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https://github.com/sahilkumardhala/streamlit-learning

Streamlit is an open-source app framework designed for creating and deploying data-driven web applications quickly and easily, using only Python scripts. It allows developers to transform data scripts into interactive and beautiful apps without the need for front-end experience.
https://github.com/sahilkumardhala/streamlit-learning

python3 streamlit

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Streamlit is an open-source app framework designed for creating and deploying data-driven web applications quickly and easily, using only Python scripts. It allows developers to transform data scripts into interactive and beautiful apps without the need for front-end experience.

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README 

          
# Streamlit-LEARNING

![Streamlit-LEARNING-iamge](streamlitlogo.png)



[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://demo-st-learning-sahil.streamlit.app/)



# Abouts:



Streamlit is an open-source app framework designed for creating and deploying data-driven web applications quickly and easily, using only Python scripts. It allows developers to transform data scripts into interactive and beautiful apps without the need for front-end experience. To start using Streamlit, you first need to install it via pip with `pip install streamlit`. Once installed, you can run a Streamlit app by executing the command `streamlit run your_script.py`. Streamlit offers a range of commands and widgets that make building interactive applications straightforward. Some of the essential commands include `st.title()` for setting the app title, `st.header()` and `st.subheader()` for different levels of headings, and `st.text()` for adding simple text. For displaying data, you can use `st.write()`, which automatically formats your data depending on its type, or `st.dataframe()` for interactive tables. You can also create various input widgets like `st.button()`, `st.slider()`, `st.selectbox()`, and `st.text_input()` to capture user input. Additionally, Streamlit supports charting libraries like Matplotlib, Plotly, and Altair through commands such as `st.pyplot()` and `st.plotly_chart()`. Once your app is ready, you can share it with others by deploying it to a web server, or using `Streamlit Cloud` for hosting. Streamlit’s simplicity and versatility make it an excellent choice for data scientists and engineers looking to create web applications efficiently.



Author:

* @sahilkumardhala : https://github.com/sahilkumardhala/sahilkumardhala



# Deployments

[Streamlit-LEARNING - Sharing for Streamlit](https://demo-st-learning-sahil.streamlit.app/)



# Show me

![Streamlit-LEARNING-iamge](demo.jpg)



---



# All content



## All commands



```python

# All Magic commands simplicitly `st.write()`

''' _This_ is some __Markdown__ '''

a=3

'dataframe:', data

```



## Display text



```python

st.text('Fixed width text')

st.markdown('_Markdown_') # see #*

st.caption('Balloons. Hundreds of them...')

st.latex(r\'\'\' e^{i\pi} + 1 = 0 \'\'\')

st.write('Most objects') # df, err, func, keras!

st.write(['st', 'is <', 3]) # see *

st.title('My title')

st.header('My header')

st.subheader('My sub')

st.code('for i in range(8): foo()')



# * optional kwarg unsafe_allow_html = True

```



## Display data



```python

st.dataframe(my_dataframe)

st.table(data.iloc[0:10])

st.json({'foo':'bar','fu':'ba'})

st.metric(label="Temp", value="273 K", delta="1.2 K")

```



## Display media



```python

st.image('./header.png')

st.audio(data)

st.video(data)

```



## Columns



```python

col1, col2 = st.columns(2)

col1.write('Column 1')

col2.write('Column 2')



# Three columns with different widths

col1, col2, col3 = st.columns([3,1,1])

# col1 is wider

              

# Using 'with' notation:

>>> with col1:

>>>     st.write('This is column 1')

```



## Tabs



```python

# Insert containers separated into tabs:

>>> tab1, tab2 = st.tabs(["Tab 1", "Tab2"])

>>> tab1.write("this is tab 1")

>>> tab2.write("this is tab 2")



# You can also use "with" notation:

>>> with tab1:

>>>   st.radio('Select one:', [1, 2])

```



## Control flow

```python

# Stop execution immediately:

st.stop()

# Rerun script immediately:

st.experimental_rerun()



# Group multiple widgets:

>>> with st.form(key='my_form'):

>>>   username = st.text_input('Username')

>>>   password = st.text_input('Password')

>>>   st.form_submit_button('Login')

```



## Personalize apps for users



```python

# Show different content based on the user's email address.

>>> if st.user.email == 'jane@email.com':

>>>    display_jane_content()

>>> elif st.user.email == 'adam@foocorp.io':

>>>    display_adam_content()

>>> else:

>>>    st.write("Please contact us to get access!")

```



## Display interactive widgets



```python

st.button('Hit me')

st.data_editor('Edit data', data)

st.checkbox('Check me out')

st.radio('Pick one:', ['nose','ear'])

st.selectbox('Select', [1,2,3])

st.multiselect('Multiselect', [1,2,3])

st.slider('Slide me', min_value=0, max_value=10)

st.select_slider('Slide to select', options=[1,'2'])

st.text_input('Enter some text')

st.number_input('Enter a number')

st.text_area('Area for textual entry')

st.date_input('Date input')

st.time_input('Time entry')

st.file_uploader('File uploader')

st.download_button('On the dl', data)

st.camera_input("一二三,茄子!")

st.color_picker('Pick a color')



# Use widgets\' returned values in variables

>>> for i in range(int(st.number_input('Num:'))): foo()

>>> if st.sidebar.selectbox('I:',['f']) == 'f': b()

>>> my_slider_val = st.slider('Quinn Mallory', 1, 88)

>>> st.write(slider_val)



# Disable widgets to remove interactivity:

>>> st.slider('Pick a number', 0, 100, disabled=True)

```



## Build chat-based apps



```python

# Insert a chat message container.

>>> with st.chat_message("user"):

>>>    st.write("Hello 👋")

>>>    st.line_chart(np.random.randn(30, 3))



# Display a chat input widget.

>>> st.chat_input("Say something")  

```



## Mutate data



```python

# Add rows to a dataframe after showing it.

>>> element = st.dataframe(df1)

>>> element.add_rows(df2)



# Add rows to a chart after showing it.

>>> element = st.line_chart(df1)

>>> element.add_rows(df2)

```



## Display code



```python

st.echo()

>>> with st.echo():

>>>     st.write('Code will be executed and printed')

```



## Placeholders, help, and options



```python

# Replace any single element.

>>> element = st.empty()

>>> element.line_chart(...)

>>> element.text_input(...)  # Replaces previous.



# Insert out of order.

>>> elements = st.container()

>>> elements.line_chart(...)

>>> st.write("Hello")

>>> elements.text_input(...)  # Appears above "Hello".



st.help(pandas.DataFrame)

st.get_option(key)

st.set_option(key, value)

st.set_page_config(layout='wide')

st.experimental_show(objects)

st.experimental_get_query_params()

st.experimental_set_query_params(**params)

```



## Connect to data sources



```python

st.experimental_connection('pets_db', type='sql')

conn = st.experimental_connection('sql')

conn = st.experimental_connection('snowpark')



>>> class MyConnection(ExperimentalBaseConnection[myconn.MyConnection]):

>>>    def _connect(self, **kwargs) -> MyConnection:

>>>        return myconn.connect(**self._secrets, **kwargs)

>>>    def query(self, query):

>>>       return self._instance.query(query)

```



## Optimize performance



### Cache data objects



```python

# E.g. Dataframe computation, storing downloaded data, etc.

>>> @st.cache_data

... def foo(bar):

...   # Do something expensive and return data

...   return data

# Executes foo

>>> d1 = foo(ref1)

# Does not execute foo

# Returns cached item by value, d1 == d2

>>> d2 = foo(ref1)

# Different arg, so function foo executes

>>> d3 = foo(ref2)

# Clear all cached entries for this function

>>> foo.clear()

# Clear values from *all* in-memory or on-disk cached functions

>>> st.cache_data.clear()

```



### Cache global resources



```python

# E.g. TensorFlow session, database connection, etc.

>>> @st.cache_resource

... def foo(bar):

...   # Create and return a non-data object

...   return session

# Executes foo

>>> s1 = foo(ref1)

# Does not execute foo

# Returns cached item by reference, s1 == s2

>>> s2 = foo(ref1)

# Different arg, so function foo executes

>>> s3 = foo(ref2)

# Clear all cached entries for this function

>>> foo.clear()

# Clear all global resources from cache

>>> st.cache_resource.clear()

```



### Deprecated caching



```python

>>> @st.cache

... def foo(bar):

...   # Do something expensive in here...

...   return data

>>> # Executes foo

>>> d1 = foo(ref1)

>>> # Does not execute foo

>>> # Returns cached item by reference, d1 == d2

>>> d2 = foo(ref1)

>>> # Different arg, so function foo executes

>>> d3 = foo(ref2)

```



## Display progress and status



```python

# Show a spinner during a process

>>> with st.spinner(text='In progress'):

>>>   time.sleep(3)

>>>   st.success('Done')



# Show and update progress bar

>>> bar = st.progress(50)

>>> time.sleep(3)

>>> bar.progress(100)



st.balloons()

st.snow()

st.toast('Mr Stay-Puft')

st.error('Error message')

st.warning('Warning message')

st.info('Info message')

st.success('Success message')

st.exception(e)

```



### Other key parts of the API

[State API](https://docs.streamlit.io/en/stable/session_state_api.html)


[Theme option reference](https://docs.streamlit.io/en/stable/theme_options.html)


[Components API reference](https://docs.streamlit.io/en/stable/develop_streamlit_components.html)



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