import streamlit as st
import pickle
import json
import pandas as pd
# from sklearn.pipeline import make_pipeline
# from sklearn.preprocessing import StandardScaler, OneHotEncoder
# from sklearn.svm import SVC
# from sklearn.linear_model import LogisticRegression
# from sklearn.tree import DecisionTreeClassifier
# from sklearn.ensemble import RandomForestClassifier


with open('svc_model.pkl', 'rb') as f:
    pipesvc = pickle.load(f)

with open('logreg_model.pkl', 'rb') as f:
    pipeLR = pickle.load(f)

with open('dt_model.pkl', 'rb') as f:
    pipeDT = pickle.load(f)

with open('rf_model.pkl', 'rb') as f:
    pipeRF = pickle.load(f)

with open('preprocessor.pkl', 'rb') as f:
    preprocessor = pickle.load(f)

with open('le.pkl', 'rb') as f:
    Le = pickle.load(f)

with open('num_cols.json', 'r') as f:
    numerical_cols = json.load(f)

with open('cat_cols.json', 'r') as f:
    categorical_cols = json.load(f)
def run():
    with st.form(key='form_prediksi'):
        name = st.text_input('Nama', value='')
        sex = st.radio('Kelamin', ('Perempuan', 'Laki-Laki'))
        if sex=='Laki-Laki':
            sexnum='M'
        else: sexnum='F'
        age= st.number_input('Umur', min_value=16, max_value=80, value=50, step=1)
        smoking = st.radio('Apakah merokok?', ('Ya', 'Tidak'))
        if smoking=='Ya':
            smokingnum=2
        else: smokingnum=1
        Yelfing= st.radio('Apakah memiliki Yellow Finger?', ('Ya', 'Tidak'))
        if Yelfing=='Ya':
            yelfingnum=2
        else: yelfingnum=1
        anxeity = st.radio('Apakah memiliki Anxeity?', ('Ya', 'Tidak'))
        if anxeity == 'Ya':
            anxeitynum=2
        else: anxeitynum=1
        peer_pressure = st.radio('Apakah terdapat peer pressure?', ('Ya', 'Tidak'))
        if peer_pressure=='Ya':
            peer_pressurenum=2
        else: peer_pressurenum=1
        Crondis= st.radio('Apakah memiliki penyakit Kronis?', ('Ya', 'Tidak'))
        if Crondis=='Ya':
            crondisnum=2
        else: crondisnum=1
        Fatigue= st.radio('Apakah mudah capai?', ('Ya', 'Tidak'))
        if Fatigue=='Ya':
            fatiguenum=2
        else: fatiguenum=1
        alergi= st.radio('Apakah memiliki alergi?', ('Ya', 'Tidak'))
        if alergi=='Ya':
            alerginum=2
        else: alerginum=1
        mengi= st.radio('Apakah mengidap mengi?', ('Ya', 'Tidak'))
        if mengi=='Ya':
            menginum=2
        else: menginum=1
        Alkohol= st.radio('Apakah mengkonsumsi alkohol?', ('Ya', 'Tidak'))
        if Alkohol=='Ya':
            alkoholnum=2
        else: alkoholnum=1
        batuk= st.radio('Apakah ada batuk?', ('Ya', 'Tidak'))
        if batuk=='Ya':
            batuknum=2
        else: batuknum=1
        sesak= st.radio('Apakah terdapat sesak?', ('Ya', 'Tidak'))
        if sesak=='Ya':
            sesaknum=2
        else: sesaknum=1
        sutel= st.radio('Apakah susah untuk menalan?', ('Ya', 'Tidak'))
        if sutel=='Ya':
            sutelnum=2
        else: sutelnum=1
        sakda= st.radio('Apakah terdapat sakit di bagian dada?', ('Ya', 'Tidak'))
        if sakda=='Ya':
            sakdanum=2
        else: sakdanum=1

        submitted = st.form_submit_button('Predict')

    data_inf = {'GENDER':sexnum,
                'AGE': age, 
                'SMOKING':smokingnum,
                'YELLOW_FINGERS':yelfingnum, 
                'ANXIETY':anxeitynum,
                'PEER_PRESSURE':peer_pressurenum,
                'CHRONIC DISEASE':crondisnum,
                'FATIGUE ':fatiguenum, 
                'ALLERGY ':alerginum,
                'WHEEZING':menginum,
                'ALCOHOL CONSUMING':alkoholnum,
                'COUGHING':batuknum,
                'SHORTNESS OF BREATH':sesaknum,
                'SWALLOWING DIFFICULTY':sutelnum,
                'CHEST PAIN':sakdanum
    }



    if submitted:
        data_inf = pd.DataFrame([data_inf])
        # y_pred_inf_rf = pipeRF.predict(data_inf)
        # y_pred_inf_DT = pipeDT.predict(data_inf)
        y_pred_inf_LR = pipeLR.predict(data_inf)
        # y_pred_inf_SVC = pipesvc.predict(data_inf)
        # st.write('# hasil inf dari Randomforest', Le.inverse_transform(y_pred_inf_rf))
        # st.write('# hasil inf dari Decision Tree', Le.inverse_transform(y_pred_inf_DT))
        # st.write('# hasil inf dari SVC', Le.inverse_transform(y_pred_inf_SVC))
        st.write('# hasil inf dari Logistic Regression', Le.inverse_transform(y_pred_inf_LR))

if __name__== '__main__':
    run()