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| import streamlit as st | |
| import numpy as np | |
| import plotly.figure_factory as ff | |
| import plotly.express as px | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| from streamlit_extras.stylable_container import stylable_container | |
| import pickle | |
| import ast | |
| # Implement AND condition when downloading data | |
| st.set_page_config(layout="wide") | |
| color = {'Black or African American': '#ff7eb6', 'White':'#be95ff', 'Native American':'#0f62fe', 'Indian':'#82cfff', 'Japanese':'lightyellow', 'Korean':'gray','Chinese':'yellow', 'Hispanic':'#dface6', 'Pacific Islander':'#3ddbd9', 'Unkown/Other':'#c1c7cd','Filipino':'Green', 'Middle Eastern':'#000000','Vietnamese':'coral','Laotian':'cornsilk','Cambodian':'darkcyan','Other Asian':'darkgoldenrod', 'Asian':'#82cfff',} | |
| file = open("login_state.pkl",'rb') | |
| st.session_state['logged_in'] = pickle.load(file) | |
| file.close() | |
| #----------------------------NavBar-------------------------# | |
| hide_menu_style = """ | |
| <style> | |
| #MainMenu {visibility: hidden;} | |
| header {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hide_menu_style, unsafe_allow_html=True) | |
| if st.session_state.get("logged_in") == False or st.session_state.get("logged_in") == None: | |
| st.switch_page("app.py") | |
| st.markdown('<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">', unsafe_allow_html=True) | |
| st.markdown(""" | |
| <head> | |
| <script defer src="https://pyscript.net/latest/pyscript.js"></script> | |
| </head> | |
| <nav class="navbar fixed-top navbar-expand-lg navbar-dark" style="background-color: #3498DB;"> | |
| <a class="navbar-brand" href="https://www.ipr.northwestern.edu/who-we-are/faculty-experts/redbird.html" target="_blank">RJA Dashboard</a> | |
| <button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> | |
| <span class="navbar-toggler-icon"></span> | |
| </button> | |
| <div class="collapse navbar-collapse" id="navbarNav" style="width:100%;"> | |
| <ul class="navbar-nav"> | |
| <li class="nav-item active"> | |
| <a class="nav-link disabled" href="https://sanbernardinorja.streamlit.app/page_0" target="_self">Arrest Summary<span class="sr-only">(current)</span></a> | |
| </li> | |
| <li class="nav-item"> | |
| <a class="nav-link" href="https://sanbernardinorja.streamlit.app/page_1" target="_self">Charge By Race</a> | |
| </li> | |
| <li class="nav-item"> | |
| <a class="nav-link" href="https://sanbernardinorja.streamlit.app/page_2" target="_self">Download Data</a> | |
| </li> | |
| </ul> | |
| <ul class="navbar-nav ml-auto"> | |
| <li class="nav-item mr-auto" style="padding-left:5px;padding-right:5px;outline-color:#f0f2f5;border: 2px solid white;border-radius:10px;"> | |
| <a class="nav-link" href="https://sanbernardinorja.streamlit.app/ target="_self" >Logout</a> | |
| </li> | |
| </ul> | |
| </div> | |
| </nav> | |
| """, unsafe_allow_html=True) | |
| # with open("list_of_charges.pkl", "rb") as fp: # Unpickling | |
| # charges = pickle.load(fp) | |
| # Page 3 | |
| Page3 = stylable_container(key="Page3", css_styles=""" {box-shadow: rgba(0, 0, 0, 0.24) 0px 3px 15px;} """) | |
| Page3.header("Download Data", anchor = 'section-3', help = 'Download data relevant to your case. Use the filters to Determine what data do you need.') | |
| selected_point = [] | |
| #loc = "DA_referrals_2022(1).csv" | |
| Charge = Page3.selectbox('Select Type of Charge', ('Booking Charge', 'Filed Charge', 'CDCR')) | |
| loc = "Referral_page2.csv" if Charge == 'Booking Charge' else "Court_page2.csv" if Charge == 'Filed Charge' else "Sentence_page2.csv" | |
| df = pd.read_csv(loc) | |
| df = df.drop(columns='Unnamed: 0') | |
| uid = 'UID' if Charge == 'Booking Charge' else 'Case Number' | |
| filename = "list_of_charges.pkl" if Charge == 'Booking Charge' else "list_of_charges_detailed.pkl" | |
| with open(filename, "rb") as fp: # Unpickling | |
| charges = pickle.load(fp) | |
| #split_first = False if loc == "Referral_page2.csv" else True | |
| cols = Page3.columns(4) | |
| Filtera = cols[0].multiselect('Select Charges', tuple(charges), help='Select Charges relevant to your case and client') | |
| Filterb = cols[2].multiselect('Select Ethnicity', tuple(color.keys()), help='Select relevant Ethnicity') | |
| Filterc = cols[1].selectbox('Select Function - Charges', ("AND", "OR"), help="AND - All Clients charged with all chosen charges \n\n OR - All Clients charged with atleast one of the chosen charges") | |
| cols_list = df.columns.tolist() | |
| cols_list.remove('Charges') | |
| cols_list.remove('Race') | |
| cols_list.remove(uid) | |
| Filterd = cols[3].multiselect('Select Additional Columns to View', tuple(cols_list), help="1. Incident Number - Unique Incident ID assigned to SF Cases \n\n 2. Gender - Gender of the perpetrator \n\n 3. Booked.Case.Type / Filed.Case.Type - Felony / Misdemeanor \n\n 4. Age.at.Arrest - Age at which the person was arrested \n\n 5. Status.CTNum / Status.CTNum.Agg - Case Status After arrest or filing i.e. new charges filed or discharged \n\n 6. Description - Arrest / Charges Description \n\n 7. Year - Year of Arrest for Booking charge / Year of Charging for Filed Charge or Sentenced For \n\n 8. Case.Dispo - Case Disposition Number \n\n 9. Dispo.Description - Final court decision") | |
| if len(Filtera) > 0: | |
| filst = "|".join(Filtera) | |
| df = df[df['Charges'].str.contains(filst, regex=True, na=False)] | |
| if len(Filterb) > 0: | |
| filst = "|".join(Filterb) | |
| df = df[df['Races'].str.contains(filst, regex=True, na=False)] | |
| if Filterc == 'AND' and len(Filtera) > 0: | |
| cnos = [df[df['Charges'].str.contains(i)][uid].tolist() for i in Filtera] | |
| cm_cnos = list(set.intersection(*map(set, cnos))) | |
| df = df[df[uid].isin(cm_cnos)] | |
| df = df.drop_duplicates() | |
| df[uid] = df[uid].map(str) | |
| for i in df.columns[1:]: | |
| df[i] = df[i].str.strip('[]').str.split(',') | |
| disp_cols = [uid, 'Charges', 'Race'] + Filterd | |
| Page3.dataframe(df[disp_cols],width=1300) | |