Create app.py

app.py

@@ -0,0 +1,156 @@
+import streamlit as st
+import pandas as pd
+from joblib import Parallel, delayed
+from redfin import Redfin
+import requests
+requests.urllib3.disable_warnings()
+
+@st.cache_data
+def convert_df(df):
+    return df.to_csv()
+
+
+def red_fin_api(add):
+    client = Redfin()
+    response = client.search(add)
+    
+    try:
+        url = response['payload']['exactMatch']['url']
+        initial_info = client.initial_info(url)
+    except:
+        initial_info = add
+    
+    try:
+         property_id = initial_info['payload']['propertyId']
+         mls_data = client.below_the_fold(property_id)
+    except:
+        mls_data = add
+    
+
+    try:
+        
+        lat,lon=initial_info['payload']['latLong'].values()
+        img=initial_info['payload']['preloadImageUrls'][0]
+        
+        # int_group=r[1]['payload']['amenitiesInfo']['superGroups'][0]['amenityGroups']
+       
+        ext_prop=mls_data['payload']['amenitiesInfo']['superGroups'][1]['amenityGroups'][0]['amenityEntries']
+        ext_prop=pd.DataFrame(ext_prop)
+        ext_prop['amenityValues']=[i[0] for i in ext_prop['amenityValues'].values]
+        ext_prop2=ext_prop[['referenceName','amenityValues']].T
+        ext_prop2.columns=ext_prop2.values[0]
+        ext_prop3=ext_prop2.tail(1).reset_index(drop=1)
+
+        df=pd.DataFrame(mls_data['payload']['publicRecordsInfo']['basicInfo'],index=[0]).drop(columns=['apn','propertyLastUpdatedDate','displayTimeZone'])
+    
+        df['Lat']=lat
+        df['Lon']=lon
+        # df['Image']=img
+        df2=df.join(ext_prop3)
+        df2.insert(0,'url',f'https://www.redfin.com{url}')
+        
+    except:
+        df2=pd.DataFrame({'Missing':[1]})  
+    
+    df2.insert(0,'Address Input',add)  
+    
+
+    return df2
+
+
+    
+def catch_errors(addresses):
+    try: 
+        return red_fin_api(addresses)
+    except:
+        return pd.DataFrame({'Address Input':[addresses]})
+
+@st.cache_data
+def process_multiple_address(addresses):
+    results=Parallel(n_jobs=64, prefer="threads")(delayed(catch_errors)(i) for i in addresses)
+    return results
+    
+
+st.set_page_config(layout="wide")
+st.header("Redfin Data")  
+
+address = st.sidebar.text_input("Single Address:", "190 Pebble Creek Dr Etna, OH 43062")
+uploaded_file = st.sidebar.file_uploader("Upload Multiple Addresses:")
+
+if uploaded_file is not None:
+    try:
+        df = pd.read_csv(uploaded_file)
+    except:
+        df = pd.read_excel(uploaded_file)
+     
+    address_cols=list(df.columns[:4])
+    df[address_cols[-1]]=df[address_cols[-1]].astype(str).str[:5].astype(int).astype(str)
+    df[address_cols[-1]]=df[address_cols[-1]].apply(lambda x: x.zfill(5))
+    
+    df['Address All']=df[address_cols[0]]+', '+df[address_cols[1]]+', '+df[address_cols[2]]+' '+df[address_cols[3]]
+    
+    results= process_multiple_address(df['Address All'].values)
+    results=pd.concat(results).reset_index(drop=1)
+    results.index=results.index+1
+
+else:    
+    results=red_fin_api(address).reset_index(drop=1)
+    results.index=results.index+1
+
+
+cols_order=['Address Input',  'sqFtFinished', 'totalSqFt', 'yearBuilt', 'propertyTypeName', 'beds', 'baths', 'numStories',
+            'url',
+            'Lat', 'Lon']
+cols_other=[i for i in results.columns if i not in cols_order ]
+
+
+try:
+    missing=results.query("Missing==Missing")[['Address Input']].reset_index()
+    missing.index=missing.index+1
+    missing['index']=missing['index']+1
+    missing.columns=['Input Position','Address Input']
+    results=results.query("Missing!=Missing")[cols_order+cols_other].drop(columns=['Missing'])
+except:
+    results=results[cols_order+cols_other]
+   
+
+results['yearBuilt']=results['yearBuilt'].fillna(0).astype(int).astype(str).replace('0','')
+
+
+with st.container():
+    st.write("Redfin Results")
+    st.dataframe(
+        results,
+        column_config={
+       
+            "url": st.column_config.LinkColumn("url"),
+             "Image": st.column_config.LinkColumn("Image"),
+           
+        },
+        hide_index=False,
+
+    )
+
+csv = convert_df(results)
+st.download_button(
+    label="Download Results as CSV",
+    data=csv,
+    file_name=f'Redfin Results.csv',
+    mime='text/csv')
+
+try:
+    csv2 = convert_df(missing)
+    st.write("Missing Addresses")
+    st.dataframe(missing)
+    st.download_button(
+        label="Download Missing Data as CSV",
+        data=csv2,
+        file_name=f'Redfin missing.csv',
+        mime='text/csv')
+except:
+    pass
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)