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] | open | false | null | [] | null | 2 | 2020-09-28T22:45:12 | 2022-11-15T17:00:20 | null | CONTRIBUTOR | null | Right now expander only displays the header as a text field. It would be nice to do this with st.header or st.subheader.
## Proposal 1
Perhaps a configuration for header type, i.e.
`st.expander("header", header_type="header|subheader|text(default)", expanded=True)`
## Proposal 2
Support markup with header text so that `# header` will display as a header and so on. This could open up too many possibilities
[Source](https://discuss.streamlit.io/t/hide-collapse-fold-a-section/1579/10)
---
Community voting on feature requests enables the Streamlit team to understand which features are most important to our users.
**If you'd like the Streamlit team to prioritize this feature request, please use the 👍 (thumbs up emoji) reaction in response to the initial post.** | {
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---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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] | closed | false | null | [] | null | 1 | 2020-09-28T19:28:08 | 2020-10-06T00:11:40 | 2020-10-06T00:11:40 | CONTRIBUTOR | null | In `st.expander`, the header currently has a cursor but is not togglable. Only Show/Hide is currently togglable. Make the entire header container togglable as well.
[Source](https://discuss.streamlit.io/t/hide-collapse-fold-a-section/1579/10) | {
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] | closed | false | null | [] | null | 1 | 2020-09-28T19:02:06 | 2020-10-12T15:25:04 | 2020-10-06T08:04:36 | CONTRIBUTOR | null | ### Proposal 1: Once screen width drops below a certain threshold (600px?), we just stack every column
- Kinda brute force but simple to reason about, anyways.
- Might make things like grid layout weird
### Proposal 2: When a specific column drops below a certain threshold (200px?), wrap that one
- Problem: how to handle variable-width columns?
- Problem: 3 same-width columns, but two in line 1 and one in line 2? The second will fill the entire space unless we bound it somehow | {
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Cypress tests occasionally gets stuck. Probably a result of minor dependency upgrade #1954. Alternatively `start-server-and-test` was updated 8 days ago but first instance was from 3 days ago.
## Sample failures
https://app.circleci.com/pipelines/github/streamlit/streamlit/4642/workflows/d3bc2d57-a589-4f4f-9652-402e88d29684/jobs/11493
https://app.circleci.com/pipelines/github/streamlit/streamlit/4634/workflows/690b1faa-43f2-4e49-abf6-04a77e24223f/jobs/11467
https://app.circleci.com/pipelines/github/streamlit/streamlit/4630/workflows/c8bb323a-6482-451b-a72b-b6bcf9d38ee7/jobs/11460
https://app.circleci.com/pipelines/github/streamlit/streamlit/4619/workflows/98fcf7a4-12b9-44b2-967f-719f95e9cdc3/jobs/11429 (first instance)
## Error
```
Error: Timed out waiting for: http://localhost:3000
at MergeMapSubscriber.timer.pipe.mergeMap [as project] (/home/circleci/repo/frontend/node_modules/wait-on/lib/wait-on.js:130:25)
at MergeMapSubscriber._tryNext (/home/circleci/repo/frontend/node_modules/rxjs/internal/operators/mergeMap.js:67:27)
at MergeMapSubscriber._next (/home/circleci/repo/frontend/node_modules/rxjs/internal/operators/mergeMap.js:57:18)
at MergeMapSubscriber.Subscriber.next (/home/circleci/repo/frontend/node_modules/rxjs/internal/Subscriber.js:66:18)
at AsyncAction.dispatch [as work] (/home/circleci/repo/frontend/node_modules/rxjs/internal/observable/timer.js:31:16)
at AsyncAction._execute (/home/circleci/repo/frontend/node_modules/rxjs/internal/scheduler/AsyncAction.js:71:18)
at AsyncAction.execute (/home/circleci/repo/frontend/node_modules/rxjs/internal/scheduler/AsyncAction.js:59:26)
at AsyncScheduler.flush (/home/circleci/repo/frontend/node_modules/rxjs/internal/scheduler/AsyncScheduler.js:52:32)
at ontimeout (timers.js:436:11)
at tryOnTimeout (timers.js:300:5)
at listOnTimeout (timers.js:263:5)
at Timer.processTimers (timers.js:223:10)
``` | {
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} | [] | closed | false | null | [] | null | 0 | 2020-09-28T03:26:36 | 2020-10-01T17:11:16 | 2020-09-28T03:31:12 | CONTRIBUTOR | null | ## Before contributing (PLEASE READ!)
⚠️ **If your contribution is more than a few lines of code, then prior to starting to code on it please post in the issue saying you want to volunteer, and then wait for a positive response.** And if there is no issue for it yet, create it first.
This helps make sure (1) two people aren't working on the same thing, (2) this is something Streamlit's maintainers believe should be implemented/fixed, (3) any API, UI, or deeper architectural changes that need to be implemented have been fully thought through by Streamlit's maintainers, and (4) your time is well spent!
More information in our wiki: https://github.com/streamlit/streamlit/wiki/Contributing
---
**Issue:** Please include a link to the issue you're addressing. If no issue exists, create one first and then link it here.
**Description:** Describe the changes you made to the code, so it's easier for the reader to navigate your pull request. Usually this is a bullet list.
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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Type here a clear and concise description of the bug. Aim for 2-3 sentences.
Bar Chart annotations are not displayed correctly
What are the steps we should take to reproduce the bug:
1. After unzipping attached file run script bug_stream1.py
2. We can notice the only bar with annotation in the top of the bar is the last bar.
3. There are 2 screenshots included in the zipfile, we can compare with expected results from Jupiter screenshot.
[bug_streamliter.zip](https://github.com/streamlit/streamlit/files/5288261/bug_streamliter.zip)
## Expected behavior:
The tools should display annotation on top of the bar on all bars and not only the last bar
## Actual behavior:
Only last bars has annotated values
## Is this a regression?
First time running this code
# Debug info
- Streamlit version: (get it with `$ streamlit version`)
Streamlit, version 0.65.2
- Python version: (get it with `$ python --version`)
Python 3.7.7
- Using Conda? PipEnv? PyEnv? Pex?
Conda
- OS version:
MacOS Cataline 10.15
- Browser version:
Chrome Version 85.0.4183.102
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} | [] | closed | false | null | [] | null | 0 | 2020-09-25T20:30:32 | 2020-09-28T13:20:20 | 2020-09-28T13:20:20 | CONTRIBUTOR | null | Adds two frequently encountered errors when working with Streamlit: not having Python on the PATH and not being able to install pyarrow due to compiler errors. | {
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} | [] | closed | false | null | [] | null | 0 | 2020-09-25T18:54:22 | 2021-07-24T00:36:45 | 2020-09-30T05:02:57 | COLLABORATOR | null | **Issue:** #1859
**Description:**
* Dropdown Options are more complex than BaseWeb, so they needed a complete override to handle everything
**Notes:** Button matches all others now
**Notes:** Slightly darker hover gray, but it matches selects. BaseWeb uses a drop shadow, which I think looks better.
Active State (No change)
Recording Indicator (No Change)
Red Stop Recording (No Change)
Disabled Menu Options (No Change)
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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} | [] | closed | false | null | [] | null | 0 | 2020-09-25T18:47:42 | 2020-09-28T13:17:08 | 2020-09-25T19:44:42 | CONTRIBUTOR | null | At the place where this line occurs, it mentions as a pre-requisite to "make sure you install Streamlit". But then in the next section, we talk about installing Streamlit into a virtual environment. | {
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I’d find it super helpful to list the latest features that exist in beta or experimental in the nightlies in the documentation. Maybe with a brief description and a gif. 🤔
Cheers,
C | {
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I don't know how to show full LONG text of labels in bar charts
# Screenshots
* I want to show full text on the left of chart.
* As you can see in the screenshot above:
['**Very long text manageme**nt(mobile)', '**Very long text (He**xbyAgent)'...]
was cut to
['nt(mobile)', 'xbyAgent)'...]
Thank you, | {
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When I remove st.beta_set_page_config(layout="wide") from the code, the wide mode layout sticks around until I hard refresh. I'm seeing this on streamlit-nightly
## Is this a regression?
Believe so
# Debug info
- Streamlit version: version 0.67.1.dev20200921
- Python version: Python 3.8.5
- Pipenv
- OS version: MacOS Catalina 10.15.6
- Browser version: Chrome Version 84.0.4147.89 (Official Build) (64-bit)
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] | closed | false | null | [] | null | 1 | 2020-09-23T18:55:34 | 2020-11-10T00:05:23 | 2020-11-10T00:05:23 | NONE | null | # Summary
Dictionary objects with nan values return syntaxerror
# Steps to reproduce
What are the steps we should take to reproduce the bug:
Minimum viable code snippet with a working dictionary and a non-working dictionary
## Expected behavior:
I would expect that the dictionary object would allow nan values.
## Actual behavior:
Actual behavior is syntax error
## Is this a regression?
No
# Debug info
- Streamlit version: 0.66.0
- Python version: 3.7
- Using Conda
- OS version: Windows 10
- Browser version: Chrome
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https://github.com/streamlit/streamlit/blob/develop/lib/tests/streamlit/url_util_test.py#L22-L23 | {
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Fixes https://github.com/streamlit/streamlit/issues/1138 | {
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] | null | 1 | 2020-09-23T06:47:01 | 2021-07-24T00:36:44 | 2020-09-28T18:05:07 | CONTRIBUTOR | null | [Issue #1135](https://app.clubhouse.io/streamlit/story/1135/fix-user-id-and-report-hash)
1. If we have no ID in ~/.streamlit/.stable_random_id, create one
2. Read the ID from ~/.streamlit/.stable_random_id
3. __installation_id__ = str(_uuid.uuid5(_uuid.NAMESPACE_DNS, STABLE_RANDOM_ID)
4. Store the old __installation_id__ in a id_v1 field and the new __installation_id__ in a id_v2 field. Send both to Segment.
Note: I had issues mocking the creation of .stable_random_id from __init__.py so moved it out of there | {
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} | [] | closed | false | null | [] | null | 2 | 2020-09-23T05:34:39 | 2021-07-24T00:36:44 | 2020-09-25T18:03:44 | COLLABORATOR | null | **Issue:** #1860
**Description:**
Buttons are converted to its own custom component based on the DOM button
Prettier also updates some look and feel (sorry but they had to be included)
**After**
**`st.button`**
Standard
In Sidebar
Hover
Focus
Active
**Notes** Mostly a little bigger top/bottom padding
**Modal**
Modal Buttons
Secondary Button Hover
**Notes** Modal Button Border Radius is consistent with other buttons.
**Icon**
Standard
Hover
Focus+Active
**Notes** Slight change in look, smaller border radius.
**Status Bar (Small Button)**
**Notes** Should be very similar
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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**Description:**
Virtualized Multiselects require the options to be a single line. To resolve this, we are going to truncate options past with an ellipsis.
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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Currently: 13s to onload:
With `loading="lazy"`: 0.7s to onload
Lighthouse before
Lighthouse after
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import streamlit as st
import pathlib
dict1 = {pathlib.PurePath(d).name: d for d in pathlib.Path(__file__).parent.iterdir()}
st.selectbox('please choose', [*dict1])
newitem = st.text_input('please input new item')
if newitem:
with open(newitem, 'w') as f:
f.write(newitem) | {
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] | closed | false | null | [] | null | 3 | 2020-09-22T14:42:03 | 2020-10-28T21:19:06 | 2020-09-23T00:17:50 | NONE | null | # Summary
Since v0.67.0, options with long text for Multiselect and Selectbox selectors overlap each other when the text starts wrapping.
# Steps to reproduce
This is easiest to replicate in the sidebar (or make the browser window smaller):
```
options = ['long text long text long text long text long text long text long text long text long text long text long text long text',
'long text long text long text long text long text long text long text long text long text long text long text long text',
'long text long text long text long text long text long text long text long text long text long text long text long text',
'long text long text long text long text long text long text long text long text long text long text long text long text']
st.sidebar.selectbox(label='label', options=options)
```
## Expected behavior:
The options will be legible and pretty in the dropdown regardless of string length.
## Actual behavior:
Options are not legible and the functionality is not usable for options with long text.
## Is this a regression?
That is, did this use to work the way you expected in the past?
yes
This was likely caused by streamlit#1888.
# Debug info
- Streamlit version: 0.67.0
- Python version: Python 3.8.3
- Using Conda? PipEnv? PyEnv? Pex? no...
- OS version: CentOS 7
- Browser version: Firefox 80.0.1 | {
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} | [] | closed | false | null | [] | null | 0 | 2020-09-21T19:51:28 | 2021-07-24T00:36:44 | 2020-09-21T21:34:33 | CONTRIBUTOR | null | **Issue:** #2017
**Description:** Empty containers were always rendered as part of st.columns. This makes it so that the block must allow empty in order to be rendered.
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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} | [] | closed | false | null | [] | null | 0 | 2020-09-21T16:54:47 | 2021-07-24T00:36:44 | 2020-09-21T18:47:07 | CONTRIBUTOR | null | This is primarily to unblock Tim's work on ImmutableJS. Before we get to release, we're still missing:
- Docstrings
- Mentions in the docs
- Product approval
- Any bugs we deem P1
P1 bugs:
- #2017 | {
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] | closed | false | null | [] | null | 3 | 2020-09-21T16:04:13 | 2022-11-20T22:05:11 | 2021-12-16T01:42:46 | NONE | null | # Summary
The `use_container_width` argument doesn't work as expected when using Altair trellis charts. Specifically the chart does not resize when changing the container size.
# Steps to reproduce
```python
# Based on Altair documentation example: https://altair-viz.github.io/gallery/trellis_stacked_bar_chart.html
import altair as alt
from vega_datasets import data
import streamlit as st
source = data.barley()
chart = alt.Chart(source).mark_bar().encode(
x='yield',
y='variety',
color='site'
)
trellis_chart = alt.Chart(source).mark_bar().encode(
column='year',
x='yield',
y='variety',
color='site'
)
st.altair_chart(chart, use_container_width=True) # works
st.altair_chart(trellis_chart, use_container_width=True) # does not work
```
## Expected behavior:
That the chart dynamically resizes based on the container width
## Actual behavior:
Trellis chart does not resize (first chart is a reference, which works correctly):
Reducing browser window size further fully cuts off second half of trellis chart:
## Is this a regression?
Unknown.
# Debug info
- Streamlit version: 0.67.0
- Python version: 3.7.6
- Using Conda
- OS version: macOS Catalina 10.15.6
- Browser version: Google Chrome 80.0.3987.106
# Additional information
N/A | {
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} | [] | closed | false | null | [] | null | 0 | 2020-09-21T14:24:26 | 2020-09-21T18:03:13 | 2020-09-21T18:03:13 | CONTRIBUTOR | null | Removes stale comment in docs about returning value from Streamlit call, which is fairly well covered elsewhere in the docs. | {
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favicon() missing 1 required positional argument: 'image', this is the bug
# Steps to reproduce
```python3
import streamlit as st
from PIL import Image
favicon = Image.open('./favicon.png') # favicon.png can be any PNG image
st.beta_set_favicon(favicon)
```
## Expected behavior:
The favicon should be displayed.
## Actual behavior:
favicon() missing 1 required positional argument: 'image'
still the favicon is the default streamlit app favicon
## Is this a regression?
That is, did this use to work the way you expected in the past?
**No, this is the first time I am using this function.**
# Debug info
- Streamlit version: 0.64.0
- Python version: 3.6.9
- Using Conda? PipEnv? PyEnv? Pex? No, I am not using any of them
- OS version: 18.04 Linux
- Browser version: Mozilla Firefox 78.0.2
# Additional information
I have used the same example syntax which was provided by the docs of beta_set_favicon function in the prompt. | {
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] | closed | false | null | [] | null | 0 | 2020-09-20T19:39:11 | 2021-03-08T22:12:50 | 2021-03-08T22:12:50 | NONE | null | # Summary
Once I started `streamlit hello` I see the above stated error for the Plotting Demo. All the other demos work perfectly but this one says the above stated error for some reason
# Steps to reproduce
What are the steps we should take to reproduce the bug:
1. Do `pip install streamlit`
2. Run `streamlit hello`
3. Select `Plotting Demo` with `Show Code checked`
## Expected behavior:
Gives an example of plotted charts and shows the relevant code with it.
## Actual behavior:
Shows an error `StreamlitAPIException: 'RangeIndex' object has no attribute 'step' ` in a red box and the code window is not shown.
## Is this a regression?
Not sure as I tried this the first time
# Debug info
- Streamlit version: `Streamlit, version 0.67.0`
- Python version: `Python 3.6.10 :: Anaconda, Inc.`
- Using Conda: `Yes`
- OS version: `macOS Catalina Version 10.15.6`
- Browser version: `Safari Version 13.1.2`
<img width="865" alt="Screenshot 2020-09-21 at 01 00 39" src="https://user-images.githubusercontent.com/32913433/93720497-05954400-fba7-11ea-8587-1b3a717759ea.png">
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] | closed | false | null | [] | null | 1 | 2020-09-20T12:55:03 | 2021-05-23T20:11:52 | 2020-09-20T20:52:54 | NONE | null | Earlier this year, GitHub launched [GitHub Discussions](https://github.blog/2020-05-06-new-from-satellite-2020-github-codespaces-github-discussions-securing-code-in-private-repositories-and-more/#discussions).
Right now this feature is only enabled on public repositories and the easiest way to get it activated seems to be by requesting that the feature is enabled on [this](https://github.community/t/can-one-apply-for-github-discussions-beta/121120/203) support thread.
Since Streamlit is already on GitHub, I think it would be really convenient to have discussions here as well. | {
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When an app with expander is running, the expander does not get removed completely on rerun.
# Steps to reproduce
What are the steps we should take to reproduce the bug:
1. Create and run an app with `st.expander`
2. Remove `st.expander` while app is running
3. Rerun code
## Expected behavior:
Expander is gone with label and toggle
## Actual behavior:
Label and toggle is still available | {
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] | closed | false | null | [] | null | 1 | 2020-09-18T20:08:07 | 2021-11-30T21:43:02 | 2021-11-30T21:43:02 | NONE | null | # While changing the contents, the multiselect box resets to all its options
I'm writing an app and this happened, so I made a much simpler version, and it's still happening. Basically, I have a multiselect box in the sidebar. I select or unselect items. Randomly it will go back to having all the default options. It's like Russian Roulette because you never know when it'll happen. It may happen on the first click or perhaps on the twenty-fifth. In addition, the other parts of the app don't reflect the change until the next call.
# Steps to reproduce
Example code for a small app that will reproduce the bug:
```
import streamlit as st
from sklearn.datasets import load_wine
@st.cache(allow_output_mutation=True)
def load_data():
data = load_wine(as_frame=True).frame
return data.sample(frac=1, replace=False,
random_state=101).reset_index(drop=True)
show_cols = st.sidebar.empty()
data_slot = st.empty()
X = load_data()
select_cols = ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash',
'magnesium', 'total_phenols', 'flavanoids',
'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity',
'hue', 'od280/od315_of_diluted_wines', 'proline']
chosen_cols = show_cols.multiselect("Select columns to display",
select_cols, select_cols)
data_slot.table(X.copy()[chosen_cols])
```
## Expected behavior:
Selected options stay selected and unselected ones stay unselected.
## Actual behavior:
Occasionally resets to default options
## Is this a regression?
I haven't used Streamlit enough to tell
# Debug info
- Streamlit version: 0.66.0
- Python version: 3.7.7
- OS version: macOS Mojave (10.14.6)
- Browser version: Chrome
# Additional information
I've tried to fix the bug by taking out lots of things. Originally, I had the list of selections/defaults being called as `list(X.columns)`, but I thought that might be the problem, so I listed them out manually. I'm also displaying `X.copy()` in case it was changing the data frame, but it's not. I've also taken out all other unnecessary widgets, elements, and functions.
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Bokeh column layout are not being displayed
# Steps to reproduce
Execute the code below
## Expected behavior:
Column layout should work
## Actual behavior:
Other layouts are working fine
# Debug info
- Streamlit version: Streamlit, version 0.67.0
- Python version: 3.7.4
- Using PyEnv
- OS version: Linux Mint 19
- Browser version: Google Chrome 84.0.4147.125
# Additional information
```import bokeh
bokeh.sampledata.download()
# In[2]:
from bokeh.embed import file_html
from bokeh.resources import CDN, INLINE
from bokeh.sampledata.stocks import AAPL, GOOG, IBM, MSFT
import holoviews as hv
from holoviews.operation.timeseries import rolling
from holoviews import opts
import numpy as np
import pandas as pd
import streamlit as st
# In[3]:
hv.extension('bokeh')
color_cycle = hv.Cycle(values=['#A6CEE3', '#B2DF8A','#33A02C', '#FB9A99'])
def get_curve(data, label=''):
df = pd.DataFrame(data)
df['date'] = df.date.astype('datetime64[ns]')
return hv.Curve(df, ('date', 'Date'), ('adj_close', 'Price'), label=label)
hv.Dimension.type_formatters[np.datetime64] = '%Y'
aapl = get_curve(AAPL, label='AAPL')
goog = get_curve(GOOG, label='GOOG')
ibm = get_curve(IBM, label='IBM')
msft = get_curve(MSFT, label='MSFT')
avg_curve = rolling(aapl, rolling_window=30).relabel('Average')
avg_scatter = hv.Scatter((np.array(AAPL['date'], dtype=np.datetime64), np.array(AAPL['adj_close'])),
('date', 'Date'), ('adj_close', 'Price'), label='close')
# In[4]:
full_view = ((aapl * goog * ibm * msft) + (avg_scatter * avg_curve)).opts(
opts.Curve(color=color_cycle),
opts.Curve('Curve.Average', color='navy'),
opts.Scatter(alpha=0.2, size=4, color='darkgrey'),
opts.Overlay(width=400, height=400, legend_position='top_left'))
# In[5]:
full_view
# In[6]:
single_view = aapl
# In[7]:
single_view
# In[8]:
type(hv.render(single_view, backend='bokeh'))
# In[9]:
### THIS WORKS
st.write(hv.render(single_view, backend='bokeh'))
# In[10]:
type(hv.render(full_view, backend='bokeh'))
# In[11]:
### THIS WORKS
st.write(hv.render(full_view[0], backend='bokeh'))
# In[12]:
### THIS DOES NOT WORK
st.write(hv.render(full_view, backend='bokeh'))```
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} | [] | closed | false | null | [] | null | 1 | 2020-09-18T05:19:25 | 2021-07-24T00:36:45 | 2020-10-08T05:36:06 | CONTRIBUTOR | null | Completely redesigned `st.file_uploader` with the following additional features. The redesigned file uploader also closes out the following bugs. This redesign does not include file persistence on disk or file picker functionalities. (Closes #1202, Closes #831)
**Features**
- Enabled multiple file uploads (#912)
- Returns file meta data including name, size, and file type (Closes #897, Closes #896)
- Ability to clear uploaded files (Closes #897)
- Redesigned widget with better user feedback
**Bugs**
- Works with android 8.0.0 on chromium browser (Closes #1537)
- Users only load the files they have uploaded in their session. Restarting the server will clear out all the uploaded files across all open sessions. However, rerun and clearing the cache will retain the uploaded files. Users must manually delete the files in order to clear files during open sessions. (Closes #1686)
**Demos**
- [Different uploaders](https://s4a.streamlit.io/karriebear/streamlit-tests/file-uploader/demo.py/+/)
- [Image uploader](https://s4a.streamlit.io/karriebear/streamlit-tests/file-uploader/demo2.py/+/ )
This is the initial set of changes. There are [open items](https://www.notion.so/streamlit/Implementation-Plan-WIP-0e8d0642ad9a41e498ec139f220ed943) needed prior to release
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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] | closed | false | null | [] | null | 3 | 2020-09-17T17:24:04 | 2022-09-23T14:13:56 | 2022-09-23T14:13:56 | CONTRIBUTOR | null | Would also be nice to have a resizable text_area, which adapt length to the content. Thank you for the great work!
_Originally posted by @P-mir in https://github.com/streamlit/streamlit/issues/277#issuecomment-694138238_ | {
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from PIL import Image
import io
import time
# Imports
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
# import tensorflow as tf
import tensorflow.compat.v1 as tf
import zipfile
import cv2
import numpy as np
import csv
import time
from packaging import version
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
import connect
# Object detection imports
from utils import label_map_util
from utils import visualization_utils as vis_util
import shutil
import moviepy.editor as moviepy
import cv2 as cv
from app import gun
def fun():
with open("style.css") as f:
st.markdown('<style>{}</style>'.format(f.read()), unsafe_allow_html=True)
path = "path/image/tele_logo.jpg"
image = Image.open(path)
st.image(image, width=150)
st.markdown("<h1 style='text-align: center; color:darkturquoise;'>VIDARIA PLATFORM DEMO</h1>",
unsafe_allow_html=True)
st.markdown("<h1 style ='text-align: center; color:dimgray;'>Telenext System ML Capability Showcase",unsafe_allow_html=True)
st.sidebar.title("Select Feature")
listmenu =[ "Vehicle Detection","other"]
option =st.sidebar.selectbox("Select Option",listmenu)
if option=="Vehicle Detection":
menu2=["Video_Detection","Video_Demo_test"]
choice1 =st.selectbox("choose Option", menu2)
if choice1=="Video_Demo_test":
test_vid = os.listdir(connect.INPUT_VID)
test_vid1 = st.selectbox('Please choose a test Video', test_vid)
if test_vid1 == "walk.mp4":
source = "path/input-ar/walk.mp4"
destination = "path/video/input.mp4"
new_path = shutil.copy(source, destination)
gun()
if test_vid1 == "sample_1.mp4":
source = "path/input-ar/sample_1.mp4"
destination = "path/video/input.mp4"
new_path = shutil.copy(source, destination)
gun()
if test_vid1 == "sample_2.mp4":
source = "path/input-ar/sample_2.mp4"
destination = "path/video/input.mp4"
new_path = shutil.copy(source, destination)
gun()
#
if choice1=="Video_Detection":
frameST = st.empty()
st.title("Vehicle Detection Demo")
st.subheader("To Detect objects From Videos Upload a file")
uploaded_file = st.file_uploader("Choose a video...", type=["mp4"])
temporary_location = False
if uploaded_file is not None:
g = io.BytesIO(uploaded_file.read()) ## BytesIO Object
temporary_location = "path/video/input.mp4"
with open(temporary_location, 'wb') as out: ## Open temporary file as bytes
out.write(g.read()) ## Read bytes into file
# close file
out.close()
# Read the video
# st.video(temporary_location)
frameST = st.empty()
a = st.button("Process")
if a:
st.markdown("<h1 style='color:brown;'>Processing Video Please Wait.. </h1>",
unsafe_allow_html=True)
# initialize .csv
with open('traffic_measurement.csv', 'w') as f:
writer = csv.writer(f)
csv_line = \
'Vehicle Type/Size, Vehicle Color, Vehicle Movement Direction, Vehicle Speed (km/h)'
writer.writerows([csv_line.split(',')])
if version.parse(tf.__version__) < version.parse('1.4.0'):
raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!'
)
# input video
cap = cv2.VideoCapture('path/video/input.mp4')
# Variables
total_passed_vehicle = 0 # using it to count vehicles
# By default I use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2018_01_28'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = \
'http://download.tensorflow.org/models/object_detection/'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
# Download Model
# uncomment if you have not download the model yet
# Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# Loading label map
# Label maps map indices to category names, so that when our convolution network predicts 5, we know that this corresponds to airplane. Here I use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map,
max_num_classes=NUM_CLASSES,
use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Helper code
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width,
3)).astype(np.uint8)
if os.path.exists("traffic_measurement.csv"):
os.remove("traffic_measurement.csv")
# Detection
def object_detection_function():
total_passed_vehicle = 0
speed = 'waiting...'
direction = 'waiting...'
size = 'waiting...'
color = 'waiting...'
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# for all the frames that are extracted from input video
while cap.isOpened():
(ret, frame) = cap.read()
if not ret:
print('end of the video file...')
break
input_frame = frame
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(input_frame, axis=0)
# Actual detection.
(boxes, scores, classes, num) = \
sess.run([detection_boxes, detection_scores,
detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
(counter, csv_line) = \
vis_util.visualize_boxes_and_labels_on_image_array(
cap.get(1),
input_frame,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=4,
)
total_passed_vehicle = total_passed_vehicle + counter
# insert information text to video frame
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(
input_frame,
'Detected Vehicles: ' + str(total_passed_vehicle),
(10, 35),
font,
0.8,
(0, 0xFF, 0xFF),
2,
cv2.FONT_HERSHEY_SIMPLEX,
)
# when the vehicle passed over line and counted, make the color of ROI line green
if counter == 1:
cv2.line(input_frame, (0, 200), (640, 200), (0, 0xFF, 0), 5)
else:
cv2.line(input_frame, (0, 200), (640, 200), (0, 0, 0xFF), 5)
# insert information text to video frame
cv2.rectangle(input_frame, (10, 275), (230, 337), (180, 132, 109), -1)
cv2.putText(
input_frame,
'ROI Line',
(545, 190),
font,
0.6,
(0, 0, 0xFF),
2,
cv2.LINE_AA,
)
cv2.putText(
input_frame,
'LAST PASSED VEHICLE INFO',
(11, 290),
font,
0.5,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_SIMPLEX,
)
cv2.putText(
input_frame,
'-Movement Direction: ' + direction,
(14, 302),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)
cv2.putText(
input_frame,
'-Speed(km/h): ' + speed,
(14, 312),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)
cv2.putText(
input_frame,
'-Color: ' + color,
(14, 322),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)
cv2.putText(
input_frame,
'-Vehicle Size/Type: ' + size,
(14, 332),
font,
0.4,
(0xFF, 0xFF, 0xFF),
1,
cv2.FONT_HERSHEY_COMPLEX_SMALL,
)
# cv2.imshow('vehicle detection', input_frame)
# frameST.image(input_frame, channels="BGR")
if csv_line != 'not_available':
with open('traffic_measurement.csv', 'a') as f:
writer = csv.writer(f)
(size, color, direction, speed) = \
csv_line.split(',')
writer.writerows([csv_line.split(',')])
cap.release()
cv2.destroyAllWindows()
object_detection_function()
st.balloons()
with open("traffic_measurement.csv") as input:
st.title("This is the detected Vehicle list")
st.text(input.read())
else:
pass
if __name__ == '__main__':
fun()
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See title
# Steps to reproduce
What are the steps we should take to reproduce the bug:
```python
st.file_uploader(...)
```
works fine locally but on Kubernetes it just does nothing even after a file is dragged onto it.
## Is this a regression?
Yes
# Debug info
- Streamlit version: (get it with `$ streamlit version`) 0.66.0
- Python version: (get it with `$ python --version`) 3.7
- Using Conda? PipEnv? PyEnv? Pex? Conda
- OS version: Linux
- Browser version: Chrome
# Additional information
If needed, add any other context about the problem here. For example, did this bug come from https://discuss.streamlit.io or another site? Link the original source here!
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- Release 0.67
- Add date to figure-less `st.plot` deprecation
- Postponed balloons release
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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Let's say I create a dashboard and a user navigates to it and customizes the input controls to show what they want to see. Now they want to share that view with someone else. Right now, as far as I know, they have to give someone else the URL and then tell them what input values to set. That is, they cannot share the app *and its current configuration* with a single URL.
### Solution
**MVP:** As the input controls change, update the URL in the address bar to have a query string containing the values of all parameters that aren't at their default values, and the new values that they've been assigned. Then a user can just share that URL at any time and always be sharing the full state of the app.
**Possible additions:** The above solution would work only for apps below a certain size; if you get to the point where you're uploading large files or having large text input boxes, then a query string is not large enough. In such a case, the app could cache the inputs on disk and create hashes, which go in the query string, and live for up to X days.
### Note
While I realize that it's possible for streamlit users to add functionality akin to this, it's much better to simply add it to streamlit itself once, in generality, and then all users get it without having to code it once each.
---
Community voting on feature requests enables the Streamlit team to understand which features are most important to our users.
**If you'd like the Streamlit team to prioritize this feature request, please use the 👍 (thumbs up emoji) reaction in response to the initial post.**
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[1] The one I'm interested in is optional chaining, aka the ability to replace:
`const var = foo && foo.bar`
with:
`const var = foo?.bar`
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* Issue is fixed by passing svg string to urlllib.parse.quote to encode special characters
## Before contributing (PLEASE READ!)
⚠️ **If your contribution is more than a few lines of code, then prior to starting to code on it please post in the issue saying you want to volunteer, and then wait for a positive response.** And if there is no issue for it yet, create it first.
This helps make sure (1) two people aren't working on the same thing, (2) this is something Streamlit's maintainers believe should be implemented/fixed, (3) any API, UI, or deeper architectural changes that need to be implemented have been fully thought through by Streamlit's maintainers, and (4) your time is well spent!
More information in our wiki: https://github.com/streamlit/streamlit/wiki/Contributing
---
**Issue:** Please include a link to the issue you're addressing. If no issue exists, create one first and then link it here.
Issue: https://github.com/streamlit/streamlit/issues/1957
**Description:** Describe the changes you made to the code, so it's easier for the reader to navigate your pull request. Usually this is a bullet list.
As discussed:
* Removed any trailing/leading spaces
* Removed all newlines from the svg string
* Passed the svg string to `quote` from `urllib.parse` (with `safe=''`)
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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**Description:**
BaseWeb's Table was too much for our needs, so we built our based on the DOM.
**Before**
Note the Gray rows/cols are actually DataFrames (The yellow is very intentional from the tests).
**After**
Note: the empty table/dataframe is adjusted to still show the table headers
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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Python 3.9 RC1
https://www.python.org/downloads/release/python-390rc1/ | {
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I wrote a streamlit app which runs just fine on my native Ubuntu 18.04 machine. I Declared all dependencies with pipenv and tested it with a new virtual env on the same machine, everything works.
But when I try to run the app within a docker container or WSL Ubuntu 18.04, I get `segmentation fault (core dumped)` as soon as I try to run the server with `streamlit run`. Same for the streamlit yolo demo. streamlit hello works though.
# Steps to reproduce
e.g. run the yolo object detection demo on WSL Ubuntu 18.04.
# Debug info
- Streamlit version: 0.66.0
- Python version: 3.8
- Using pipenv, conda
- OS version: Ubuntu 18.04 (native, docker, WSL)
- Browser version: Firefox latest, Edge Chromium
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} | [] | closed | false | null | [] | null | 1 | 2020-09-15T06:37:32 | 2020-09-15T12:26:33 | 2020-09-15T12:26:25 | NONE | null | # Summary
Type here a clear and concise description of the bug. Aim for 2-3 sentences.
# Steps to reproduce
What are the steps we should take to reproduce the bug:
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
## Expected behavior:
Explain what you expect to happen when you go through the steps above, assuming there were no bugs.
## Actual behavior:
Explain the buggy behavior you experience when you go through the steps above.
If applicable, add screenshots to help explain your problem.
## Is this a regression?
That is, did this use to work the way you expected in the past?
yes / no
# Debug info
- Streamlit version: (get it with `$ streamlit version`)
- Python version: (get it with `$ python --version`)
- Using Conda? PipEnv? PyEnv? Pex?
- OS version:
- Browser version:
# Additional information
If needed, add any other context about the problem here. For example, did this bug come from https://discuss.streamlit.io or another site? Link the original source here!
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When clicking on a slider to change its value, the app gets reloaded and all variables are changed to their default values.
# Steps to reproduce
I have an app with several parameters, set using a variety of Streamlit widgets. I try to change the value of one of the variables (a slider) by clicking on a point along the slider. Here is the slider code:
```
temp = st.slider("Temperature (F)",
min_value=30,
max_value=100,
value=70,
step=1)
```
## Expected behavior:
I expect the variable to change and the app to run with the value that is selected.
## Actual behavior:
The app reloads in the browser and all variables (including the slider on which I selected the value) are reverted to their default values.
# Debug info
- Streamlit version: 0.66.0
- Python version: 3. I 8.5
- Using Conda: yes
- OS version: macOS Catalina
- Browser version: Chrome 85
# Additional information
If needed, add any other context about the problem here. For example, did this bug come from https://discuss.streamlit.io or another site? Link the original source here!
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file = st.file_uploader("Upload file",type=['mp3','wav'])
d=st.audio(file)
i=d.save("path/audio-in/input.mp3") ???? | {
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---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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] | closed | false | null | [] | null | 7 | 2020-09-14T04:14:43 | 2022-11-09T23:08:33 | 2022-11-09T23:08:33 | NONE | null | There is a particular TensorFlow type that `st.cache` fails to cache: `tensorflow.python.util.object_identity._ObjectIdentityWrapper`. This is apparently a very common type and used in TF2 SavedModels, including HuggingFace tokenizers and TensorFlow Hub models.
I tried overriding `hash_funcs` with `id` and `lamba _: None`, but both resulted in the program hanging. Without overriding `hash_funcs`, I get a Streamlit error saying that Streamlit doesn't know how to cache the type. | {
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### Problem
I would love to be able to get an Altair selection in Python. https://github.com/domoritz/streamlit-vega-lite is a POC but it would be better if the feature was available directly in Streamlit.
### Solution
**MVP:** Support getting the row ids and the predicted.
**Possible additions:** Automatically re-filter the dataframe.
**Preferred solution:** Both. Give me the predicate and the filtered rows.
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] | closed | false | null | [] | null | 1 | 2020-09-12T06:16:44 | 2022-11-07T17:35:06 | 2020-09-14T16:39:21 | NONE | null | # Summary
The model isn't considering a major flaw. Negation.
# Steps to reproduce
If given a sentence like, "I'm very happy that I'm not in collaboration with you", it is actually a negative remark, but the model predicts as happy. Of course, I'm happy, but that's a negative review, right? Atleast for the company who's analyzing.
## Expected behavior:
Since this model is analyzing one's emotion, predicting the above sentence as happy isn't wring, but isn't enough. The client who's using this package might get a negative analysis.
So, you may have to add another section to this, which, say, predicts even the sentiment, then compare between the 2 different predictions, and then draw another conclusion from them.
## Is this a regression?
Haven't used it before.
# Debug info
- Using Conda? PipEnv? PyEnv? Nope
- OS version: Android 6
- Browser version: chrome v85 | {
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] | closed | false | null | [] | null | 0 | 2020-09-11T18:30:43 | 2020-10-07T13:14:26 | 2020-10-07T13:14:26 | CONTRIBUTOR | null | It's literally just a 3 links in bullet point format. I feel it doesn't deserve its own space in the sidebar as-is.
Some ideas for sprucing it up:
- Throw in some screenshots
- Write up a short (1-2 sentence) summary of the key things that each tutorial will let you learn
- Add in more tutorials
Or we could just remove it, if we're trying to lighten our documentation load? | {
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] | closed | false | null | [] | null | 2 | 2020-09-11T18:25:38 | 2021-05-23T20:11:51 | 2020-09-22T20:09:33 | CONTRIBUTOR | null | Our main API page loads ridiculously slow for an API page, taking 20+ seconds before basic functionality like `ctrl + f` is available. The culprit, AFAICT, is the "Streamlit preview" sections we have -- each loads in an iframe to an actual view-only streamlit app we host on AWS.
Glancing at the waterfall diagram, these appear to be loading serially (whether because of page structure or competing for network bandwidth, I'm not sure). Notice how we repeatedly load the same font and image assets:
I can think of a few solutions to this:
1. Just remove these sections entirely -- they might not add that much value, plus they're somewhat inconsistent in how they're used (many newer APIs don't include previews, afaict)
2. Replace them with screenshots, still linking to the live app. (Exception: some widgets like st.video/st.audio have controls)
3. Consolidate the previews all into a single streamlit app so we only load from one external site instead of 20; then the iframes just show different views into the app.
4. Somehow lazy-load the previews so user interaction with the page isn't blocked?
Incidentally, I feel like the preview section could use a better UI (even as simple as a box around the preview to delineate from the rest of the app) | {
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RuntimeError: Data of size 82.1MB exceeds write limit of 50.0MB
\!\[image]([https://user-images.githubusercontent.com/4397489/92728231-00a5e880-f38e-11ea-9d50-f54853ffab2e.png)|https://user-images.githubusercontent.com/4397489/92728231-00a5e880-f38e-11ea-9d50-f54853ffab2e.png)]
Using the standard function to download csv file:
{code:python}def get_table_download_link(df,filename):
"""Generates a link allowing the data in a given panda dataframe to be downloaded
in: dataframe
out: href string
"""
csv = df.to_csv(index=False)
b64 = base64.b64encode(
csv.encode()
).decode() # some strings <-> bytes conversions necessary here
return f'<a href="data:file/csv;base64,{b64}" download="{filename}.csv">Download csv file</a>'{code}
I am sending the filtered csv which is roughly more than 80 MB. How do change the max size in this case of downloading?
Ideally, i should be able to download any size or an configuration to increase the limit as per the use case.
#
## Is this a regression?
no
# Debug info
* Streamlit version: 0.65.2
* Python version: 3.7.3
* Using Conda? PipEnv? PyEnv? Pex? - Conda
* OS version: Windows 10
* Browser version: Chrome Version 85.0.4183.83 | {
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* Change balloon animation, remove jitter and re-randomization that happened for more complex apps
* Set balloon z-index below modals, popups, and the headerbar | {
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**Python -> Frontend**:
- In Python, Component functions can now take `bytes` and `bytearray` values as arguments.
- These are delivered to the frontend in the `special_args` list inside `ComponentInstance.proto`. (DataFrames are also sent inside this list.)
- The frontend delivers these bytes args to the iframe as `UInt8Array` values (which is their "native" datatype within protobuf-js).
**Frontend -> Python**:
- The `Streamlit.setComponentValue` function (implemented in our component-lib npm package) can now take `ArrayBuffer` values, and any TypedArray value (`UInt8Array`, `UInt16Array`, etc etc). These are all converted to `UInt8Array` and shipped over to Python, where they're deserialized as `bytes`.
There's also a fix to `WidgetStateManager` in here: `WidgetStateManager.getOrCreateWidgetStateProto` is now just `createWidgetStateProto`, and it always overwrites any existing value. Previously, changing the type of a WidgetState's oneOf value would *not* remove its previous value. In the case of `bytesValue`, its `value` string was also not properly updated.
(It's not clear to me whether this is a bug in protobufjs - it seems like a setter function should be responsible for updating things appropriately? But no such setter function is called in these circumstances - it's just raw data being set.) | {
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A neat app often elucidates complex problems. Hence there is often a lot to explain to carry details about the app itself, its backgrounds and further details - especially for public apps. Currently, the little space in the sidebar and main window quickly get filled with explanations and further links. I have written long markdown documents one can hide, but still, either people quickly uncheck the box to head for the box or get overwhelmed with details. Eventually, a good app is not used because of a cluttered layout, long scrolling and still insufficient clarity.
### Solution
**MVP:** A 2nd sidebar on the right side could be a nice designated place for such instructions, background information etc. It would save a lot of real estate in the other sections and still provide novice users with guidance without distracting frequent users. Moreover I consider it rather simple to copy the class from the existing sidebar - just as a right-bound counterpart.
**Possible additions:** Another option cold be an annotation overlay for all controls.
**Preferred solution:** I prefer the simple and straight forward solution
### Additional context
Thanks so much and many cheers.
Conrad | {
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**Description:**
- Add ```collapsible_container(label="", collapsed=False)```
- Create a base `_container_block` to be shared between `container` and `collapsible_container`
**Preview**
[Demo](https://collapsible.herokuapp.com/)
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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**Description:**
This fixes a regression where a single default value raised an exception. If the default value is in the set of options, create a list with it.
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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**Description:**
Following the spec and combining it with how we do deprecations. This commit:
* Modifies the docs to use the recommended local version
* Displays a deprecation error when using the global pyplot context.
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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] | closed | false | null | [] | null | 3 | 2020-09-07T09:05:45 | 2020-09-08T13:19:35 | 2020-09-08T10:24:22 | NONE | null | # Summary
When creating a dataframe which contains missing datetime elements they appear with a date via st.write(). Confusingly the date is Sep 21, 1677 10:17 AM.
# Steps to reproduce
```
import streamlit as st
import numpy as np
import pandas as pd
df= pd.DataFrame({'Date':pd.to_datetime(['2020-05-01', np.nan, '2019-08-01'])})
st.write(df)
```
## Expected behavior:
I expected that the missing date would be empty or be NaT
Example from a Jupyter Lab Notebook
## Actual behavior:
## Is this a regression?
Not Sure (only just started using Streamlit)
# Debug info
- Streamlit version: Streamlit, version 0.66.0
- Python version: Python 3.8.5
- Using Conda
- OS version: MacOS Catalina 10.15.6
- Browser version: Chrome Version 85.0.4183.83 (Official Build) (64-bit)
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} | [] | closed | false | null | [] | null | 1 | 2020-09-05T05:03:08 | 2020-09-08T14:30:41 | 2020-09-08T14:30:28 | NONE | null | AttributeError: module 'tensorflow' has no attribute 'GraphDef'
Traceback:
File "/home/ravi/.local/lib/python3.6/site-packages/streamlit/ScriptRunner.py", line 322, in _run_script
exec(code, module.__dict__)
File "/home/ravi/Documents/personal/vehicle_detection/app.py", line 291, in <module>
fun()
File "/home/ravi/Documents/personal/vehicle_detection/app.py", line 109, in fun
od_graph_def = tf.GraphDef() | {
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} | [] | closed | false | null | [] | null | 1 | 2020-09-04T20:01:24 | 2021-07-24T00:36:43 | 2020-09-04T22:15:06 | COLLABORATOR | null | Resolves [security vulnerability](https://github.com/advisories/GHSA-9v62-24cr-58cx)
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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} | [] | closed | false | null | [] | null | 1 | 2020-09-04T04:55:55 | 2020-09-04T13:42:53 | 2020-09-04T13:42:45 | NONE | null | Processing WebCamera Please Wait.... 🌔
st.write("Processing WebCamera Please Wait.... :moon:")
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] | closed | false | null | [] | null | 3 | 2020-09-04T03:31:00 | 2022-09-23T17:12:28 | 2022-09-23T17:12:28 | NONE | null | Pull request #1169 enabled control over query parameters, which is an amazing leap forward for this tool. The next step I'd like to see is control over the subdirectories in the URL. As I mentioned [here](https://github.com/streamlit/streamlit/issues/798#issuecomment-667370834), something like `myapp.com/benlindsay/dashboard` looks a little nicer than `myapp.com/?user=benlindsay&page=dashboard`.
I recognize that this is just an aesthetic change, since you can't functionally accomplish anything with subdirectories that you couldn't with query parameters (I don't think), so this should probably not be at the top of the priority list, but it would be great to see this eventually.
Some straw man api ideas:
## Idea 1: Separate functions for subdirectories and query parameters
This would introduce one new pair of functions and leave the `st.experimental_(get|set)_query_params()` as is:
```
import streamlit as st
subdirectories = st.experimental_get_url_subdirs()
params = {k: v[0] for k, v in st.experimental_get_query_params().items()}
print("subdirectories:", subdirectories)
print("params:", params)
st.experimental_set_url_subdirs(subdirectories)
st.experimental_set_query_params(**params)
```
If the URL was `myapp.com/benlindsay/dashboard/?mode=dark`, the print statements would look like
```
subdirectories: ['benlindsay', 'dashboard']
params: {'mode': 'dark'}
```
## Idea 2: Use a single function to control subdirectories and query parameters
```
import streamlit as st
subdirectories, params = st.experimental_get_url_parts()
params = {k: v[0] for k, v in params.items()}
print("subdirectories:", subdirectories)
print("params:", params)
st.experimental_set_url_parts(subdirectories, params)
```
Same print statements as above.
Curious what the feasibility of something like this would be and if people have other thoughts for what the API could look like. | {
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**Description:**
In attempting to fix an issue with Tensorflow, I realized that users can upgrade tensorflow to support later versions of Python 3.8+. I removed the requirement to see how CI checks this.
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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Description:
Multiselect box is slow on large datasets.
To solve this problem, we add a virtualized list to make the scrolling of options snappy
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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} | [] | closed | false | null | [] | null | 0 | 2020-09-03T21:12:03 | 2021-07-24T00:36:45 | 2020-10-08T23:44:03 | COLLABORATOR | null | **Issue:** https://github.com/streamlit/streamlit/issues/1804 #1294
**Description:**
Browsers are beginning to require Range Requests (notably Chrome and Safari) in order to display/set the start time effectively. This change fixes adds that functionality.
The Range Request is managed will in Tornado's `StaticFileHandler`, so I subclassed it and followed their convention for using the `MediaFileManager` vs an actual path.
We also remove the 5 second timer in favor of expiring files after a script completes running or when the session is disconnected.
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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] | closed | false | null | [] | null | 1 | 2020-09-03T19:00:34 | 2021-05-23T20:11:51 | 2020-09-03T20:00:16 | CONTRIBUTOR | null | No real reason to require a dev version.
See slack discussion:
https://streamlit.slack.com/archives/C017USJF285/p1599159096136000 | {
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] | closed | false | null | [] | null | 0 | 2020-09-02T21:16:34 | 2020-11-09T16:18:08 | 2020-11-09T16:18:08 | CONTRIBUTOR | null | Looks like there's some nuances with pipenv(?) that is causing us to have issues pinning down setuptools (#1944). In the meantime, we're reinstalling setuptools after the other installations (#1962).
Would be great if we could figure out what the nuance is and address that instead of force reinstalling. However, if we're able to unpin setuptools (#1945), this is no longer required.
Background:
Looks like the version is being upgraded from our `pipenv-test-install`.
Logs: https://app.circleci.com/pipelines/github/streamlit/streamlit/4302/workflows/8ff805dd-51d0-4344-9903-2b6e5d62f153/jobs/10615
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} | [] | closed | false | null | [] | null | 0 | 2020-09-02T17:58:46 | 2021-07-24T00:36:43 | 2020-09-02T22:19:51 | CONTRIBUTOR | null | **Issue:** CI is occasionally failing because in some instances it's still resolving to setuptools v50.0.3.
**Description:**
1. Pin setuptools in requirements.txt (this still looks flakey?)
2. ☝️ seems flakey still so let's force a reinstall of pinned setuptools after just in case.
---
**Contribution License Agreement**
By submitting this pull request you agree that all contributions to this project are made under the Apache 2.0 license.
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] | closed | false | null | [] | null | 4 | 2020-09-02T10:10:55 | 2022-09-23T17:25:54 | 2022-09-23T17:25:54 | NONE | null | Hi,
Using this solution [SessionState]( https://gist.github.com/tvst/036da038ab3e999a64497f42de966a92) and storing each session a dict with session_info.session.id as key, I'm facing some crosstalk problem between user's sessions.
I believe this problem is happening because I always retrieve the session_info.session.id from the current session, it means I all session always retrieve the id from the last created session by last access.
What is the best way to become the session_info.session.id persistent for each session?
I tried to use the st.experimental_set_query_params(), but it probably uses the url from the session too, because after a new user access I'm not able to retrieve this info using st.experimental_get_query_params(). I also tried to store this session_info.session.id in a browser cookie or sessionStorage, using components.html, but I the allow-same-origin sandbox permission is needed for that.
Any suggestion in a way to get a persistent unique id for each user session?
Thanks
PS: I'm using streamlit 0.65.2
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There are many javascript library available, especially for Graph : highcharts,....
Having a generic interface wrapper to access those javascript, would be useful,
and enhance streamlit by 100x by leveraring Jvascript community...
Not sure of the actual implementation, but something like this :
myjson_config --> PythonObject --> create javascript object dynamically --> get the data.
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Input:
```
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="100" height="100">
<clipPath id="clipCircle">
<circle r="25" cx="25" cy="25"/>
</clipPath>
<image href="https://avatars.githubusercontent.com/karriebear" width="50" height="50" clip-path="url(#clipCircle)"/>
</svg>
```
Output:
Expected:
## Example 2:
Input
```
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="500" height="100">
<text x="0" y="50">"I am a quote" - https://avatars.githubusercontent.com/karriebear</text>
</svg>
```
Expected:
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} | [] | closed | false | null | [] | null | 0 | 2020-09-01T23:46:07 | 2021-07-24T00:36:42 | 2020-09-02T16:33:31 | COLLABORATOR | null | Unit tests for `ComponentInstance.tsx`
These involve slightly complex plumbing, because they're testing the communication between `ComponentInstance` and its iframe, which all happens via `postMessage`.
This PR supersedes #1689 - that PR can be closed. | {
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