Upload app.py

app.py

@@ -0,0 +1,165 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 11 07:26:42 2022
+
+@author: mritchey
+"""
+# streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\quick address\quick_address.py"
+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')
+        # final2['County Office Url']=url
+    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)