from flask import Flask, render_template, request
import pandas as pd
from test import *  # Assurez-vous d'avoir un fichier predict.py avec votre fonction predict

app = Flask(__name__)

# Charger le DataFrame une seule fois pour économiser des ressources
df = pd.read_csv('archive/data.csv')  # Assurez-vous de spécifier le bon chemin vers votre fichier de données

limit_date = '2001-04-01'
df = df[(df['date'] > limit_date)]

displayNumberOfNaNValues(df)

imp_features = ['R_Weight_lbs', 'R_Height_cms', 'B_Height_cms', 'R_age', 'B_age', 'R_Reach_cms', 'B_Reach_cms']
imp_median = SimpleImputer(missing_values=np.nan, strategy='median')

# Iterate over each feature to impute missing values
for feature in imp_features:
    # Fit and transform the feature using median imputation
    imp_feature = imp_median.fit_transform(df[feature].values.reshape(-1,1))
    # Assign the imputed values back to the DataFrame
    df[feature] = imp_feature

# Impute missing values for 'R_Stance' using most frequent strategy
imp_stance_R = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
imp_R_stance = imp_stance_R.fit_transform(df['R_Stance'].values.reshape(-1,1))

# Impute missing values for 'B_Stance' using most frequent strategy
imp_stance_B = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
imp_B_stance = imp_stance_B.fit_transform(df['B_Stance'].values.reshape(-1,1))

# Create DataFrames for imputed stances
df['R_Stance'] = pd.DataFrame(imp_R_stance, columns=['R_Stance'])
df['B_Stance'] = pd.DataFrame(imp_B_stance, columns=['B_Stance'])


df.drop(['Referee', 'location'], axis=1, inplace=True)

# Drop column 'B_draw' and 'R_draw' and 'Draw' fight and 'Catch Weight' fight
df.drop(['B_draw', 'R_draw'], axis=1, inplace=True)
df = df[df['Winner'] != 'Draw']
df = df[df['weight_class'] != 'Catch Weight']

# Remove column when data type is not float or int
dfWithoutString = df.select_dtypes(include=['float64', 'int64'])

plt.figure(figsize=(50, 40))
corr_matrix = dfWithoutString.corr(method='pearson').abs()
sns.heatmap(corr_matrix, annot=True)

fighters = list_fighters(df,'2015-01-01')

df_train = build_df_all_but_last(df, fighters)
df_test = build_df(df, fighters,0)

preprocessor = make_column_transformer((OrdinalEncoder(), ['weight_class', 'B_Stance', 'R_Stance']), remainder='passthrough')


label_encoder = LabelEncoder()
y_train = label_encoder.fit_transform(df_train['Winner'])
y_test = label_encoder.transform(df_test['Winner'])

X_train, X_test = df_train.drop(['Winner'], axis=1), df_test.drop(['Winner'], axis=1)

# Random Forest composed of 100 decision trees. We optimized parameters using cross-validation and GridSearch tool paired together
random_forest = RandomForestClassifier(n_estimators=100, 
                                       criterion='entropy', 
                                       max_depth=10, 
                                       min_samples_split=2,
                                       min_samples_leaf=1, 
                                       random_state=0)

model = Pipeline([('encoding', preprocessor), ('random_forest', random_forest)])
model.fit(X_train, y_train)

# We use cross-validation with 5-folds to have a more precise accuracy (reduce variation)
accuracies = cross_val_score(estimator=model, X=X_train, y=y_train, cv=5)
print('Accuracy mean : ', accuracies.mean())
print('Accuracy standard deviation : ', accuracies.std())

y_pred = model.predict(X_test)
print('Testing accuracy : ', accuracy_score(y_test, y_pred), '\n')

target_names = ["Blue","Red"]
print(classification_report(y_test, y_pred, labels=[0,1], target_names=target_names))

feature_names = [col for col in X_train]
feature_importances = model['random_forest'].feature_importances_
indices = np.argsort(feature_importances)[::-1]
n = 30 # maximum feature importances displayed
idx = indices[0:n] 
std = np.std([tree.feature_importances_ for tree in model['random_forest'].estimators_], axis=0)

#for f in range(n):
#    print("%d. feature %s (%f)" % (f + 1, feature_names[idx[f]], feature_importances[idx[f]])) 

# plt.figure(figsize=(30, 8))
# plt.title("Feature importances")
# plt.bar(range(n), feature_importances[idx], color="r", yerr=std[idx], align="center")
# plt.xticks(range(n), [feature_names[id] for id in idx], rotation = 45) 
# plt.xlim([-1, n]) 
# plt.show()

# Sélectionnez un arbre de votre modèle
tree_estimator = model['random_forest'].estimators_[10]


@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predict', methods=['POST'])
def make_prediction():
    blue_fighter = request.form['blue_fighter']
    red_fighter = request.form['red_fighter']
    weightclass = request.form['weightclass']
    rounds = int(request.form['rounds'])
    title_bout = True if request.form['title_bout'] == 'True' else False
    
    prediction_proba = predict(df, model, blue_fighter, red_fighter, weightclass, rounds, title_bout)
    
    # Formatage du résultat pour l'afficher dans le navigateur
    result = ""
    if prediction_proba is not None:
        result = f"The predicted probability of {blue_fighter} winning is {round(prediction_proba[0][0] * 100, 2)}% and the predicted probability of {red_fighter} winning is {round(prediction_proba[0][1] * 100, 2)}%"
    
    return render_template('result.html', result=result)

if __name__ == '__main__':
    app.run(debug=True)