Spaces:
Sleeping
Sleeping
| 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 | |
| import streamlit_authenticator as stauth | |
| from st_aggrid import GridOptionsBuilder, AgGrid, GridUpdateMode, DataReturnMode | |
| from streamlit_extras.stylable_container import stylable_container | |
| from streamlit_extras.metric_cards import style_metric_cards | |
| import warnings | |
| from streamlit_extras.switch_page_button import switch_page | |
| import pickle | |
| import time | |
| import plotly.io as pio | |
| pio.templates.default = 'plotly' | |
| warnings.filterwarnings('ignore') | |
| st.set_page_config(layout="wide") | |
| color = {'Unknown/Other':'#3a393a', 'Black or African American': '#2993A3', 'White':'#666766', 'Native American':'#f4b780', 'Hispanic':'#a0cd7c', 'Pacific Islander':'#a680ba','Asian':'#f37e85'} | |
| 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) | |
| #------------------------------- Metric Cards ------------------------# | |
| pop = pd.read_csv("Population.csv") | |
| cols = st.columns(2) | |
| cols[0].metric(label="Felony Convicitons for the Population (2015-2023)", value=32979, delta='inc') | |
| v = int(pop.loc[0, pop.columns.str.contains("Black")].values[0]) / int(pop.loc[0, pop.columns.str.contains("White")].values[0]) | |
| cols[1].metric(label="Black : White Population ratio", value=str(round(v*100, 2))+"/100", delta = "As per 2020") | |
| style_metric_cards() | |
| #------------------------------- Page 0 ------------------------------# | |
| Page0 = st.container(border=True) | |
| Page0 = stylable_container(key="Page0", css_styles=[""" {box-shadow: rgba(0, 0, 0, 0.24) 0px 3px 25px;}"""])#, """{background-color:white;opacity:0.8;}"""])#, """ {border: 2px solid rgba(49, 51, 63, 0.2); border-radius: 10px;}"""]) | |
| cols = Page0.columns([0.5,5.5,0.2,3.3,0.5]) | |
| cols[1].header("Most Common Charges Booked in the County", anchor='section-1', help="A Graph to understrand the most commnly charged crimes in the county by racial distribution. The graph gives a yearly estimates of the crimes. You can choose to see the data by Race/Race and see upto top 300.") | |
| cols = Page0.columns([0.5,3, 3,3,0.5]) | |
| Race = cols[1].selectbox('Select Race', tuple(color.keys()), index=None) | |
| Dec = cols[2].selectbox('Change Data', tuple(['Court Data', 'Curently Incarcerated']), index=0) | |
| st.markdown(""" | |
| <style> | |
| .stSlider [data-baseweb=slider]{ | |
| width: 100%; | |
| } | |
| </style> | |
| """,unsafe_allow_html=True) | |
| timeline = cols[3].slider('Select Timeline for Cases', 1990, 2022, (2015,2021)) | |
| cols = Page0.columns([6.5,3.5]) | |
| if Race != None: | |
| df1 = pd.read_csv("Court_appA.csv") if Dec == 'Court Data' else pd.read_csv("Incarceration_appA.csv") | |
| df1 = df1[df1['Race'] == Race] | |
| df1 = df1[(df1['year'] >= timeline[0]) & (df1['year'] <= timeline[1])] | |
| df1 = df1[['Charges', 'Race', 'count']].groupby('Charges').agg({'count':'sum'}).reset_index() | |
| df1['count'] = (df1['count'] / (timeline[1] - timeline[0])) if timeline[1] != timeline[0] else df1['count'] | |
| df1 = df1.sort_values('count') | |
| fig = px.bar(pd.DataFrame(df1[-40:]), y='Charges', x='count', orientation='h') | |
| fig.update_traces(marker_color=color[Race]) | |
| else: | |
| df1 = pd.read_csv("Court_appB.csv") if Dec == 'Court Data' else pd.read_csv("Incarceration_appB.csv") | |
| df1 = df1[(df1['year'] >= timeline[0]) & (df1['year'] <= timeline[1])] | |
| df1 = df1[['Charges', 'Race', 'count']].groupby(['Charges','Race']).agg({'count':'sum'}).reset_index() | |
| df1['count'] = (df1['count'] / (timeline[1] - timeline[0])) if timeline[1] != timeline[0] else df1['count'] | |
| df1 = df1.sort_values(['Charges', 'count']) | |
| fig = px.bar(df1, y='Charges', x='count', orientation='h', color_discrete_map=color, color='Race') | |
| fig.update_layout(barmode='stack', yaxis={'categoryorder':'total ascending'}) | |
| if Dec == 'Court Data': | |
| fig.update_layout(width=900, height=900, xaxis = {'side':'top'},title_y=0.99, xaxis_title="Number of Cases Per Year", yaxis_title="Charges") | |
| else: | |
| fig.update_layout(width=900, height=900, xaxis = {'side':'top'},title_y=0.99, xaxis_title="Total Cases", yaxis_title="Charges") | |
| fig.update_layout(legend=dict(yanchor="top", y=0.7, xanchor="left", x=0.6)) | |
| cols[0].plotly_chart(fig, theme=None) | |
| # df = pd.read_excel("California Penal Code .xlsx") | |
| # # dfx = pd.read_csv("Court_appB.csv") | |
| # # df1 = df1[['Charges', 'count']].groupby(['Charges']).agg({'count':'sum'}).reset_index() | |
| # # df1 = df1.sort_values(['Charges', 'count']) | |
| # cols[1].markdown('######') | |
| # cols[1].markdown('####') | |
| # cols[1].dataframe(df, height=760) | |
| # if st.session_state["authentication_status"]: | |
| # st.session_state.auth.logout() | |
| # else: | |
| # switch_page('app') | |