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| import streamlit as st | |
| import pandas as pd | |
| import pickle | |
| from PIL import Image | |
| def run(): | |
| # Load All Files | |
| with open('pipeline_model.pkl', 'rb') as file: | |
| full_process = pickle.load(file) | |
| distance = st.number_input(label='input your distance here',min_value=0.0,max_value=7.5) | |
| surge_multiplier = st.selectbox(label='choose your surge_multiplier here',options=[1. , 1.25, 2.5 , 2. , 1.75, 1.5 , 3. ]) | |
| name = st.selectbox(label='choose your cab name here',options=['Shared', 'Lux', 'Lyft', 'Lux Black XL', 'Lyft XL', 'Lux Black', | |
| 'UberXL', 'Black', 'UberX', 'WAV', 'Black SUV', 'UberPool']) | |
| product_id = st.selectbox(label='choose your product id here',options=['lyft_line', 'lyft_premier', 'lyft', 'lyft_luxsuv', 'lyft_plus', | |
| 'lyft_lux', 'uber_line', 'uber_premier', 'uber', 'uber_luxsuv', | |
| 'uber_plus', 'uber_lux']) | |
| st.write('In the following is the result of the data you have input : ') | |
| data_inf = pd.DataFrame({ | |
| 'distance' : distance, | |
| 'surge_multiplier' : surge_multiplier, | |
| 'name' : name , | |
| 'product_id' : product_id, | |
| }, index=[0]) | |
| st.table(data_inf) | |
| if st.button(label='predict'): | |
| # Melakukan prediksi data dummy | |
| y_pred_inf = full_process.predict(data_inf) | |
| st.metric(label="Here is a prediction of your travel costs : ", value = y_pred_inf[0]) | |
| # If your data is a classification, you can follow the example below | |
| # if y_pred_inf[0] == 0: | |
| # st.write('Pasien tidak terkena jantung') | |
| # st.markdown("[Cara Cegah Serangan Jantung](https://www.siloamhospitals.com/informasi-siloam/artikel/cara-cegah-serangan-jantung-di-usia-muda)") | |
| # else: | |
| # st.write('Pasien kemungkinan terkena jantung') | |
| # st.markdown("[Cara Hidup Sehat Sehabis Terkena Serangan Jantung](https://lifestyle.kompas.com/read/2021/11/09/101744620/7-pola-hidup-sehat-setelah-mengalami-serangan-jantung?page=all)") | |