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app.py
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app.py
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import streamlit as st
import pandas as pd
import numpy as np
from model_predict import *
import streamlit as st
import pandas as pd
import plotly.express as px
from Script_fetch_from_db import cursor_df as filtered_df
import math
import datetime
def main():
map_object = px.density_mapbox(
filtered_df,
lat="latitude",
lon="longitude",
radius=15,
center=dict(lat=filtered_df['latitude'].mean(), lon=filtered_df['longitude'].mean()),
zoom=10,
mapbox_style="open-street-map",
range_color=(25, 125),
color_continuous_scale="viridis"
)
map_object.update_traces(
colorbar=dict(
title="Density",
titleside="top",
tickmode="array",
tickvals=[25, 50, 75, 100, 125],
ticktext=["Low", "25%", "50%", "75%", "High"]
)
)
map_object.update_layout(height=600, width=800)
st.plotly_chart(map_object)
adjustment_factors = {
'Mon': 1.23,
'Tue': 0.90,
'Wed': 1.06,
'Thur': 1.0,
'Fri': 1.33,
'Sat': 1.14,
'Sun': 0.92
}
regions = predicted_values
cols = st.columns(len(regions))
for i, (region, value) in enumerate(regions.items()):
with cols[i]:
day_of_week = datetime.datetime.now().strftime('%a')
adjusted_value = value * adjustment_factors[day_of_week]
rounded_value = round(abs(adjusted_value))
st.button(str(rounded_value), key=f"btn_{region}")
st.write(region)
if __name__ == "__main__":
main()
#{Mon - 1.23 Tues - 0.90 Wed - 1.06 Thur - 1 Fri - 1.33 sat - 1.14 sun - 0.92}
'''regions = predicted_values
cols = st.columns(len(regions))
for i, (region, value) in enumerate(regions.items()):
with cols[i]:
st.button(str(value), key=f"btn_{region}")
st.write(region)
'''