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import joblib
import streamlit as st
from sklearn.ensemble import RandomForestRegressor
import base64
# Using "with" notation
st.markdown(
f'''
<style>
.sidebar .sidebar-content {{
width: 375px;
}}
</style>
''',
unsafe_allow_html=True
)
with st.sidebar:
st.link_button("more_information", "https://www.kaggle.com/code/pankajsinghardh/p-tax-fare")
st.success("")
model = joblib.load("model.joblib")
titleimg = "new.jpg"
#impliment background formating
def set_bg_hack(main_bg):
# set bg name
main_bg_ext = "jpg"
st.markdown(
f"""
<style>
.stApp {{
background: url(data:image/{main_bg_ext};base64,{base64.b64encode(open(main_bg, "rb").read()).decode()});
background-repeat: no-repeat;
background-position: right 50% bottom 95% ;
background-size: cover;
background-attachment: scroll;
}}
</style>
""",
unsafe_allow_html=True,
)
set_bg_hack(titleimg)
st.markdown("<h1><font color='yellow'><center>'Taxi_Fare'</center></font></h1>",unsafe_allow_html=True)
le = r'''
$\textsf{
\LARGE Hour\
}$
'''
hour = st.selectbox(f":red[{le}]",range(1,25))
le = r'''
$\textsf{
\LARGE Distance\
}$
'''
distance = st.number_input(f":red[{le}]",step=1)
le = r'''
$\textsf{
\LARGE Weekday\
}$
'''
weekday = st.selectbox(f":red[{le}]",["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"])
day = {"Sunday":0,"Monday":1,"Tuesday":3,"Wednesday":4,"Thursday":5,"Friday":6,"Saturday":7}
btn = st.button("predict")
if btn:
price = model.predict([[hour,distance,day[weekday]]])
st.snow()
st.write( ":red[Fare_in_USD]",price)
def autoplay_audio(file_path: str):
with open(file_path, "rb") as f:
data = f.read()
b64 = base64.b64encode(data).decode()
md = f"""
<audio controls autoplay="true">
<source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
</audio>
"""
st.markdown(
md,
unsafe_allow_html=True,
)
autoplay_audio("synthesize.mp3")
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