import itertools
import os
from joblib import dump, load
import pandas as pd


model_dict = {
    "adb_char" : "model/char_ADB.joblib",
    "dtr_char": "model/char_DTR.joblib",
    "en_char":"model/char_ElasticNet.joblib",
    "gp_char":"model/char_GaussianProcessRegressor.joblib",
    "kn_char":"model/char_KNeighborsRegressor.joblib",
    "lasso_char":"model/char_Lasso.joblib",
    "lr_char":"model/char_LinearRegression.joblib",
    "rfr_char":"model/char_RFR.joblib",
    "ridge_char":"model/char_Ridge.joblib",
    "svr_char":"model/char_SVR.joblib",
    "xgb_char":"model/char_XGB.joblib",

    "adb_gas" : "model/gas_ADB.joblib",
    "dtr_gas": "model/gas_DTR.joblib",
    "en_gas":"model/gas_ElasticNet.joblib",
    "gp_gas":"model/gas_GaussianProcessRegressor.joblib",
    "kn_gas":"model/gas_KNeighborsRegressor.joblib",
    "lasso_gas":"model/gas_Lasso.joblib",
    "lr_gas":"model/gas_LinearRegression.joblib",
    "rfr_gas":"model/gas_RFR.joblib",
    "ridge_gas":"model/gas_Ridge.joblib",
    "svr_gas":"model/gas_SVR.joblib",
    "xgb_gas":"model/gas_XGB.joblib",

    "adb_water" : "model/water_ADB.joblib",
    "dtr_water": "model/water_DTR.joblib",
    "en_water":"model/water_ElasticNet.joblib",
    "gp_water":"model/water_GaussianProcessRegressor.joblib",
    "kn_water":"model/water_KNeighborsRegressor.joblib",
    "lasso_water":"model/water_Lasso.joblib",
    "lr_water":"model/water_LinearRegression.joblib",
    "rfr_water":"model/water_RFR.joblib",
    "ridge_water":"model/water_Ridge.joblib",
    "svr_water":"model/water_SVR.joblib",
    "xgb_water":"model/water_XGB.joblib",

    "adb_tar" : "model/tar_ADB.joblib",
    "dtr_tar": "model/tar_DTR.joblib",
    "en_tar":"model/tar_ElasticNet.joblib",
    "gp_tar":"model/tar_GaussianProcessRegressor.joblib",
    "kn_tar":"model/tar_KNeighborsRegressor.joblib",
    "lasso_tar":"model/tar_Lasso.joblib",
    "lr_tar":"model/tar_LinearRegression.joblib",
    "rfr_tar":"model/tar_RFR.joblib",
    "ridge_tar":"model/tar_Ridge.joblib",
    "svr_tar":"model/tar_SVR.joblib",
    "xgb_tar":"model/tar_XGB.joblib"
    
}


model_list_gas = ['xgb_gas','lr_gas','ridge_gas','gp_gas','en_gas','kn_gas','svr_gas','dtr_gas','rfr_gas','adb_gas']
model_list_char = ['xgb_char','lr_char','ridge_char','gp_char','en_char','kn_char','svr_char','dtr_char','rfr_char','adb_char']
model_list_water = ['xgb_water','lr_water','ridge_water','gp_water','en_water','kn_water','svr_water','dtr_water','rfr_water','adb_water']
model_list_tar = ['xgb_tar','lr_tar','ridge_tar','gp_tar','en_tar','kn_tar','svr_tar','dtr_tar','rfr_tar','adb_tar']


gas_mae = [1.93, 3.15, 3.15,1.51, 3.15, 2.15, 2.98, 2.4, 2.42, 3.0]
gas_r2 = [0.78, 0.68, 0.68,0.89,0.68, 0.75, 0.65, 0.74, 0.69, 0.62]

char_mae = [5.38, 7.0, 7.03, 2.91, 7.03, 3.94,13.5,12.55, 4.8,6.39]
char_r2 = [0.09, 0.01, 0.01, 0.8, 0.01, 0.7, 0.1,0.13, 0.16, 0.16]

water_mae = [1.74,4.33,4.35,1.47,0.83,4.18,2.51,1.38,1.42]
water_r2 = [0.87,0.47,0.46,0.89,0.45,0.98,0.23,0.67,0.89,0.95]

tar_mae = [0.93,2.09,2.09,1.31,2.09,1.28,1.51,1.24,0.96,1.49]
tar_r2 = [0.78,0.32,0.33,0.6,0.33,0.55,0.57,0.48,0.74,0.6]

test_content = {
    'A':    11.92,
    'V' :	51.16,
    'FC':	48.84,
    'C':	83.22 ,
    'H':	3.89,
    'N':	2.72,
    'S':	0.45,
    'O':20.21,
    'H/C':0.56,
    'O/C':0.18,
    'N/C':0.03,
    'Rt':40.00,
    'Hr':5.00,
    'dp':0.20,
    'T':510.00
}

result_content = {
    'Tar':11.71,
    'Gas':10.81 ,
    'Char':75.31,
    'Water':2.17
}

test_content = pd.DataFrame([test_content])  
current_script_directory = os.path.dirname(os.path.abspath(__file__))

print(current_script_directory)

def choose_model(name,data):
    model_path = os.path.join(current_script_directory,model_dict[name])
    model = load(model_path)
    pred = model.predict(data)
    return pred


def get_excel():
    current_script_directory = os.path.dirname(os.path.abspath(__file__))
    data_path = os.path.join(current_script_directory,'data/gas_data_test.csv')
    test_data = pd.read_csv(data_path)
    test_data  = test_data.drop('Gas', axis=1)
    print(test_data.shape)
    pred = choose_model('xgb_gas',test_data)
    test_data['Gas'] = pred
    print(test_data)


def pred_single_tar(test_content):
    tar_pred = []
    for name in model_list_tar:
        model_path = os.path.join(current_script_directory,model_dict[name])
        tar_model = load(model_path)
        pred = tar_model.predict(test_content)
        tar_pred.append(pred[0])
    return tar_pred



def pred_single_gas(test_content):
    gas_pred = []
    for name in model_list_gas:
        model_path = os.path.join(current_script_directory,model_dict[name])
        gas_model = load(model_path)
        pred = gas_model.predict(test_content)
        gas_pred.append(pred[0])
    return gas_pred

def pred_single_water(test_content):
    water_pred = []
    for name in model_list_water:
        model_path = os.path.join(current_script_directory,model_dict[name])
        water_model = load(model_path)
        pred = water_model.predict(test_content)
        water_pred.append(pred[0])
    return water_pred




if __name__ == "__main__":
    tar_result = pred_single_tar(test_content)
    result = [tar_mae, tar_r2,tar_result]
    print(result)

    print(pred_single_water(test_content))
    get_excel()