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City-State-FIPS-Zip-Search
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from cmath import pi
from json import load, tool
from os import stat
import streamlit as st
import pandas as pd
import numpy as np
import pydeck as pdk
from typing import Dict, Union
import streamlit.components.v1 as components

st.title("Live 3D Map")
location = st.checkbox('Location Filter')
queried_zip_code = None
queried_city = None
queried_state = None
queried_age = None
if location:
    queried_zip_code = st.text_input('Zip Code:')
    queried_city = st.text_input('City')
    queried_state = st.selectbox('State:',  ('AL', 'AK', 'AZ', 'AR', 'AS','CA','CO','CT','DE','DC','FL','GA','GU','HI','ID','IL',
    'IN','IA','KS','KY','LA','ME','MD','MA','MI','MN','MS','MO','MT','NE','NV','NH','NJ','NM','NY','NC','ND','CM','OH',
    'OK','OR','PA','PR','RI','SC','SD','TN','TX','UT','VT','VA','VI','WA','WV','WI','WY'))
ageBox = st.checkbox("Age Filter")
if ageBox:
    queried_age = st.slider("Age",0,200,(0,200))

queried_male = st.checkbox("Male",value=True)
queried_female = st.checkbox("Female",value=True)

@st.cache(allow_output_mutation=True)
def gen_load() -> pd.DataFrame:
    df = pd.read_csv('US.txt')
    return df

import streamlit as st
import pandas as pd
import pydeck as pdk

def ShowCityDataframe(uscities, US):
    df = pd.read_csv(uscities)
    df1 = pd.read_csv(uscities)
    df2 = pd.read_csv(uscities)
    
    st.title("City FIPS, Location, and Population")
    st.text("Search for any city in the United States:")

    search_query = st.text_input(label="City Name", value="")
    if search_query != "":
        df = df1[df1["city"].str.contains(search_query, case=False)]
    st.subheader("City Detail")
    st.write(df)
    
    search_query2 = st.text_input(label="Zip Code", value="")
    if search_query2 != "":
        df = df2[df2["zips"].str.contains(search_query2, case=False)]
    st.subheader("Zip Code Area Detail")
    st.write(df)

uscities = "uscities.csv"  # CSV - Columns are: "city","city_ascii","state_id","state_name","county_fips","county_name","lat","lng","population","density","source","military","incorporated","timezone","ranking","zips","id"
US = "US.txt"   # TSV - Columns are: Country	Zip	City	State	Area	AreaCode	Latitude	Longitude	Include
                # TSV Columns sample:       US	99553	Akutan	Alaska	AK	Aleutians East	013			54.143	-165.7854	1
us-zip-codes = "us-zip-code-latitude-and-longitude.txt"  # SSV - Columns are:  Zip;City;State;Latitude;Longitude;Timezone;Daylight savings time flag;geopoint
                # SSV Columns sample:       71937;Cove;AR;34.398483;-94.39398;-6;1;34.398483,-94.39398

ShowCityDataframe(uscities, US)