simplified main

streamlit-app.py

@@ -1,77 +1,10 @@
-import math
-import random
+from src.page import TheftRadarPage
 
-import pandas as pd
-import streamlit as st
-from numpy import random
 
-"""
-# Berlin Bike Theft Radar!
-"""
-"""
-Theft period is from January 2021 to May 2022. The exact geo coordinates are approximated using the 
-information from [data source](https://www.govdata.de/web/guest/suchen/-/details/fahrraddiebstahl-in-berlin).
-The next enhancement will include the deep learning based prediction model to predict geo-coordinates where theft 
-incident is about to occur.
-"""
+def main():
+    page = TheftRadarPage()
+    page.render()
 
 
-def get_updated_latitude(prev_latitude):
-    earth_radius = 6378.137
-    pi = math.pi
-    m = (1 / ((2 * pi / 360) * earth_radius)) / 1000
-    factor = 1 if random.randint(0, 1) % 2 == 0 else -1
-    # Within 3 KM
-    # my_meters = random.randint(1, 2000)
-
-    my_meters = random.normal(loc=0, scale=1000, size=1)[0]
-
-    # New Latitude
-    return prev_latitude + factor * (my_meters * m)
-
-
-def get_updated_longitude(longitude, latitude):
-    earth_radius = 6378.137
-    pi = math.pi
-
-    # Within 3 KM
-    # my_meters = random.randint(1, 3000)
-
-    my_meters = random.normal(loc=0, scale=1000, size=1)[0]
-
-    cos = math.cos
-    m = (1 / ((2 * pi / 360) * earth_radius)) / 1000
-    # New Longitude
-    return longitude + (my_meters * m) / cos(latitude * (pi / 180))
-
-
-"#### Number of samples"
-samples = st.slider("", min_value=100, max_value=25000)
-
-
-def load_data():
-    df = pd.read_csv("data/bicycle_theft_berlin.csv", sep=",", header=0, encoding="utf-8").sample(samples)
-
-    # Sub-district Longitudes and Latitudes
-    # Debug using -> df["lat"] = df["sub_district_lat"].astype(float)
-    df["lat"] = df["sub_district_lat"].astype(float).apply(lambda x: get_updated_latitude(x))
-    # Debug using ->  df["lon"] = df["sub_district_long"].astype(float)
-    df["lon"] = df.apply(lambda row: get_updated_longitude(row.sub_district_long, row.sub_district_lat), axis=1)
-    # df.apply(lambda row: row.a + row.b)
-    return df
-
-
-data = load_data()
-
-"#### Theft Map"
-st.map(data)
-
-agree = st.checkbox('Show raw data')
-
-if agree:
-    "#### Source Data"
-    data
-
-"#### Contact Developer"
-
-st.markdown("Programmed with ❤️ by [Muhammad Ahsan](https://www.linkedin.com/in/muhammad-ahsan/)")
+if __name__ == "__main__":
+    main()