Delete app.py

app.py

@@ -1,157 +0,0 @@
-import streamlit as st
-from streamlit_folium import st_folium
-import pandas as pd
-import numpy as np
-import folium
-from joblib import Parallel, delayed
-
-
-@st.cache
-def convert_df(df):
-    return df.to_csv(index=0).encode('utf-8')
-
-
-def map_results(results):
-    for index, row in results.iterrows():
-        address, sq_ft = results.loc[index,
-                                     'Address'], results.loc[index, 'Total Area']
-        html = f"""<p style="arial"><p style="font-size:14px"> 
-                {address}  
-                <br> Square Footage: {sq_ft}"""
-
-        iframe = folium.IFrame(html)
-        popup = folium.Popup(iframe,
-                             min_width=140,
-                             max_width=140)
-
-        folium.Marker(location=[results.loc[index, 'Lat'],
-                                results.loc[index, 'Lon']],
-                      fill_color='#43d9de',
-                      popup=popup,
-                      radius=8).add_to(m)
-    return folium
-
-
-@st.cache
-def get_housing_data(address_input):
-    address = address_input.replace(
-        ' ', '+').replace(',', '').replace('#+', '').upper()
-    try:
-        census = pd.read_json(
-            f"https://geocoding.geo.census.gov/geocoder/geographies/onelineaddress?address={address}&benchmark=2020&vintage=2020&format=json")
-        results = census.iloc[:1, 0][0]
-        matchedAddress_first = results[0]['matchedAddress']
-        matchedAddress_last = results[-1]['matchedAddress']
-        lat, lon = results[0]['coordinates']['y'], results[0]['coordinates']['x']
-        # lat2, lon2 = results[-1]['coordinates']['y'], results[-1]['coordinates']['x']
-        censusb = pd.DataFrame({'Description': ['Address Input', 'Census Matched Address: First',
-                                                'Census Matched Address: Last', 'Lat', 'Lon'],
-                                'Values': [address_input, matchedAddress_first, matchedAddress_last, lat, lon]})
-
-        #Property Records
-        url = f'https://www.countyoffice.org/property-records-search/?q={address}'
-        county_office_list = pd.read_html(url)
-
-        if county_office_list[1].shape[1] == 2:
-            df2 = pd.concat([county_office_list[0], county_office_list[1]])
-        else:
-            df2 = county_office_list[0]
-        df2.columns = ['Description', 'Values']
-
-        final = censusb.append(df2)
-
-        #Transpose
-        final2 = final.T
-        final2.columns = final2.loc['Description']
-        final2 = final2.loc[['Values']].set_index('Address Input')
-        
-    except:
-        final2 = address_input
-    return final2
-
-
-@st.cache(allow_output_mutation=True)
-def address_quick(df, n_jobs=128):
-    if isinstance(df, pd.DataFrame):
-        df = df.drop_duplicates()
-        df['address_input'] = df.iloc[:, 0]+', '+df.iloc[:, 1] + \
-            ', '+df.iloc[:, 2]+' '+df.iloc[:, 3].astype(str).str[:5]
-        df['address'] = df['address_input'].replace(
-            {' ': '+', ',': ''}, regex=True).str.upper()
-        df['address'] = df['address'].replace({'#+': ''}, regex=True)
-        # addresses=df['address'].values
-        addresses_input = df['address_input'].values
-    else:
-        addresses_input = [df]
-    results = Parallel(n_jobs=n_jobs, prefer="threads")(
-        delayed(get_housing_data)(i) for i in addresses_input)
-    results_df = [i for i in results if isinstance(i, pd.DataFrame)]
-    results_errors = [i for i in results if not isinstance(i, pd.DataFrame)]
-    errors = pd.DataFrame({'Error Addresses': results_errors})
-    final_results = pd.concat(results_df)
-    final_results = final_results[final_results.columns[2:]].copy()
-
-    return final_results, errors
-
-
-st.set_page_config(layout="wide")
-col1, col2 = st.columns((2))
-
-address = st.sidebar.text_input(
-    "Address", "1500 MOHICAN DR, FORESTDALE, AL, 35214")
-uploaded_file = st.sidebar.file_uploader("Choose a file")
-uploaded_file = 'C:/Users/mritchey/addresses_sample.csv'
-address_file = st.sidebar.radio('Choose',
-                                ('Single Address', 'Addresses (Geocode: Will take a bit)'))
-
-
-if address_file == 'Addresses (Geocode: Will take a bit)':
-    try:
-        df = pd.read_csv(uploaded_file)
-        cols = df.columns.to_list()[:4]
-        with st.spinner("Getting Data: Hang On..."):
-           results, errors = address_quick(df[cols])
-
-    except:
-        st.header('Make Sure File is Loaded First and then hit "Addresses"')
-
-else:
-    results, errors = address_quick(address)
-
-m = folium.Map(location=[39.50, -98.35],  zoom_start=3)
-
-with col1:
-    st.title('Addresses')
-    map_results(results)
-    st_folium(m, height=500, width=500)
-
-with col2:
-    st.title('Results')
-    results.index = np.arange(1, len(results) + 1)
-    st.dataframe(results)
-    csv = convert_df(results)
-    st.download_button(
-        label="Download data as CSV",
-        data=csv,
-        file_name='Results.csv',
-        mime='text/csv')
-    try:
-        if errors.shape[0] > 0:
-
-            st.header('Errors')
-            errors.index = np.arange(1, len(errors) + 1)
-            st.dataframe(errors)
-            # st.table(errors.assign(hack='').set_index('hack'))
-            csv2 = convert_df(errors)
-            st.download_button(
-                label="Download Errors as CSV",
-                data=csv2,
-                file_name='Errors.csv',
-                mime='text/csv')
-    except:
-        pass
-
-st.markdown(""" <style>
-#MainMenu {visibility: hidden;}
-footer {visibility: hidden;}
-</style> """, unsafe_allow_html=True)