams with the highest winning percentage at home and away
-
-# # In[95]:
-
-
-# def home_percentage(team):
-#     score = len(df[(df['home_team'] == team) & (df['home_team_result'] == "Win")]) / len(
-#         df[df['home_team'] == team]) * 100
-#     return round(score)
-
-
-# def away_percentage(team):
-#     score = len(df[(df['away_team'] == team) & (df['home_team_result'] == "Lose")]) / len(
-#         df[df['away_team'] == team]) * 100
-#     return round(score)
-
-
-# # In[96]:
-
-
-# fifa_rank_top10['Home_win_Per'] = np.vectorize(home_percentage)(fifa_rank_top10['team'])
-# fifa_rank_top10['Away_win_Per'] = np.vectorize(away_percentage)(fifa_rank_top10['team'])
-# fifa_rank_top10['Average_win_Per'] = round((fifa_rank_top10['Home_win_Per'] + fifa_rank_top10['Away_win_Per']) / 2)
-# fifa_rank_win = fifa_rank_top10.sort_values('Average_win_Per', ascending=False)
-# # fifa_rank_win
-
-
-# # ### Top 10 attacking teams in the last FIFA date
-
-# # In[97]:
-
-
-# fifa_offense = df[['date', 'home_team', 'away_team', 'home_team_mean_offense_score', 'away_team_mean_offense_score']]
-# home = fifa_offense[['date', 'home_team', 'home_team_mean_offense_score']].rename(
-#     columns={"home_team": "team", "home_team_mean_offense_score": "offense_score"})
-# away = fifa_offense[['date', 'away_team', 'away_team_mean_offense_score']].rename(
-#     columns={"away_team": "team", "away_team_mean_offense_score": "offense_score"})
-# fifa_offense = pd.concat([home, away])
-# fifa_offense = fifa_offense.sort_values(['date', 'team'], ascending=[False, True])
-# last_offense = fifa_offense
-# fifa_offense_top10 = fifa_offense.groupby('team').first().sort_values('offense_score', ascending=False)[
-#                      0:10].reset_index()
-# # fifa_offense_top10
-
-# import plotly.graph_objs as go
-# import plotly.figure_factory as ff
-
-# # In[99]:
-
-# # Display the data for the bar chart
-# st.write("Top 10 Attacking Teams")
-# st.write(fifa_offense_top10)
-
-# # Create a horizontal bar chart
-# fig_bar = go.Figure(data=[go.Bar(y=fifa_offense_top10['team'], x=fifa_offense_top10['offense_score'], orientation='h')])
-# # Update layout to include title, x-label, and y-label
-# fig_bar.update_layout(title='Top 10 Attacking Teams',
-#                       xaxis_title='Offense Score',
-#                       yaxis_title='Team')
-# st.plotly_chart(fig_bar)
-
-# # Display the data for the bar chart
-# # st.write("Top 10 Offense Teams")
-# # st.write(fifa_offense_top10)
-
-# # sns.barplot(data=fifa_offense_top10, x='offense_score', y='team', color="#7F1431")
-# # plt.xlabel('Offense Score', size = 20)
-# # plt.ylabel('Team', size = 20)
-# # plt.title("Top 10 Attacking teams");
-
-
-# # ### Top 10 Midfield teams in the last FIFA date
-
-# # In[100]:
-
-
-# fifa_midfield = df[['date', 'home_team', 'away_team', 'home_team_mean_midfield_score', 'away_team_mean_midfield_score']]
-# home = fifa_midfield[['date', 'home_team', 'home_team_mean_midfield_score']].rename(
-#     columns={"home_team": "team", "home_team_mean_midfield_score": "midfield_score"})
-# away = fifa_midfield[['date', 'away_team', 'away_team_mean_midfield_score']].rename(
-#     columns={"away_team": "team", "away_team_mean_midfield_score": "midfield_score"})
-# fifa_midfield = pd.concat([home, away])
-# fifa_midfield = fifa_midfield.sort_values(['date', 'team'], ascending=[False, True])
-# last_midfield = fifa_midfield
-# fifa_midfield_top10 = fifa_midfield.groupby('team').first().sort_values('midfield_score', ascending=False)[
-#                       0:10].reset_index()
-# # fifa_midfield_top10
-
-
-# # In[101]:
-
-# # Display the data for the bar chart
-# st.write("Top 10 Midfield Teams")
-# st.write(fifa_midfield_top10)
-
-# # Create a horizontal bar chart
-# fig_bar = go.Figure(
-#     data=[go.Bar(y=fifa_midfield_top10['team'], x=fifa_midfield_top10['midfield_score'], orientation='h')])
-# # Update layout to include title, x-label, and y-label
-# fig_bar.update_layout(title='Top 10 Midfield Teams',  # Set the title
-#                       xaxis_title='Midfield Score',  # Set the x-axis label
-#                       yaxis_title='Team')  # Set the y-axis label
-
-# # Display the bar chart
-# st.plotly_chart(fig_bar)
-
-# # sns.barplot(data=fifa_midfield_top10, x='midfield_score', y='team', color="#7F1431")
-# # plt.xlabel('Midfield Score', size = 20)
-# # plt.ylabel('Team', size = 20)
-# # plt.title("Top 10 Midfield teams");
-
-
-# # ### Top 10 defending teams in the last FIFA date
-
-# # In[102]:
-
-
-# fifa_defense = df[['date', 'home_team', 'away_team', 'home_team_mean_defense_score', 'away_team_mean_defense_score']]
-# home = fifa_defense[['date', 'home_team', 'home_team_mean_defense_score']].rename(
-#     columns={"home_team": "team", "home_team_mean_defense_score": "defense_score"})
-# away = fifa_defense[['date', 'away_team', 'away_team_mean_defense_score']].rename(
-#     columns={"away_team": "team", "away_team_mean_defense_score": "defense_score"})
-# fifa_defense = pd.concat([home, away])
-# fifa_defense = fifa_defense.sort_values(['date', 'team'], ascending=[False, True])
-# last_defense = fifa_defense
-# fifa_defense_top10 = fifa_defense.groupby('team').first().sort_values('defense_score', ascending=False)[
-#                      0:10].reset_index()
-# # fifa_defense_top10
-
-
-# # In[103]:
-
-# # Display the data for the bar chart
-# st.write("Top 10 Defensive Teams")
-# st.write(fifa_defense_top10)
-
-# # Create the horizontal bar chart
-# fig_bar = go.Figure(data=[go.Bar(y=fifa_defense_top10['team'], x=fifa_defense_top10['defense_score'], orientation='h')])
-
-# # Update layout to include title, x-label, and y-label
-# fig_bar.update_layout(title='Top 10 Defensive Teams',  # Set the title
-#                       xaxis_title='Defense Score',  # Set the x-axis label
-#                       yaxis_title='Team')  # Set the y-axis label
-
-# # Display the bar chart
-# st.plotly_chart(fig_bar)
-
-# # sns.barplot(data=fifa_defense_top10, x='defense_score', y='team', color="#7F1431")
-# # plt.xlabel('Defense Score', size=20)
-# # plt.ylabel('Team', size=20)
-# # plt.title("Top 10 Defense Teams")
-
-# # ### Do Home teams have any advantage?
-
-# # In[104]:
-
-
-# # Select all matches played at non-neutral locations
-# home_team_advantage = df[df['neutral_location'] == False]['home_team_result'].value_counts(normalize=True)
-
-# # # Plot
-# # fig, axes = plt.subplots(1, 1, figsize=(8,8))
-# # ax =plt.pie(home_team_advantage  ,labels = ['Win', 'Lose', 'Draw'], autopct='%.0f%%')
-# # plt.title('Home team match result', fontsize = 15)
-# # plt.show()
-
-
-# # As the graph shows, the home team has an advantage over the away team. This is due to factors such as the fans, the weather and the confidence of the players. For this reason, in the World Cup, those teams that sit at home will have an advantage.
-
-# # # DATA PREPARATION AND FEATURE ENGINEERING
-# # In this section, I will fill in the empty fields in the dataset and clean up the data for teams that did not qualify for the World Cup. Then, I will use the correlation matrix to choose the characteristics that will define the training dataset of the Machine Learning model. Finally, I will use the ratings of the teams in their last match to define the "Last Team Scores" dataset (i.e., the dataset that I will use to predict the World Cup matches).
-
-# # ### Analyze and fill na's
-
-# # In[105]:
-
-# #
-# # df.isnull().sum()
-
-
-# # In[106]:
-
-
-# # We can fill mean for na's in goal_keeper_score
-# df[df['home_team'] == "Brazil"]['home_team_goalkeeper_score'].describe()
-
-# # In[107]:
-
-
-# df['home_team_goalkeeper_score'] = round(
-#     df.groupby("home_team")["home_team_goalkeeper_score"].transform(lambda x: x.fillna(x.mean())))
-# df['away_team_goalkeeper_score'] = round(
-#     df.groupby("away_team")["away_team_goalkeeper_score"].transform(lambda x: x.fillna(x.mean())))
-
-# # In[108]:
-
-
-# # We can fill mean for na's in defense score
-# df[df['away_team'] == "Uruguay"]['home_team_mean_defense_score'].describe()
-
-# # In[65]:
-
-
-# df['home_team_mean_defense_score'] = round(
-#     df.groupby('home_team')['home_team_mean_defense_score'].transform(lambda x: x.fillna(x.mean())))
-# df['away_team_mean_defense_score'] = round(
-#     df.groupby('away_team')['away_team_mean_defense_score'].transform(lambda x: x.fillna(x.mean())))
-
-# # In[109]:
-
-
-# # We can fill mean for na's in offense score
-# df[df['away_team'] == "Uruguay"]['home_team_mean_offense_score'].describe()
-
-# # In[67]:
-
-
-# df['home_team_mean_offense_score'] = round(
-#     df.groupby('home_team')['home_team_mean_offense_score'].transform(lambda x: x.fillna(x.mean())))
-# df['away_team_mean_offense_score'] = round(
-#     df.groupby('away_team')['away_team_mean_offense_score'].transform(lambda x: x.fillna(x.mean())))
-
-# # In[110]:
-
-
-# # We can fill mean for na's in midfield score
-# df[df['away_team'] == "Uruguay"]['home_team_mean_midfield_score'].describe()
-
-# # In[111]:
-
-
-# df['home_team_mean_midfield_score'] = round(
-#     df.groupby('home_team')['home_team_mean_midfield_score'].transform(lambda x: x.fillna(x.mean())))
-# df['away_team_mean_midfield_score'] = round(
-#     df.groupby('away_team')['away_team_mean_midfield_score'].transform(lambda x: x.fillna(x.mean())))
-
-# # In[112]:
-
-
-# df.isnull().sum()
-
-# # In[113]:
-
-
-# # Teams are not available in FIFA game itself, so they are not less than average performing teams, so giving a average score of 50 for all.
-# df.fillna(50, inplace=True)
-
-# # ### Filter the teams participating in QATAR - World cup 2022
-
-# # In[115]:
-
-
-# list_2022 = ['Qatar', 'Germany', 'Denmark', 'Brazil', 'France', 'Belgium', 'Croatia', 'Spain', 'Serbia', 'England',
-#              'Switzerland', 'Netherlands', 'Argentina', 'IR Iran', 'Korea Republic', 'Japan', 'Saudi Arabia', 'Ecuador',
-#              'Uruguay', 'Canada', 'Ghana', 'Senegal', 'Portugal', 'Poland', 'Tunisia', 'Morocco', 'Cameroon', 'USA',
-#              'Mexico', 'Wales', 'Australia', 'Costa Rica']
-# final_df = df[(df["home_team"].apply(lambda x: x in list_2022)) | (df["away_team"].apply(lambda x: x in list_2022))]
-
-# # **Top 10 teams in QATAR 2022**
-
-# # In[116]:
-
-
-# rank = final_df[['date', 'home_team', 'away_team', 'home_team_fifa_rank', 'away_team_fifa_rank']]
-# home = rank[['date', 'home_team', 'home_team_fifa_rank']].rename(
-#     columns={"home_team": "team", "home_team_fifa_rank": "rank"})
-# away = rank[['date', 'away_team', 'away_team_fifa_rank']].rename(
-#     columns={"away_team": "team", "away_team_fifa_rank": "rank"})
-# rank = pd.concat([home, away])
-
-# # Select each country latest match
-# rank = rank.sort_values(['team', 'date'], ascending=[True, False])
-# rank_top10 = rank.groupby('team').first().sort_values('rank', ascending=True).reset_index()
-# rank_top10 = rank_top10[(rank_top10["team"].apply(lambda x: x in list_2022))][0:10]
-
-# st.write("Top 10 Countries by Rank - Latest Match")
-# rank_top10
-
-# # # Create a scatter plot
-# # fig_scatter = go.Figure(data=go.Scatter(x=rank_top10['team'], y=rank_top10['rank'], mode='markers', marker=dict(color='lightskyblue', size=12)))
-# #
-# # # Update layout to include title and labels
-# # fig_scatter.update_layout(title='Top 10 Countries by Rank - Latest Match',
-# #                           xaxis_title='Country',
-# #                           yaxis_title='Rank')
-# #
-# # # Display the scatter plot
-# # st.plotly_chart(fig_scatter)
-
-# # **Top 10 teams with the highest winning percentage in QATAR 2022**
-
-# # In[117]:
-
-
-# rank_top10['Home_win_Per'] = np.vectorize(home_percentage)(rank_top10['team'])
-# rank_top10['Away_win_Per'] = np.vectorize(away_percentage)(rank_top10['team'])
-# rank_top10['Average_win_Per'] = round((rank_top10['Home_win_Per'] + rank_top10['Away_win_Per']) / 2)
-# rank_top10_Win = rank_top10.sort_values('Average_win_Per', ascending=False)
-
-# # st.write("Top 10 Countries by Rank - Latest Match")
-# # rank_top10_Win
-
-
-# # In[118]:
-
-# # Display the data for the bar chart
-# st.write("Top 10 Average Win Per game Teams")
-# st.write(rank_top10_Win)
-
-# # Create a horizontal bar chart
-# # Create a horizontal bar chart
-# fig_bar = go.Figure(data=[go.Bar(y=rank_top10_Win['team'], x=rank_top10_Win['Average_win_Per'], orientation='h')])
-
-# # Update layout to include title and labels
-# fig_bar.update_layout(title='Top 10 Countries by Average Win Percentage',
-#                       xaxis_title='Average Win Percentage',
-#                       yaxis_title='Country')
-
-# # Display the horizontal bar chart
-# st.plotly_chart(fig_bar)
-
-# sns.barplot(data=rank_top10_Win,x='Average_win_Per',y='team',color="#7F1431")
-# plt.xticks()
-# plt.xlabel('Win Average', size = 20)
-# plt.ylabel('Team', size = 20)
-# plt.title('Top 10 QATAR 2022 teams with the highest winning percentage')
-
-#
-# # ### Correlation Matrix
-#
-# # In[124]:
-#
-#
-# final_df['home_team_result'].values
-# # for index, value in final_df['home_team_result'].items():
-# #     print(f"Row {index}: {value}")
-#
-#
-# # In[125]:
-#
-#
-# team_result_df = final_df
-# # for index, value in team_result_df['home_team_result'].items():
-# #     print(f"Row {index}: {value}")
-#
-#
-# # In[151]:
-#
-#
-# # Mapping numeric values for home_team_result to find the correleations
-# final_df['home_team_result'] = final_df['home_team_result'].map({'Win':1, 'Draw':2, 'Lose':0})
-#
-#
-# # In[145]:
-#
-#
-#
-#
-#
-# # In[150]:
-#
-#
-# final_df['home_team_result'].head(1)
-#
-#
-# # In[152]:
-#
-#
-# final_df['home_team_result'] = pd.to_numeric(final_df['home_team_result'], errors='coerce')
-#
-#
-# # In[155]:
-#
-#
-# # df.head()
-#
-#
-# # In[156]:
-#
-#
-# # final_df.head()
-#
-#
-# # In[157]:
-#
-#
-# numerical_df = final_df.select_dtypes(include=['number'])
-#
-#
-# # In[158]:
-#
-#
-# numerical_df.corr()['home_team_result'].sort_values(ascending=False)
-#
-#
-# # In[153]:
-#
-#
-# # final_df.corr()['home_team_result'].sort_values(ascending=False)
-#
-#
-# # Dropping unnecessary colums.
-#
-# # In[ ]:
-#
-#
-# #Dropping unnecessary colums
-# final_df = final_df.drop(['date', 'home_team_continent', 'away_team_continent', 'home_team_total_fifa_points', 'away_team_total_fifa_points', 'home_team_score', 'away_team_score', 'tournament', 'city', 'country', 'neutral_location', 'shoot_out'],axis=1)
-#
-#
-# # In[ ]:
-#
-#
-# # final_df.columns
-#
-#
-# # In[ ]:
-#
-#
-# # Change column names
-# final_df.rename(columns={"home_team":"Team1", "away_team":"Team2", "home_team_fifa_rank":"Team1_FIFA_RANK",
-#                          "away_team_fifa_rank":"Team2_FIFA_RANK", "home_team_result":"Team1_Result", "home_team_goalkeeper_score":"Team1_Goalkeeper_Score",
-#                         "away_team_goalkeeper_score":"Team2_Goalkeeper_Score", "home_team_mean_defense_score":"Team1_Defense",
-#                         "home_team_mean_offense_score":"Team1_Offense", "home_team_mean_midfield_score":"Team1_Midfield",
-#                         "away_team_mean_defense_score":"Team2_Defense", "away_team_mean_offense_score":"Team2_Offense",
-#                         "away_team_mean_midfield_score":"Team2_Midfield"}, inplace=True)
-#
-#
-# # In[ ]:
-#
-#
-# plt.figure(figsize=(10, 4), dpi=200)
-# sns.heatmap(final_df.corr(), annot=True)
-#
-#
-# # In[ ]:
-#
-#
-# # final_df.info()
-#
-#
-# # In[ ]:
-#
-#
-# # final_df
-#
-#
-# # Exporting the training dataset.
-#
-# # In[ ]:
-#
-#
-# # final_df.to_csv("./data/training.csv", index = False)
-#
-#
-# # ### Creating "Last Team Scores" dataset
-# # This dataset contains the qualifications of each team on the previous FIFA date and will be used to predict the World Cup matches.
-#
-# # In[ ]:
-#
-#
-# last_goalkeeper = df[['date', 'home_team', 'away_team', 'home_team_goalkeeper_score', 'away_team_goalkeeper_score']]
-# home = last_goalkeeper[['date', 'home_team', 'home_team_goalkeeper_score']].rename(columns={"home_team":"team", "home_team_goalkeeper_score":"goalkeeper_score"})
-# away = last_goalkeeper[['date', 'away_team', 'away_team_goalkeeper_score']].rename(columns={"away_team":"team", "away_team_goalkeeper_score":"goalkeeper_score"})
-# last_goalkeeper = pd.concat([home,away])
-#
-# last_goalkeeper = last_goalkeeper.sort_values(['date', 'team'],ascending=[False, True])
-#
-# list_2022 = ['Qatar', 'Germany', 'Denmark', 'Brazil', 'France', 'Belgium', 'Croatia', 'Spain', 'Serbia', 'England', 'Switzerland', 'Netherlands', 'Argentina', 'IR Iran', 'Korea Republic', 'Japan', 'Saudi Arabia', 'Ecuador', 'Uruguay', 'Canada', 'Ghana', 'Senegal', 'Portugal', 'Poland', 'Tunisia', 'Morocco', 'Cameroon', 'USA', 'Mexico', 'Wales', 'Australia', 'Costa Rica']
-#
-# rank_qatar = last_rank[(last_rank["team"].apply(lambda x: x in list_2022))]
-# rank_qatar = rank_qatar.groupby('team').first().reset_index()
-# goal_qatar = last_goalkeeper[(last_goalkeeper["team"].apply(lambda x: x in list_2022))]
-# goal_qatar = goal_qatar.groupby('team').first().reset_index()
-# goal_qatar = goal_qatar.drop(['date'], axis = 1)
-# off_qatar = last_offense[(last_offense["team"].apply(lambda x: x in list_2022))]
-# off_qatar = off_qatar.groupby('team').first().reset_index()
-# off_qatar = off_qatar.drop(['date'], axis = 1)
-# mid_qatar = last_midfield[(last_midfield["team"].apply(lambda x: x in list_2022))]
-# mid_qatar = mid_qatar.groupby('team').first().reset_index()
-# mid_qatar = mid_qatar.drop(['date'], axis = 1)
-# def_qatar = last_defense[(last_defense["team"].apply(lambda x: x in list_2022))]
-# def_qatar = def_qatar.groupby('team').first().reset_index()
-# def_qatar = def_qatar.drop(['date'], axis = 1)
-#
-# qatar = pd.merge(rank_qatar, goal_qatar, on = 'team')
-# qatar = pd.merge(qatar, def_qatar, on ='team')
-# qatar = pd.merge(qatar, off_qatar, on ='team')
-# qatar = pd.merge(qatar, mid_qatar, on ='team')
-#
-# qatar['goalkeeper_score'] = round(qatar["goalkeeper_score"].transform(lambda x: x.fillna(x.mean())))
-# qatar['offense_score'] = round(qatar["offense_score"].transform(lambda x: x.fillna(x.mean())))
-# qatar['midfield_score'] = round(qatar["midfield_score"].transform(lambda x: x.fillna(x.mean())))
-# qatar['defense_score'] = round(qatar["defense_score"].transform(lambda x: x.fillna(x.mean())))
-# # qatar.head(5)
-#
-#
-# # Exporting the "Last Team Scores" dataset.
-#
-# # In[ ]:
-#
-
-
-st.title("FIFA winner predication")
-st.write('This app predict 2022 FIFA winner')
-
-if st.button("Predict FIFA Winner"):
-
-    last_team_scores = pd.read_csv('./data/last_team_scores.csv')
-    last_team_scores.tail()
-
-    squad_stats = pd.read_csv('./data/squad_stats.csv')
-    squad_stats.tail()
-
-    group_matches = pd.read_csv('./data/Qatar_group_stage.csv')
-    round_16 = group_matches.iloc[48:56, :]
-    quarter_finals = group_matches.iloc[56:60, :]
-    semi_finals = group_matches.iloc[60:62, :]
-    final = group_matches.iloc[62:63, :]
-    second_final = group_matches.iloc[63:64, :]
-    group_matches = group_matches.iloc[:48, :]
-    group_matches.tail()
-
-    xgb_gs_model = joblib.load("./groups_stage_prediction.pkl")
-
-    xgb_ks_model = joblib.load("./knockout_stage_prediction.pkl")
-
-    team_group = group_matches.drop(['country2'], axis=1)
-    team_group = team_group.drop_duplicates().reset_index(drop=True)
-    team_group = team_group.rename(columns={"country1": "team"})
-    team_group.head(5)
-
-    def matches(g_matches):
-        g_matches.insert(2, 'potential1',
-                         g_matches['country1'].map(squad_stats.set_index('nationality_name')['potential']))
-        g_matches.insert(3, 'potential2',
-                         g_matches['country2'].map(squad_stats.set_index('nationality_name')['potential']))
-        g_matches.insert(4, 'rank1', g_matches['country1'].map(last_team_scores.set_index('team')['rank']))
-        g_matches.insert(5, 'rank2', g_matches['country2'].map(last_team_scores.set_index('team')['rank']))
-        pred_set = []
-
-        for index, row in g_matches.iterrows():
-            if row['potential1'] > row['potential2'] and abs(row['potential1'] - row['potential2']) > 2:
-                pred_set.append({'Team1': row['country1'], 'Team2': row['country2']})
-            elif row['potential2'] > row['potential1'] and abs(row['potential2'] - row['potential1']) > 2:
-                pred_set.append({'Team1': row['country2'], 'Team2': row['country1']})
-            else:
-                if row['rank1'] > row['rank2']:
+    st.title("FIFA winner predication")
+    st.write('This app predict 2022 FIFA winner')
+
+    if st.button("Predict FIFA Winner"):
+
+        last_team_scores = pd.read_csv('./data/last_team_scores.csv')
+        last_team_scores.tail()
+
+        squad_stats = pd.read_csv('./data/squad_stats.csv')
+        squad_stats.tail()
+
+        group_matches = pd.read_csv('./data/Qatar_group_stage.csv')
+        round_16 = group_matches.iloc[48:56, :]
+        quarter_finals = group_matches.iloc[56:60, :]
+        semi_finals = group_matches.iloc[60:62, :]
+        final = group_matches.iloc[62:63, :]
+        second_final = group_matches.iloc[63:64, :]
+        group_matches = group_matches.iloc[:48, :]
+        group_matches.tail()
+
+        xgb_gs_model = joblib.load("./groups_stage_prediction.pkl")
+
+        xgb_ks_model = joblib.load("./knockout_stage_prediction.pkl")
+
+        team_group = group_matches.drop(['country2'], axis=1)
+        team_group = team_group.drop_duplicates().reset_index(drop=True)
+        team_group = team_group.rename(columns={"country1": "team"})
+        team_group.head(5)
+
+        def matches(g_matches):
+            g_matches.insert(2, 'potential1',
+                             g_matches['country1'].map(squad_stats.set_index('nationality_name')['potential']))
+            g_matches.insert(3, 'potential2',
+                             g_matches['country2'].map(squad_stats.set_index('nationality_name')['potential']))
+            g_matches.insert(4, 'rank1', g_matches['country1'].map(last_team_scores.set_index('team')['rank']))
+            g_matches.insert(5, 'rank2', g_matches['country2'].map(last_team_scores.set_index('team')['rank']))
+            pred_set = []
+
+            for index, row in g_matches.iterrows():
+                if row['potential1'] > row['potential2'] and abs(row['potential1'] - row['potential2']) > 2:
                     pred_set.append({'Team1': row['country1'], 'Team2': row['country2']})
-                else:
+                elif row['potential2'] > row['potential1'] and abs(row['potential2'] - row['potential1']) > 2:
                     pred_set.append({'Team1': row['country2'], 'Team2': row['country1']})
-
-        pred_set = pd.DataFrame(pred_set)
-        pred_set.insert(2, 'Team1_FIFA_RANK', pred_set['Team1'].map(last_team_scores.set_index('team')['rank']))
-        pred_set.insert(3, 'Team2_FIFA_RANK', pred_set['Team2'].map(last_team_scores.set_index('team')['rank']))
-        pred_set.insert(4, 'Team1_Goalkeeper_Score',
-                        pred_set['Team1'].map(last_team_scores.set_index('team')['goalkeeper_score']))
-        pred_set.insert(5, 'Team2_Goalkeeper_Score',
-                        pred_set['Team2'].map(last_team_scores.set_index('team')['goalkeeper_score']))
-        pred_set.insert(6, 'Team1_Defense', pred_set['Team1'].map(last_team_scores.set_index('team')['defense_score']))
-        pred_set.insert(7, 'Team1_Offense', pred_set['Team1'].map(last_team_scores.set_index('team')['offense_score']))
-        pred_set.insert(8, 'Team1_Midfield',
-                        pred_set['Team1'].map(last_team_scores.set_index('team')['midfield_score']))
-        pred_set.insert(9, 'Team2_Defense', pred_set['Team2'].map(last_team_scores.set_index('team')['defense_score']))
-        pred_set.insert(10, 'Team2_Offense', pred_set['Team2'].map(last_team_scores.set_index('team')['offense_score']))
-        pred_set.insert(11, 'Team2_Midfield',
-                        pred_set['Team2'].map(last_team_scores.set_index('team')['midfield_score']))
-        return pred_set
-
-    def print_results(dataset, y_pred, matches, proba):
-        results = []
-        for i in range(dataset.shape[0]):
-            print()
-            if y_pred[i] == 2:
-                print(matches.iloc[i, 0] + " vs. " + matches.iloc[i, 1] + " => Draw")
-                results.append({'result': 'Draw'})
-            elif y_pred[i] == 1:
-                print(matches.iloc[i, 0] + " vs. " + matches.iloc[i, 1] + " => Winner: " + dataset.iloc[i, 0])
-                results.append({'result': dataset.iloc[i, 0]})
-            else:
-                print(matches.iloc[i, 0] + " vs. " + matches.iloc[i, 1] + " => Winner: " + dataset.iloc[i, 1])
-                results.append({'result': dataset.iloc[i, 1]})
-            try:
-                print('Probability of ' + dataset.iloc[i, 0] + ' winning: ', '%.3f' % (proba[i][1]))
-                print('Probability of Draw: ', '%.3f' % (proba[i][2]))
-                print('Probability of ' + dataset.iloc[i, 1] + ' winning: ', '%.3f' % (proba[i][0]))
-            except:
-                print('Probability of ' + dataset.iloc[i, 1] + ' winning: ', '%.3f' % (proba[i][0]))
-            print("")
-        results = pd.DataFrame(results)
-        matches = pd.concat([matches.group, results], axis=1)
-        return matches
-
-    def winner_to_match(round, prev_match):
-        round.insert(0, 'c1', round['country1'].map(prev_match.set_index('group')['result']))
-        round.insert(1, 'c2', round['country2'].map(prev_match.set_index('group')['result']))
-        round = round.drop(['country1', 'country2'], axis=1)
-        round = round.rename(columns={'c1': 'country1', 'c2': 'country2'}).reset_index(drop=True)
-        return round
-
-    def prediction_knockout(round):
-        dataset_round = matches(round)
-        prediction_round = xgb_ks_model.predict(dataset_round)
-        proba_round = xgb_ks_model.predict_proba(dataset_round)
-
-        # prediction_round = ada_ks_model.predict(dataset_round)
-        # proba_round = ada_ks_model.predict_proba(dataset_round)
-
-        # prediction_round = rf_ks_model.predict(dataset_round)
-        # proba_round = rf_ks_model.predict_proba(dataset_round)
-
-        results_round = print_results(dataset_round, prediction_round, round, proba_round)
-        return results_round
-
-    def center_str(round):
-        spaces = ['', ' ', '  ', '   ', '    ', '     ', ]
-        for j in range(2):
-            for i in range(round.shape[0]):
-                if (13 - len(round.iloc[i, j])) % 2 == 0:
-                    round.iloc[i, j] = spaces[int((13 - len(round.iloc[i, j])) / 2)] + round.iloc[i, j] + spaces[
-                        int((13 - len(round.iloc[i, j])) / 2)]
                 else:
-                    round.iloc[i, j] = spaces[int(((13 - len(round.iloc[i, j])) / 2) - 0.5)] + round.iloc[i, j] + \
-                                       spaces[int(((13 - len(round.iloc[i, j])) / 2) + 0.5)]
-        return round
-
-    def center2(a):
-        spaces = ['', ' ', '  ', '   ', '    ', '     ', '      ', '       ', '        ', '         ', '          ',
-                  '           ', '            ', '             ', '              ', '               ',
-                  '                ', '                 ', '                  ', '                   ',
-                  '                    ']
-        if (29 - len(a)) % 2 == 0:
-            a = spaces[int((29 - len(a)) / 2)] + a + spaces[int((29 - len(a)) / 2)]
-        else:
-            a = spaces[int(((29 - len(a)) / 2) - 0.5)] + a + spaces[int(((29 - len(a)) / 2) + 0.5)]
-        return a
-
-    dataset_groups = matches(group_matches)
-    dataset_groups.tail()
-    print(dataset_groups)
-
-    prediction_groups = xgb_gs_model.predict(dataset_groups)
-    proba = xgb_gs_model.predict_proba(dataset_groups)
-
-    # prediction_groups = ada_gs_model.predict(dataset_groups)
-    # proba = ada_gs_model.predict_proba(dataset_groups)
-
-    # prediction_groups = rf_gs_model.predict(dataset_groups)
-    # proba = rf_gs_model.predict_proba(dataset_groups)
-
-    results = print_results(dataset_groups, prediction_groups, group_matches, proba)
-
-    team_group['points'] = 0
-    team_group
-    for i in range(results.shape[0]):
-        for j in range(team_group.shape[0]):
-            if results.iloc[i, 1] == team_group.iloc[j, 0]:
-                team_group.iloc[j, 2] += 3
-
-    print(team_group.groupby(['group', 'team']).mean().astype(int))
-
-    round_of_16 = team_group[team_group['points'] > 5].reset_index(drop=True)
-    round_of_16['group'] = (4 - 1 / 3 * round_of_16.points).astype(int).astype(str) + round_of_16.group
-    round_of_16 = round_of_16.rename(columns={"team": "result"})
-
-    round_16 = winner_to_match(round_16, round_of_16)
-    results_round_16 = prediction_knockout(round_16)
-
-    quarter_finals = winner_to_match(quarter_finals, results_round_16)
-    results_quarter_finals = prediction_knockout(quarter_finals)
-
-    semi_finals = winner_to_match(semi_finals, results_quarter_finals)
-    results_finals = prediction_knockout(semi_finals)
-
-    final = winner_to_match(final, results_finals)
-    winner = prediction_knockout(final)
-
-    second = results_finals[~results_finals.result.isin(winner.result)]
-    results_finals_3 = results_quarter_finals[~results_quarter_finals.result.isin(results_finals.result)]
-    results_finals_3.iloc[0, 0] = 'z1'
-    results_finals_3.iloc[1, 0] = 'z2'
-    second_final = winner_to_match(second_final, results_finals_3)
-    third = prediction_knockout(second_final)
-
-    round_16 = center_str(round_16)
-    quarter_finals = center_str(quarter_finals)
-    semi_finals = center_str(semi_finals)
-    final = center_str(final)
-    group_matches = center_str(group_matches)
-
-    # Function to center align text
-    def center(text):
-        return f"<div style='text-align: center;'>{text}</div>"
-
-    # Function to generate the formatted text
-    def generate_text(round_16, quarter_finals, semi_finals, final):
-        formatted_text = (
-                round_16.iloc[
-                    0, 0] + '━━━━┓                                                                                                                             ┏━━━━' +
-                round_16.iloc[4, 0] + '\n' +
-                '                 ┃                                                                                                                             ┃\n' +
-                '                 ┃━━━━' + quarter_finals.iloc[
-                    0, 0] + '━━━━┓                                                                                 ┏━━━━' +
-                quarter_finals.iloc[2, 0] + '━━━━┃\n' +
-                '                 ┃                     ┃                                                                                 ┃                     ┃\n' +
-                round_16.iloc[
-                    0, 1] + '━━━━┛                     ┃                                                                                 ┃                     ┗━━━━' +
-                round_16.iloc[4, 1] + '\n' +
-                '                                       ┃━━━━' + semi_finals.iloc[
-                    0, 0] + '━━━━┓                                     ┏━━━━' + semi_finals.iloc[1, 0] + '━━━━┃\n' +
-                round_16.iloc[
-                    1, 0] + '━━━━┓                     ┃                     ┃                                     ┃                     ┃                     ┏━━━━' +
-                round_16.iloc[5, 0] + '\n' +
-                '                 ┃                     ┃                     ┃                                     ┃                     ┃                     ┃\n' +
-                '                 ┃━━━━' + quarter_finals.iloc[
-                    0, 1] + '━━━━┛                     ┃                                     ┃                     ┗━━━━' +
-                quarter_finals.iloc[2, 1] + '━━━━┃\n' +
-                '                 ┃                                           ┃                                     ┃                                           ┃\n' +
-                round_16.iloc[
-                    1, 1] + '━━━━┛                                           ┃                                     ┃                                           ┗━━━━' +
-                round_16.iloc[5, 1] + '\n' +
-                '                                                             ┃━━━━' + final.iloc[0, 0] + 'vs.' +
-                final.iloc[0, 1] + '━━━━┃\n' +
-                round_16.iloc[
-                    2, 0] + '━━━━┓                                           ┃                                     ┃                                           ┏━━━━' +
-                round_16.iloc[6, 0] + '\n' +
-                '                 ┃                                           ┃                                     ┃                                           ┃\n' +
-                '                 ┃━━━━' + quarter_finals.iloc[
-                    1, 0] + '━━━━┓                     ┃                                     ┃                     ┏━━━━' +
-                quarter_finals.iloc[3, 0] + '━━━━┃\n' +
-                '                 ┃                     ┃                     ┃                                     ┃                     ┃                     ┃\n' +
-                round_16.iloc[
-                    2, 1] + '━━━━┛                     ┃                     ┃                                     ┃                     ┃                     ┗━━━━' +
-                round_16.iloc[6, 1] + '\n' +
-                '                                       ┃━━━━' + semi_finals.iloc[
-                    0, 1] + '━━━━┛                                     ┗━━━━' + semi_finals.iloc[1, 1] + '━━━━┃\n' +
-                round_16.iloc[
-                    3, 0] + '━━━━┓                     ┃                                                                                 ┃                     ┏━━━━' +
-                round_16.iloc[7, 0] + '\n' +
-                '                 ┃                     ┃                                                                                 ┃                     ┃\n' +
-                '                 ┃━━━━' + quarter_finals.iloc[
-                    1, 1] + '━━━━┛                                                                                 ┗━━━━' +
-                quarter_finals.iloc[3, 1] + '━━━━┃\n' +
-                '                 ┃                                                                                                                             ┃\n' +
-                round_16.iloc[
-                    3, 1] + '━━━━┛                                                                                                                             ┗━━━━' +
-                round_16.iloc[7, 1] + '\n' +
-                "                                                                 " + center(
-            "\U0001F947" + winner.iloc[0, 1]) + '\n' +
-                "                                                                 " + center(
-            "\U0001F948" + second.iloc[0, 1]) + '\n' +
-                "                                                                 " + center(
-            "\U0001F949" + third.iloc[0, 1])
-        )
-        return formatted_text
-
-    # Generate the formatted text
-    formatted_text = generate_text(round_16, quarter_finals, semi_finals, final)
-
-    # Define the round_16, quarter_finals, semi_finals, final DataFrames
-    # Replace the DataFrame creation with your actual data
-
-    # Display the formatted text
-    st.text(formatted_text)
-    # st.markdown(formatted_text)
-
-    print(round_16.iloc[
-              0, 0] + '━━━━┓                                                                                                                             ┏━━━━' +
-          round_16.iloc[4, 0])
-    print(
-        '                 ┃                                                                                                                             ┃')
-    print('                 ┃━━━━' + quarter_finals.iloc[
-        0, 0] + '━━━━┓                                                                                 ┏━━━━' +
-          quarter_finals.iloc[2, 0] + '━━━━┃')
-    print(
-        '                 ┃                     ┃                                                                                 ┃                     ┃')
-    print(round_16.iloc[
-              0, 1] + '━━━━┛                     ┃                                                                                 ┃                     ┗━━━━' +
-          round_16.iloc[4, 1])
-    print('                                       ┃━━━━' + semi_finals.iloc[
-        0, 0] + '━━━━┓                                     ┏━━━━' + semi_finals.iloc[1, 0] + '━━━━┃')
-    print(round_16.iloc[
-              1, 0] + '━━━━┓                     ┃                     ┃                                     ┃                     ┃                     ┏━━━━' +
-          round_16.iloc[5, 0])
-    print(
-        '                 ┃                     ┃                     ┃                                     ┃                     ┃                     ┃')
-    print('                 ┃━━━━' + quarter_finals.iloc[
-        0, 1] + '━━━━┛                     ┃                                     ┃                     ┗━━━━' +
-          quarter_finals.iloc[2, 1] + '━━━━┃')
-    print(
-        '                 ┃                                           ┃                                     ┃                                           ┃')
-    print(round_16.iloc[
-              1, 1] + '━━━━┛                                           ┃                                     ┃                                           ┗━━━━' +
-          round_16.iloc[5, 1])
-    print('                                                             ┃━━━━' + final.iloc[0, 0] + 'vs.' + final.iloc[
-        0, 1] + '━━━━┃')
-    print(round_16.iloc[
-              2, 0] + '━━━━┓                                           ┃                                     ┃                                           ┏━━━━' +
-          round_16.iloc[6, 0])
-    print(
-        '                 ┃                                           ┃                                     ┃                                           ┃')
-    print('                 ┃━━━━' + quarter_finals.iloc[
-        1, 0] + '━━━━┓                     ┃                                     ┃                     ┏━━━━' +
-          quarter_finals.iloc[3, 0] + '━━━━┃')
-    print(
-        '                 ┃                     ┃                     ┃                                     ┃                     ┃                     ┃')
-    print(round_16.iloc[
-              2, 1] + '━━━━┛                     ┃                     ┃                                     ┃                     ┃                     ┗━━━━' +
-          round_16.iloc[6, 1])
-    print('                                       ┃━━━━' + semi_finals.iloc[
-        0, 1] + '━━━━┛                                     ┗━━━━' + semi_finals.iloc[1, 1] + '━━━━┃')
-    print(round_16.iloc[
-              3, 0] + '━━━━┓                     ┃                                                                                 ┃                     ┏━━━━' +
-          round_16.iloc[7, 0])
-    print(
-        '                 ┃                     ┃                                                                                 ┃                     ┃')
-    print('                 ┃━━━━' + quarter_finals.iloc[
-        1, 1] + '━━━━┛                                                                                 ┗━━━━' +
-          quarter_finals.iloc[3, 1] + '━━━━┃')
-    print(
-        '                 ┃                                                                                                                             ┃')
-    print(round_16.iloc[
-              3, 1] + '━━━━┛                                                                                                                             ┗━━━━' +
-          round_16.iloc[7, 1])
-    print(
-        "                                                                 " + center2("\U0001F947" + winner.iloc[0, 1]))
-    print(
-        "                                                                 " + center2("\U0001F948" + second.iloc[0, 1]))
-    print(
-        "                                                                 " + center2("\U0001F949" + third.iloc[0, 1]))
-
+                    if row['rank1'] > row['rank2']:
+                        pred_set.append({'Team1': row['country1'], 'Team2': row['country2']})
+                    else:
+                        pred_set.append({'Team1': row['country2'], 'Team2': row['country1']})
+
+            pred_set = pd.DataFrame(pred_set)
+            pred_set.insert(2, 'Team1_FIFA_RANK', pred_set['Team1'].map(last_team_scores.set_index('team')['rank']))
+            pred_set.insert(3, 'Team2_FIFA_RANK', pred_set['Team2'].map(last_team_scores.set_index('team')['rank']))
+            pred_set.insert(4, 'Team1_Goalkeeper_Score',
+                            pred_set['Team1'].map(last_team_scores.set_index('team')['goalkeeper_score']))
+            pred_set.insert(5, 'Team2_Goalkeeper_Score',
+                            pred_set['Team2'].map(last_team_scores.set_index('team')['goalkeeper_score']))
+            pred_set.insert(6, 'Team1_Defense', pred_set['Team1'].map(last_team_scores.set_index('team')['defense_score']))
+            pred_set.insert(7, 'Team1_Offense', pred_set['Team1'].map(last_team_scores.set_index('team')['offense_score']))
+            pred_set.insert(8, 'Team1_Midfield',
+                            pred_set['Team1'].map(last_team_scores.set_index('team')['midfield_score']))
+            pred_set.insert(9, 'Team2_Defense', pred_set['Team2'].map(last_team_scores.set_index('team')['defense_score']))
+            pred_set.insert(10, 'Team2_Offense', pred_set['Team2'].map(last_team_scores.set_index('team')['offense_score']))
+            pred_set.insert(11, 'Team2_Midfield',
+                            pred_set['Team2'].map(last_team_scores.set_index('team')['midfield_score']))
+            return pred_set
+
+        def print_results(dataset, y_pred, matches, proba):
+            results = []
+            for i in range(dataset.shape[0]):
+                print()
+                if y_pred[i] == 2:
+                    print(matches.iloc[i, 0] + " vs. " + matches.iloc[i, 1] + " => Draw")
+                    results.append({'result': 'Draw'})
+                elif y_pred[i] == 1:
+                    print(matches.iloc[i, 0] + " vs. " + matches.iloc[i, 1] + " => Winner: " + dataset.iloc[i, 0])
+                    results.append({'result': dataset.iloc[i, 0]})
+                else:
+                    print(matches.iloc[i, 0] + " vs. " + matches.iloc[i, 1] + " => Winner: " + dataset.iloc[i, 1])
+                    results.append({'result': dataset.iloc[i, 1]})
+                try:
+                    print('Probability of ' + dataset.iloc[i, 0] + ' winning: ', '%.3f' % (proba[i][1]))
+                    print('Probability of Draw: ', '%.3f' % (proba[i][2]))
+                    print('Probability of ' + dataset.iloc[i, 1] + ' winning: ', '%.3f' % (proba[i][0]))
+                except:
+                    print('Probability of ' + dataset.iloc[i, 1] + ' winning: ', '%.3f' % (proba[i][0]))
+                print("")
+            results = pd.DataFrame(results)
+            matches = pd.concat([matches.group, results], axis=1)
+            return matches
+
+        def winner_to_match(round, prev_match):
+            round.insert(0, 'c1', round['country1'].map(prev_match.set_index('group')['result']))
+            round.insert(1, 'c2', round['country2'].map(prev_match.set_index('group')['result']))
+            round = round.drop(['country1', 'country2'], axis=1)
+            round = round.rename(columns={'c1': 'country1', 'c2': 'country2'}).reset_index(drop=True)
+            return round
+
+        def prediction_knockout(round):
+            dataset_round = matches(round)
+            prediction_round = xgb_ks_model.predict(dataset_round)
+            proba_round = xgb_ks_model.predict_proba(dataset_round)
+
+            # prediction_round = ada_ks_model.predict(dataset_round)
+            # proba_round = ada_ks_model.predict_proba(dataset_round)
+
+            # prediction_round = rf_ks_model.predict(dataset_round)
+            # proba_round = rf_ks_model.predict_proba(dataset_round)
+
+            results_round = print_results(dataset_round, prediction_round, round, proba_round)
+            return results_round
+
+        def center_str(round):
+            spaces = ['', ' ', '  ', '   ', '    ', '     ', ]
+            for j in range(2):
+                for i in range(round.shape[0]):
+                    if (13 - len(round.iloc[i, j])) % 2 == 0:
+                        round.iloc[i, j] = spaces[int((13 - len(round.iloc[i, j])) / 2)] + round.iloc[i, j] + spaces[
+                            int((13 - len(round.iloc[i, j])) / 2)]
+                    else:
+                        round.iloc[i, j] = spaces[int(((13 - len(round.iloc[i, j])) / 2) - 0.5)] + round.iloc[i, j] + \
+                                           spaces[int(((13 - len(round.iloc[i, j])) / 2) + 0.5)]
+            return round
+
+        def center2(a):
+            spaces = ['', ' ', '  ', '   ', '    ', '     ', '      ', '       ', '        ', '         ', '          ',
+                      '           ', '            ', '             ', '              ', '               ',
+                      '                ', '                 ', '                  ', '                   ',
+                      '                    ']
+            if (29 - len(a)) % 2 == 0:
+                a = spaces[int((29 - len(a)) / 2)] + a + spaces[int((29 - len(a)) / 2)]
+            else:
+                a = spaces[int(((29 - len(a)) / 2) - 0.5)] + a + spaces[int(((29 - len(a)) / 2) + 0.5)]
+            return a
+
+        dataset_groups = matches(group_matches)
+        dataset_groups.tail()
+        print(dataset_groups)
+
+        prediction_groups = xgb_gs_model.predict(dataset_groups)
+        proba = xgb_gs_model.predict_proba(dataset_groups)
+
+        # prediction_groups = ada_gs_model.predict(dataset_groups)
+        # proba = ada_gs_model.predict_proba(dataset_groups)
+
+        # prediction_groups = rf_gs_model.predict(dataset_groups)
+        # proba = rf_gs_model.predict_proba(dataset_groups)
+
+        results = print_results(dataset_groups, prediction_groups, group_matches, proba)
+
+        team_group['points'] = 0
+        team_group
+        for i in range(results.shape[0]):
+            for j in range(team_group.shape[0]):
+                if results.iloc[i, 1] == team_group.iloc[j, 0]:
+                    team_group.iloc[j, 2] += 3
+
+        print(team_group.groupby(['group', 'team']).mean().astype(int))
+
+        round_of_16 = team_group[team_group['points'] > 5].reset_index(drop=True)
+        round_of_16['group'] = (4 - 1 / 3 * round_of_16.points).astype(int).astype(str) + round_of_16.group
+        round_of_16 = round_of_16.rename(columns={"team": "result"})
+
+        round_16 = winner_to_match(round_16, round_of_16)
+        results_round_16 = prediction_knockout(round_16)
+
+        quarter_finals = winner_to_match(quarter_finals, results_round_16)
+        results_quarter_finals = prediction_knockout(quarter_finals)
+
+        semi_finals = winner_to_match(semi_finals, results_quarter_finals)
+        results_finals = prediction_knockout(semi_finals)
+
+        final = winner_to_match(final, results_finals)
+        winner = prediction_knockout(final)
+
+        second = results_finals[~results_finals.result.isin(winner.result)]
+        results_finals_3 = results_quarter_finals[~results_quarter_finals.result.isin(results_finals.result)]
+        results_finals_3.iloc[0, 0] = 'z1'
+        results_finals_3.iloc[1, 0] = 'z2'
+        second_final = winner_to_match(second_final, results_finals_3)
+        third = prediction_knockout(second_final)
+
+        round_16 = center_str(round_16)
+        quarter_finals = center_str(quarter_finals)
+        semi_finals = center_str(semi_finals)
+        final = center_str(final)
+        group_matches = center_str(group_matches)
+
+        # Function to center align text
+        def center(text):
+            return f"<div style='text-align: center;'>{text}</div>"
+
+        # Function to generate the formatted text
+        def generate_text(round_16, quarter_finals, semi_finals, final):
+            formatted_text = (
+                    round_16.iloc[
+                        0, 0] + '━━━━┓                                                                                                                             ┏━━━━' +
+                    round_16.iloc[4, 0] + '\n' +
+                    '                 ┃                                                                                                                             ┃\n' +
+                    '                 ┃━━━━' + quarter_finals.iloc[
+                        0, 0] + '━━━━┓                                                                                 ┏━━━━' +
+                    quarter_finals.iloc[2, 0] + '━━━━┃\n' +
+                    '                 ┃                     ┃                                                                                 ┃                     ┃\n' +
+                    round_16.iloc[
+                        0, 1] + '━━━━┛                     ┃                                                                                 ┃                     ┗━━━━' +
+                    round_16.iloc[4, 1] + '\n' +
+                    '                                       ┃━━━━' + semi_finals.iloc[
+                        0, 0] + '━━━━┓                                     ┏━━━━' + semi_finals.iloc[1, 0] + '━━━━┃\n' +
+                    round_16.iloc[
+                        1, 0] + '━━━━┓                     ┃                     ┃                                     ┃                     ┃                     ┏━━━━' +
+                    round_16.iloc[5, 0] + '\n' +
+                    '                 ┃                     ┃                     ┃                                     ┃                     ┃                     ┃\n' +
+                    '                 ┃━━━━' + quarter_finals.iloc[
+                        0, 1] + '━━━━┛                     ┃                                     ┃                     ┗━━━━' +
+                    quarter_finals.iloc[2, 1] + '━━━━┃\n' +
+                    '                 ┃                                           ┃                                     ┃                                           ┃\n' +
+                    round_16.iloc[
+                        1, 1] + '━━━━┛                                           ┃                                     ┃                                           ┗━━━━' +
+                    round_16.iloc[5, 1] + '\n' +
+                    '                                                             ┃━━━━' + final.iloc[0, 0] + 'vs.' +
+                    final.iloc[0, 1] + '━━━━┃\n' +
+                    round_16.iloc[
+                        2, 0] + '━━━━┓                                           ┃                                     ┃                                           ┏━━━━' +
+                    round_16.iloc[6, 0] + '\n' +
+                    '                 ┃                                           ┃                                     ┃                                           ┃\n' +
+                    '                 ┃━━━━' + quarter_finals.iloc[
+                        1, 0] + '━━━━┓                     ┃                                     ┃                     ┏━━━━' +
+                    quarter_finals.iloc[3, 0] + '━━━━┃\n' +
+                    '                 ┃                     ┃                     ┃                                     ┃                     ┃                     ┃\n' +
+                    round_16.iloc[
+                        2, 1] + '━━━━┛                     ┃                     ┃                                     ┃                     ┃                     ┗━━━━' +
+                    round_16.iloc[6, 1] + '\n' +
+                    '                                       ┃━━━━' + semi_finals.iloc[
+                        0, 1] + '━━━━┛                                     ┗━━━━' + semi_finals.iloc[1, 1] + '━━━━┃\n' +
+                    round_16.iloc[
+                        3, 0] + '━━━━┓                     ┃                                                                                 ┃                     ┏━━━━' +
+                    round_16.iloc[7, 0] + '\n' +
+                    '                 ┃                     ┃                                                                                 ┃                     ┃\n' +
+                    '                 ┃━━━━' + quarter_finals.iloc[
+                        1, 1] + '━━━━┛                                                                                 ┗━━━━' +
+                    quarter_finals.iloc[3, 1] + '━━━━┃\n' +
+                    '                 ┃                                                                                                                             ┃\n' +
+                    round_16.iloc[
+                        3, 1] + '━━━━┛                                                                                                                             ┗━━━━' +
+                    round_16.iloc[7, 1] + '\n' +
+                    "                                                                 " + center(
+                "\U0001F947" + winner.iloc[0, 1]) + '\n' +
+                    "                                                                 " + center(
+                "\U0001F948" + second.iloc[0, 1]) + '\n' +
+                    "                                                                 " + center(
+                "\U0001F949" + third.iloc[0, 1])
+            )
+            return formatted_text
+
+        # Generate the formatted text
+        formatted_text = generate_text(round_16, quarter_finals, semi_finals, final)
+
+        # Define the round_16, quarter_finals, semi_finals, final DataFrames
+        # Replace the DataFrame creation with your actual data
+
+        # Display the formatted text
+        st.text(formatted_text)
+        # st.markdown(formatted_text)
+
+        print(round_16.iloc[
+                  0, 0] + '━━━━┓                                                                                                                             ┏━━━━' +
+              round_16.iloc[4, 0])
+        print(
+            '                 ┃                                                                                                                             ┃')
+        print('                 ┃━━━━' + quarter_finals.iloc[
+            0, 0] + '━━━━┓                                                                                 ┏━━━━' +
+              quarter_finals.iloc[2, 0] + '━━━━┃')
+        print(
+            '                 ┃                     ┃                                                                                 ┃                     ┃')
+        print(round_16.iloc[
+                  0, 1] + '━━━━┛                     ┃                                                                                 ┃                     ┗━━━━' +
+              round_16.iloc[4, 1])
+        print('                                       ┃━━━━' + semi_finals.iloc[
+            0, 0] + '━━━━┓                                     ┏━━━━' + semi_finals.iloc[1, 0] + '━━━━┃')
+        print(round_16.iloc[
+                  1, 0] + '━━━━┓                     ┃                     ┃                                     ┃                     ┃                     ┏━━━━' +
+              round_16.iloc[5, 0])
+        print(
+            '                 ┃                     ┃                     ┃                                     ┃                     ┃                     ┃')
+        print('                 ┃━━━━' + quarter_finals.iloc[
+            0, 1] + '━━━━┛                     ┃                                     ┃                     ┗━━━━' +
+              quarter_finals.iloc[2, 1] + '━━━━┃')
+        print(
+            '                 ┃                                           ┃                                     ┃                                           ┃')
+        print(round_16.iloc[
+                  1, 1] + '━━━━┛                                           ┃                                     ┃                                           ┗━━━━' +
+              round_16.iloc[5, 1])
+        print('                                                             ┃━━━━' + final.iloc[0, 0] + 'vs.' + final.iloc[
+            0, 1] + '━━━━┃')
+        print(round_16.iloc[
+                  2, 0] + '━━━━┓                                           ┃                                     ┃                                           ┏━━━━' +
+              round_16.iloc[6, 0])
+        print(
+            '                 ┃                                           ┃                                     ┃                                           ┃')
+        print('                 ┃━━━━' + quarter_finals.iloc[
+            1, 0] + '━━━━┓                     ┃                                     ┃                     ┏━━━━' +
+              quarter_finals.iloc[3, 0] + '━━━━┃')
+        print(
+            '                 ┃                     ┃                     ┃                                     ┃                     ┃                     ┃')
+        print(round_16.iloc[
+                  2, 1] + '━━━━┛                     ┃                     ┃                                     ┃                     ┃                     ┗━━━━' +
+              round_16.iloc[6, 1])
+        print('                                       ┃━━━━' + semi_finals.iloc[
+            0, 1] + '━━━━┛                                     ┗━━━━' + semi_finals.iloc[1, 1] + '━━━━┃')
+        print(round_16.iloc[
+                  3, 0] + '━━━━┓                     ┃                                                                                 ┃                     ┏━━━━' +
+              round_16.iloc[7, 0])
+        print(
+            '                 ┃                     ┃                                                                                 ┃                     ┃')
+        print('                 ┃━━━━' + quarter_finals.iloc[
+            1, 1] + '━━━━┛                                                                                 ┗━━━━' +
+              quarter_finals.iloc[3, 1] + '━━━━┃')
+        print(
+            '                 ┃                                                                                                                             ┃')
+        print(round_16.iloc[
+                  3, 1] + '━━━━┛                                                                                                                             ┗━━━━' +
+              round_16.iloc[7, 1])
+        print(
+            "                                                                 " + center2("\U0001F947" + winner.iloc[0, 1]))
+        print(
+            "                                                                 " + center2("\U0001F948" + second.iloc[0, 1]))
+        print(
+            "                                                                 " + center2("\U0001F949" + third.iloc[0, 1]))
 
 
 if __name__ == "__main__":