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import
streamlit
as
st
def
intro
():
import
streamlit
as
st
st
.
write
(
"# Welcome to Streamlit! 👋"
)
st
.
sidebar
.
success
(
"Select a demo above."
)
st
.
markdown
(
"""
Streamlit is an open-source app framework built specifically for
Machine Learning and Data Science projects.
**👈 Select a demo from the dropdown on the left** to see some examples
of what Streamlit can do!
### Want to learn more?
- Check out [streamlit.io](https://streamlit.io)
- Jump into our [documentation](https://docs.streamlit.io)
- Ask a question in our [community
forums](https://discuss.streamlit.io)
### See more complex demos
- Use a neural net to [analyze the Udacity Self-driving Car Image
Dataset](https://github.com/streamlit/demo-self-driving)
- Explore a [New York City rideshare dataset](https://github.com/streamlit/demo-uber-nyc-pickups)
"""
)
def
mapping_demo
():
from
urllib
.
error
import
URLError
import
pandas
as
pd
import
pydeck
as
pdk
import
streamlit
as
st
st
.
markdown
(
f"#
{
list
(
page_names_to_funcs
.
keys
())[
2
]
}
"
)
st
.
write
(
"""
This demo shows how to use
[`st.pydeck_chart`](https://docs.streamlit.io/develop/api-reference/charts/st.pydeck_chart)
to display geospatial data.
"""
)
@
st
.
cache_data
def
from_data_file
(
filename
):
url
=
(
"https://raw.githubusercontent.com/streamlit/"
"example-data/master/hello/v1/%s"
%
filename
)
return
pd
.
read_json
(
url
)
try
:
ALL_LAYERS
=
{
"Bike Rentals"
:
pdk
.
Layer
(
"HexagonLayer"
,
data
=
from_data_file
(
"bike_rental_stats.json"
),
get_position
=
[
"lon"
,
"lat"
],
radius
=
200
,
elevation_scale
=
4
,
elevation_range
=
[
0
,
1000
],
extruded
=
True
,
),
"Bart Stop Exits"
:
pdk
.
Layer
(
"ScatterplotLayer"
,
data
=
from_data_file
(
"bart_stop_stats.json"
),
get_position
=
[
"lon"
,
"lat"
],
get_color
=
[
200
,
30
,
0
,
160
],
get_radius
=
"[exits]"
,
radius_scale
=
0.05
,
),
"Bart Stop Names"
:
pdk
.
Layer
(
"TextLayer"
,
data
=
from_data_file
(
"bart_stop_stats.json"
),
get_position
=
[
"lon"
,
"lat"
],
get_text
=
"name"
,
get_color
=
[
0
,
0
,
0
,
200
],
get_size
=
15
,
get_alignment_baseline
=
"'bottom'"
,
),
"Outbound Flow"
:
pdk
.
Layer
(
"ArcLayer"
,
data
=
from_data_file
(
"bart_path_stats.json"
),
get_source_position
=
[
"lon"
,
"lat"
],
get_target_position
=
[
"lon2"
,
"lat2"
],
get_source_color
=
[
200
,
30
,
0
,
160
],
get_target_color
=
[
200
,
30
,
0
,
160
],
auto_highlight
=
True
,
width_scale
=
0.0001
,
get_width
=
"outbound"
,
width_min_pixels
=
3
,
width_max_pixels
=
30
,
),
}
st
.
sidebar
.
markdown
(
"### Map Layers"
)
selected_layers
=
[
layer
for
layer_name
,
layer
in
ALL_LAYERS
.
items
()
if
st
.
sidebar
.
checkbox
(
layer_name
,
True
)
]
if
selected_layers
:
st
.
pydeck_chart
(
pdk
.
Deck
(
map_style
=
"mapbox://styles/mapbox/light-v9"
,
initial_view_state
=
{
"latitude"
:
37.76
,
"longitude"
:
-
122.4
,
"zoom"
:
11
,
"pitch"
:
50
,
},
layers
=
selected_layers
,
)
)
else
:
st
.
error
(
"Please choose at least one layer above."
)
except
URLError
as
e
:
st
.
error
(
"""
**This demo requires internet access.**
Connection error: %s
"""
%
e
.
reason
)
def
plotting_demo
():
import
time
import
numpy
as
np
import
streamlit
as
st
st
.
markdown
(
f"#
{
list
(
page_names_to_funcs
.
keys
())[
1
]
}
"
)
st
.
write
(
"""
This demo illustrates a combination of plotting and animation with
Streamlit. We're generating a bunch of random numbers in a loop for around
5 seconds. Enjoy!
"""
)
progress_bar
=
st
.
sidebar
.
progress
(
0
)
status_text
=
st
.
sidebar
.
empty
()
last_rows
=
np
.
random
.
randn
(
1
,
1
)
chart
=
st
.
line_chart
(
last_rows
)
for
i
in
range
(
1
,
101
):
new_rows
=
last_rows
[
-
1
, :]
+
np
.
random
.
randn
(
5
,
1
).
cumsum
(
axis
=
0
)
status_text
.
text
(
"%i%% Complete"
%
i
)
chart
.
add_rows
(
new_rows
)
progress_bar
.
progress
(
i
)
last_rows
=
new_rows
time
.
sleep
(
0.05
)
progress_bar
.
empty
()
# Streamlit widgets automatically run the script from top to bottom. Since
# this button is not connected to any other logic, it just causes a plain
# rerun.
st
.
button
(
"Re-run"
)
def
data_frame_demo
():
from
urllib
.
error
import
URLError
import
altair
as
alt
import
pandas
as
pd
import
streamlit
as
st
st
.
markdown
(
f"#
{
list
(
page_names_to_funcs
.
keys
())[
3
]
}
"
)
st
.
write
(
"""
This demo shows how to use `st.write` to visualize Pandas DataFrames.
(Data courtesy of the [UN Data Explorer](http://data.un.org/Explorer.aspx).)
"""
)
@
st
.
cache_data
def
get_UN_data
():
AWS_BUCKET_URL
=
"https://streamlit-demo-data.s3-us-west-2.amazonaws.com"
df
=
pd
.
read_csv
(
AWS_BUCKET_URL
+
"/agri.csv.gz"
)
return
df
.
set_index
(
"Region"
)
try
:
df
=
get_UN_data
()
countries
=
st
.
multiselect
(
"Choose countries"
,
list
(
df
.
index
), [
"China"
,
"United States of America"
]
)
if
not
countries
:
st
.
error
(
"Please select at least one country."
)
else
:
data
=
df
.
loc
[
countries
]
data
/=
1000000.0
st
.
write
(
"### Gross Agricultural Production ($B)"
,
data
.
sort_index
())
data
=
data
.
T
.
reset_index
()
data
=
pd
.
melt
(
data
,
id_vars
=
[
"index"
]).
rename
(
columns
=
{
"index"
:
"year"
,
"value"
:
"Gross Agricultural Product ($B)"
}
)
chart
=
(
alt
.
Chart
(
data
)
.
mark_area
(
opacity
=
0.3
)
.
encode
(
x
=
"year:T"
,
y
=
alt
.
Y
(
"Gross Agricultural Product ($B):Q"
,
stack
=
None
),
color
=
"Region:N"
,
)
)
st
.
altair_chart
(
chart
,
use_container_width
=
True
)
except
URLError
as
e
:
st
.
error
(
"""
**This demo requires internet access.**
Connection error: %s
"""
%
e
.
reason
)
page_names_to_funcs
=
{
"—"
:
intro
,
"Plotting Demo"
:
plotting_demo
,
"Mapping Demo"
:
mapping_demo
,
"DataFrame Demo"
:
data_frame_demo
,
}
demo_name
=
st
.
sidebar
.
selectbox
(
"Choose a demo"
,
page_names_to_funcs
.
keys
())
page_names_to_funcs
[
demo_name
]()
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