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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('src/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)")
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