Hugging Face's logo

Spaces:
MadhuV28
/
Live_Mode_Real_Time
Runtime error

App Files Community
Live_Mode_Real_Time
File size: 2,958 Bytes 
6878592
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
 
import time  # to simulate a real time data, time loop

import numpy as np  # np mean, np random
import pandas as pd  # read csv, df manipulation
import plotly.express as px  # interactive charts
import streamlit as st  # 🎈 data web app development
dataset_url = "https://raw.githubusercontent.com/Lexie88rus/bank-marketing-analysis/master/bank.csv"

# read csv from a URL
@st.experimental_memo
def get_data() -> pd.DataFrame:
    return pd.read_csv(dataset_url)

df = get_data()
st.set_page_config(
    page_title="Real-Time Data Science Dashboard",
    page_icon="βœ…",
    layout="wide",
)
# dashboard title
st.title("Real-Time / Live Data Science Dashboard")
# top-level filters
job_filter = st.selectbox("Select the Job", pd.unique(df["job"]))
# dataframe filter
df = df[df["job"] == job_filter]
# create three columns
kpi1, kpi2, kpi3 = st.columns(3)

# fill in those three columns with respective metrics or KPIs
kpi1.metric(
    label="Age ⏳",
    value=round(avg_age),
    delta=round(avg_age) - 10,
)

kpi2.metric(
    label="Married Count πŸ’",
    value=int(count_married),
    delta=-10 + count_married,
)

kpi3.metric(
    label="A/C Balance οΌ„",
    value=f"$ {round(balance,2)} ",
    delta=-round(balance / count_married) * 100,
)
# create two columns for charts
fig_col1, fig_col2 = st.columns(2)

with fig_col1:
    st.markdown("### First Chart")
    fig = px.density_heatmap(
        data_frame=df, y="age_new", x="marital"
    )
    st.write(fig)
   
with fig_col2:
    st.markdown("### Second Chart")
    fig2 = px.histogram(data_frame=df, x="age_new")
    st.write(fig2)
    st.markdown("### Detailed Data View")
st.dataframe(df)
for seconds in range(200):

    df["age_new"] = df["age"] * np.random.choice(range(1, 5))
    df["balance_new"] = df["balance"] * np.random.choice(range(1, 5))
    time.sleep(1)
    # creating a single-element container.
placeholder = st.empty()
with placeholder.container():

    # create three columns
    kpi1, kpi2, kpi3 = st.columns(3)

    # fill in those three columns with respective metrics or KPIs
    kpi1.metric(
        label="Age ⏳",
        value=round(avg_age),
        delta=round(avg_age) - 10,
    )
    
    kpi2.metric(
        label="Married Count πŸ’",
        value=int(count_married),
        delta=-10 + count_married,
    )
    
    kpi3.metric(
        label="A/C Balance οΌ„",
        value=f"$ {round(balance,2)} ",
        delta=-round(balance / count_married) * 100,
    )

    # create two columns for charts
    fig_col1, fig_col2 = st.columns(2)
    
    with fig_col1:
        st.markdown("### First Chart")
        fig = px.density_heatmap(
            data_frame=df, y="age_new", x="marital"
        )
        st.write(fig)
        
    with fig_col2:
        st.markdown("### Second Chart")
        fig2 = px.histogram(data_frame=df, x="age_new")
        st.write(fig2)

    st.markdown("### Detailed Data View")
    st.dataframe(df)
    time.sleep(1)