from Packages import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
from Class_Mod.DATA_HANDLING import *

########################################################################################
# Model creation module
container2 = st.container(border=True)

M1, M2, M3 = st.columns([2,2,2])
M4, M5 = st.columns([6,2])
container3 = st.container(border=True)
M7, M8 = st.columns([2,2])

available_regression_algo = ["","SciKitLearn PLSR", "Jchemo Local Weighted PLSR", "Intervalle Selection PLSR"]
with container2:
    st.header("Calibration Model Development", divider='blue')
    st.write("Create a predictive model, then use it for predicting your target variable(chemical values) from NIRS spectra")
    # CSV files loader
    xcal_csv = M3.file_uploader("Select NIRS Data", type="csv", help=" :mushroom: select a csv matrix with samples as rows and lambdas as columns")
    ycal_csv = M3.file_uploader("Select corresponding Chemical Data", type="csv", help=" :mushroom: select a csv matrix with samples as rows and chemical values as a column")

    if xcal_csv is not None and ycal_csv is not None:
        # Select list for CSV delimiter
        sep = M3.selectbox("Select csv separator - _detected_: " + str(find_delimiter('data/'+xcal_csv.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+xcal_csv.name))), key=0)
        # Select list for CSV header True / False
        hdr = M3.selectbox("indexes column in csv? - _detected_: " + str(find_col_index('data/'+xcal_csv.name)), options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+xcal_csv.name))), key=1)
        if hdr == 'yes':
            col = 0
        else:
            col = False
        rd_seed = M1.slider("Choose seed", min_value=1, max_value=1212, value=42, format="%i")
        x, y = utils.load_csv(xcal_csv, ycal_csv, autoremove_na=True, sep=sep, x_hdr=0, y_hdr=0, x_index_col=col, y_index_col=col)
        # Split data into training and test sets using the kennard_stone method and correlation metric, 25% of data is used for testing
        train_index, test_index = train_test_split_idx(x, y=y, method="kennard_stone", metric="correlation", test_size=0.25, random_state=rd_seed)
        # Assign data to training and test sets
        X_train, y_train, X_test, y_test = pd.DataFrame(x[train_index]), pd.DataFrame(y[train_index]), pd.DataFrame(x[test_index]), pd.DataFrame(y[test_index])
        #############################

        regression_algo = M1.selectbox("Choose the algorithm for regression", options=available_regression_algo, key = 12)

        if regression_algo == 'SciKitLearn PLSR':
            # Train model with model function from application_functions.py
            Reg = PinardPlsr(x_train=X_train, x_test=X_test,y_train=y_train, y_test=y_test)
            reg_model = Reg.model_

            #M2.dataframe(Pin.pred_data_)

        elif regression_algo == 'Jchemo Local Weighted PLSR':
            reg_model = model_LWPLSR(xcal_csv, ycal_csv, sep, hdr)

        elif regression_algo == "Intervalle Selection PLSR":
            s = M2.number_input(label='Enter the maximum number of intervalls', min_value=1, max_value=6, value="min")
            reg_model = TpeIpls(x_train= X_train, y_train= y_train, x_test=X_test, y_test= y_test,Kfold= 3,scale= True, n_intervall = 3)
            reg_model.tune(n_iter=10)

        if regression_algo in ["SciKitLearn PLSR", "Jchemo Local Weighted PLSR", "Intervalle Selection PLSR"]:
            with container3:
                st.header("Model Diagnosis", divider='blue')
                yc = Reg.pred_data_[0]
                ycv = Reg.pred_data_[1]
                yt = Reg.pred_data_[2]
                M7.write('Predicted vs Measured values')
                M7.pyplot(reg_plot([y_train, y_train, y_test],[yc, ycv, yt]))
                M8.write('Residuals plot')
                M8.pyplot(resid_plot([y_train, y_train, y_test],[yc, ycv, yt]))


        # Export the model with pickle or joblib
        if regression_algo != '':
            M1.write("-- Performance metrics --")
            M1.dataframe(Reg.metrics_)
            M1.write("-- Save the model --")
            #model_export = M1.selectbox("Choose way to export", options=["pickle", "joblib"], key=20)
            model_name = M1.text_input('Give it a name')
            if M1.button('Export Model'):
                #export_package = __import__(model_export)
                with open('data/models/model_' + model_name + '_on_' + xcal_csv.name + '_and_' + ycal_csv.name + '_data_' + '.pkl','wb') as f:
                    joblib.dump(reg_model,f)
                st.write('Model Exported')

                # create a report with information on the model
                ## see https://stackoverflow.com/a/59578663
        #M4.pyplot(reg_plot(meas==(ycal_csv,ycal_csv,ycal_csv], pred=[ycal_csv,ycal_csv,ycal_csv]))


# graphical delimiter
st.write("---")