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| 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() |