Update app.py

app.py

@@ -1,7 +1,7 @@
 from cmath import pi
 from json import load, tool
 from os import stat
-from telnetlib import RCP
+#from telnetlib import RCP
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -31,19 +31,23 @@ queried_female = st.checkbox("Female",value=True)
 
 @st.cache(allow_output_mutation=True)
 def gen_load() -> pd.DataFrame:
-    df = my.get_data()
+    #df = my.get_data()
+    df = pd.read_csv('Locations.csv')
     return df
 
+#AI Emotional State Score: Anxiety, Confusion, Trepidation, Fear, Guilt
+
 @st.cache(allow_output_mutation=True)
 def gen_load_old() -> pd.DataFrame:
     # file = pd.read_csv('us-zip-code-latitude-and-longitude.txt',names = ['Zip','City','State','lat','lon','Timezone',"Daylight Savings", "Geo point"], skiprows=1, delimiter=";")
     filename = "US.txt"
     file = pd.read_csv(filename,names = ['Country','Zip','City','State','Abb','Name1','Code1','Name2','Code2','lat','lon','Accuracy'], delimiter="\t",usecols=["Zip","City","State","Abb","lat","lon"])
-    #file = pd.read_csv('US.txt',names = ['Country','Zip','City','State','Abb','Name1','Code1','Name2','Code2','lat','lon','Accuracy'], delimiter="\t",usecols=["Zip","City","State","Abb","lat","lon"])
     file['Zip'] = file['Zip'].astype(str).str.zfill(5)
     w = lambda x: (x[-5:])
     filename = "NPIProviders.xlsx"
-    df = pd.read_excel('...xlsx',header = None,names = ['Zip','FCTIN','FCName', 'Procedures','Age','Gender','Admitted','Form','Cohort'], skiprows=1,converters={'Zip':w },usecols="A,B,C,D,E,F,G,H,I")   
+    df = pd.read_csv('Locations.csv',header = None,names = ['Zip','TIN','Name', 'Services','Age','Gender','StartDate','Questions','Entity'], skiprows=1,converters={'Zip':w },usecols="A,B,C,D,E,F,G,H,I")  
+    'Zip','TIN','Name', 'Services','Age','Gender','StartDate','Questions','Entity'
+     
     df = df.merge(file, left_on = 'Zip', right_on = 'Zip', how='inner')
     df = df.fillna(" ")
     return df
@@ -78,11 +82,11 @@ def load_data(state = None,zip_code = None,city = None,age = None,male  = None,f
     except:
         pass
 
-    value_counts = df["FCName"].value_counts()
-    df2 = pd.DataFrame(value_counts)
-    df2 = df2.reset_index()
-    df2.columns = ['FCName', 'Count']
-    df = df.merge(df2, left_on = 'FCName', right_on = 'FCName',how='left')
+    #value_counts = df["FCName"].value_counts()
+    #df2 = pd.DataFrame(value_counts)
+    #df2 = df2.reset_index()
+    #df2.columns = ['FCName', 'Count']
+    #df = df.merge(df2, left_on = 'FCName', right_on = 'FCName',how='left')
 
     return df
 
@@ -119,7 +123,7 @@ def mapF(data):
         layers=[layer],
         initial_view_state= view_state,
         tooltip = {
-            "html": "<b>City,State:</b> {City} </br> <b>Procedures:</b> {Count} </br> <b>F:</b> {FCName}",
+            "html": "<b>City,State:</b> {City} </br> <b>Services:</b> {Count} </br> <b>F:</b> {FCName}",
            "style": {
                 "backgroundColor": "steelblue",
                 "color": "white"
@@ -129,10 +133,10 @@ def mapF(data):
     # st.pydeck_chart(r)
     components.html(r.to_html(as_string=True), height=600)
 
-data = load_data(state=queried_state,zip_code=queried_zip_code,city=queried_city,age=queried_age,male=queried_male,female=queried_female,selected_cohorts=queried_cohorts)
+data = load_data(state=queried_state,zip_code=queried_zip_code,city=queried_city,age=queried_age,male=queried_male,female=queried_female)
 
 if st.checkbox('Display data ?'):
     data
 
-mapFs(data)
+mapF(data)