example

Hello.py

@@ -0,0 +1,10 @@
+import streamlit as st
+
+
+st.set_page_config(
+    page_title="Hello",
+    page_icon="👋",
+)
+
+st.sidebar.success("Select a Page")
+

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,14 +1,44 @@
 ---
-title: ShadisClassExample
-emoji: 👁
-colorFrom: purple
-colorTo: yellow
+title: Streamlit Template (Shadi's Class)
+emoji: 🏢
+colorFrom: blue
+colorTo: gray
 sdk: streamlit
 sdk_version: 1.39.0
-app_file: app.py
+app_file: Hello.py
 pinned: false
-license: apache-2.0
-short_description: this is a template for use in Prof. Rezapour's class
+license: mit
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Install what you need for this notebook
+
+It is recommended you install into a conda environment:
+
+```
+conda create -n DataVizClass python=3.10
+conda activate DataVizClass
+```
+
+Then you can install the correct packages.  If you have downloaded the requirements document you can install directly with:
+
+```
+pip install -r /path/to/requirements.txt
+```
+
+Or you can install one by one with:
+
+```
+pip install streamlit==1.39.0 altair numpy pandas matplotlib
+```
+
+To work with the VSCode interface, be sure that `jupyter` is also installed:
+
+```
+pip install jupyter
+```
+ 
+Or you can install with `conda`.
+
+Note that the package [Streamlit](https://streamlit.io/) that we will be working with requires we use Python scripts, so JupyterLab and/or Jupyter Notebooks won't work for this process.

README.md~

@@ -0,0 +1,23 @@
+---
+title: Prep notebook -- My Multi-page Streamlit App (Day 2)
+emoji: 🏢
+colorFrom: blue
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.39.0
+app_file: Hello.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Info:
+This is the "prep" notebook for a introductory Streamlit app for IS445.
+
+## How to teach with this:
+We will build this "from scratch" during class using the following steps:
+1. Make sure your repo is cloned in your PraireLearn (this will be "is445_demo2" with my setup, "is445_demo" for each of the students)
+1. Update the README.md if the app.py file has changed names
+1. Copy what we did from last week to the app.py file here
+1. Update the requirements.txt file to include libraries (see whats in here now)

app.py

@@ -0,0 +1,432 @@
+
+#######################################################
+# 1. Getting setup -- using our HF template
+#######################################################
+
+# We have a few options for how to proceed.  I'll start by showing the process in 
+#   PL and then I'll move to my local installation of my template so that I can make 
+#   sure I am pushing code at various intervals so folks can check that out.
+
+# NOTE: during this process, you can click on "Always Rerun" for automatic updates.
+
+# See the class notes on this with some photos for reference!
+# **this has to be implemented!**
+
+
+###################################################################
+# 2. Review of where we got to last time, in template app.py file
+###################################################################
+
+
+# Let's start by copying things we did last time
+import streamlit as st
+import altair as alt
+
+# Let's recall a plot that we made with Altair in Jupyterlab:
+#    Make sure we copy the URL as well!
+mobility_url = 'https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv'
+
+st.title('This is my fancy app for HuggingFace!!')
+
+scatters = alt.Chart(mobility_url).mark_point().encode(
+    x='Mobility:Q', # "Q for quantiative"
+    #y='Population:Q',
+    y=alt.Y('Population:Q', scale=alt.Scale(type='log')),
+    color=alt.Color('Income:Q', scale=alt.Scale(scheme='sinebow'),bin=alt.Bin(maxbins=5))
+)
+
+st.header('More complex Dashboards')
+
+brush = alt.selection_interval(encodings=['x','y'])
+
+chart1 = alt.Chart(mobility_url).mark_rect().encode(
+    alt.X("Student_teacher_ratio:Q", bin=alt.Bin(maxbins=10)),
+    alt.Y("State:O"),
+    alt.Color("count()")
+).properties(
+   height=400
+).add_params(
+        brush
+)
+
+chart2 = alt.Chart(mobility_url).mark_bar().encode(
+    alt.X("Mobility:Q", bin=True,axis=alt.Axis(title='Mobility Score')),
+    alt.Y('count()', axis=alt.Axis(title='Mobility Score Distribution'))
+).transform_filter(
+    brush
+)
+
+chart = (chart1.properties(width=300) | chart2.properties(width=300))
+
+tab1, tab2 = st.tabs(["Mobility interactive", "Scatter plot"])
+
+with tab1:
+    st.altair_chart(chart, theme=None, use_container_width=True)
+with tab2:
+    st.altair_chart(scatters, theme=None, use_container_width=True)
+
+
+################################################
+# 3. Adding features, Pushing to HF
+################################################
+
+st.header('Requirements, README file, Pushing to HuggingFace')
+
+### 3.1 Make a plot ###
+
+# Let's say we want to add in some matplotlib plots from some data we read
+#  in with Pandas.
+
+import pandas as pd
+df = pd.read_csv(mobility_url)
+
+# There are a few ways to show the dataframe if we want our viewer to see the table:
+#df
+st.write(df)
+
+# Now, let's plot with matplotlib:
+import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots()
+df['Seg_income'].plot(kind='hist', ax=ax)
+#plt.show() # but wait! this doesn't work!  
+
+# We need to use the streamlit-specific way of showing matplotlib plots: https://docs.streamlit.io/develop/api-reference/charts/st.pyplot
+st.pyplot(fig)
+
+### 3.2 Push these changes to HF -- requirements.txt ###
+# In order to push these changes to HF and have things actually show up we need to 
+#   add the packages we've added to our requirements.txt file.
+
+st.write('''The requirements.txt file contains all the packages needed 
+         for our app to run.  These include (for our application):''')
+st.code('''
+streamlit==1.39.0
+altair
+numpy
+pandas
+matplotlib
+            ''')
+
+# NOTE: for any package you want to use in your app.py file, you must include it in 
+#   the requirements.txt file!
+
+# Note #2: we specified a version of streamlit so we can use some specific widgets
+
+### 3.3 Push these changes to HF -- README.md ###
+
+# While we're doing this, let's also take a look at the README.md file!
+
+st.header('Build in HF: README.md & requirements.txt files')
+
+st.code('''
+---
+title: Prep notebook -- My Streamlit App     
+emoji: 🏢
+colorFrom: blue   
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.39.0
+app_file: app.py
+pinned: false
+license: mit
+---
+''')
+st.write("Note: the sdk version has to match what is in your requirements.txt (and with whatever widgets you want to be able to use).")
+
+# Some important things to note here:
+
+st.write('Some important items to note about these:')
+st.markdown('''
+* the "emoji" is what will show up as an identifier on your homepage
+* the sdk *must* be streamlit
+* the "app_file" *must* link to the app file you are developing in
+            ''')
+
+################################################
+# 4. TODO Quick intro to widgets
+################################################
+
+st.header('Widgets in Streamlit apps')
+
+### 4.1 Widget basics: A few widget examples ###
+
+st.markdown("""
+These will be very similar to how we used the `ipywidgets` package in Jupyter notebooks.
+         """)
+
+st.markdown("""
+We won't go over all of them, but you can check out the [list of widgets](https://docs.streamlit.io/develop/api-reference/widgets) 
+            linked.
+""")
+
+st.markdown("""Let's try a few!""")
+
+st.subheader('Feedback Widget')
+
+st.markdown("""
+For example, we could try the [feedback widget](https://docs.streamlit.io/develop/api-reference/widgets/st.feedback).
+            """
+)
+st.markdown("""            
+            If we check out the docs for this widget, we see some familiar looking functions like 
+            `on_change` and the example they give looks very similar to an 
+            "observation" function that we built before using widgets:
+             """)
+
+st.code(
+"""
+sentiment_mapping = ["one", "two", "three", "four", "five"]
+selected = st.feedback("stars")
+if selected is not None:
+    st.markdown(f"You selected {sentiment_mapping[selected]} star(s).")
+""")
+
+# Let's give this a shot!
+
+st.write("How great are you feeling right now?")
+sentiment_mapping = ["one", "two", "three", "four", "five"] # map to these numers
+selected = st.feedback("stars")
+if selected is not None: # make sure we have a selection
+    st.markdown(f"You selected {sentiment_mapping[selected]} star(s).")
+    if selected < 1:
+        st.markdown('Sorry to hear you are so sad :(')
+    elif selected < 3:
+        st.markdown('A solid medium is great!')
+    else:
+        st.markdown('Fantastic you are having such a great day!')
+
+st.subheader('Radio Buttons')
+
+st.markdown("""
+Let's try out a [radio button](https://docs.streamlit.io/develop/api-reference/widgets/st.radio) example.
+""")
+
+favoriteViz = st.radio(
+    "What's your visualization tool so far?",
+    [":rainbow[Streamlit]", "vega-lite :sparkles:", "matplotlib :material/Home:"],
+    captions=[
+        "New and cool!",
+        "So sparkly.",
+        "Familiar and comforting.",
+    ],
+)
+
+if favoriteViz == ":rainbow[Streamlit]":
+    st.write("You selected Streamlit!")
+else:
+    st.write("You didn't select Streamlit but that's ok, Data Viz still likes you :grin:")
+
+st.markdown("""
+Note here that we made use of text highlight [colors](https://docs.streamlit.io/develop/api-reference/text/st.markdown) 
+            and [emoji's](https://streamlit-emoji-shortcodes-streamlit-app-gwckff.streamlit.app/) 
+            and [icons](https://fonts.google.com/icons?icon.set=Material+Symbols&icon.style=Rounded).
+ """)
+
+### 4.2 Connecting widgets with plots ###
+
+st.subheader('Connecting Widgets and Plots')
+
+
+st.markdown("""
+There are actually [many types of charts](https://docs.streamlit.io/develop/api-reference/charts) 
+            supported in Streamlit (including the Streamlit-based "Simple Charts"), 
+            though we will just mainly be focusing on [Altair-related](https://docs.streamlit.io/develop/api-reference/charts/st.altair_chart) plots 
+            and their interactivity options since we'll also be making use of these when 
+            we move to building Jekyll webpages.
+ """)
+
+st.markdown("""Since `matplotlib` is relatively familiar though, let's do a quick 
+            example using `pandas` and `matplotlib` to plot as 
+            Streamlit [does support `matplotlib`](https://docs.streamlit.io/develop/api-reference/charts/st.pyplot) 
+            as a plotting engine. """)
+
+st.markdown("""First, let's just make a simple plot with `pandas` and `matplotlib`. 
+            Let's re-do the matplotlib plots we did before with the mobility dataset 
+            with some interactivity. """)
+
+import pandas as pd
+import numpy as np
+
+# first, let's make a static plot:
+st.write("We'll start with a static plot:")
+# read in dataset
+df = pd.read_csv("https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv")
+
+# make bins along student-teacher ratio
+bins = np.linspace(df['Student_teacher_ratio'].min(),df['Student_teacher_ratio'].max(), 10)
+
+# make pivot table
+table = df.pivot_table(index='State', columns=pd.cut(df['Student_teacher_ratio'], bins), aggfunc='size')
+
+# our plotting code before was:
+st.code("""
+import matplotlib.pyplot as plt
+
+fig,ax = plt.subplots(figsize=(10,8))
+ax.imshow(table.values, cmap='hot', interpolation='nearest')
+ax.set_yticks(range(len(table.index)))
+ax.set_yticklabels(table.index)
+plt.show()
+ """)
+
+st.write("Let's translate it into something that will work with Streamlit:")
+
+import matplotlib.pyplot as plt
+
+fig,ax = plt.subplots() # this changed
+ax.imshow(table.values, cmap='hot', interpolation='nearest')
+ax.set_yticks(range(len(table.index)))
+ax.set_yticklabels(table.index)
+
+st.pyplot(fig) # this is different
+
+st.markdown("""But this is too big!  The trick is that we can save this as a buffer: """)
+
+from io import BytesIO
+
+fig,ax = plt.subplots(figsize=(4,8)) # this changed
+ax.imshow(table.values, cmap='hot', interpolation='nearest')
+ax.set_yticks(range(len(table.index)))
+ax.set_yticklabels(table.index)
+
+buf = BytesIO()
+fig.tight_layout()
+fig.savefig(buf, format="png")
+st.image(buf, width = 500) # can mess around with width, figsize/etc
+
+st.write("Now, let's make this interactive.")
+st.markdown("""We'll first use the [multiselect](https://docs.streamlit.io/develop/api-reference/widgets/st.multiselect) 
+            tool in order to allow for multiple state selection. """)
+
+# vertical alignment so they end up side by side
+fig_col, controls_col = st.columns([2,1], vertical_alignment='center')
+
+# multi-select
+states_selected = controls_col.multiselect('Which states do you want to view?', table.index.values)
+
+if len(states_selected) > 0:
+    df_subset = df[df['State'].isin(states_selected)] # changed
+
+    # make pivot table -- changed
+    table_sub = df_subset.pivot_table(index='State', 
+                                  columns=pd.cut(df_subset['Student_teacher_ratio'], bins), 
+                                  aggfunc='size')
+
+    base_size = 4
+    # this resizing doesn't 100% work great
+    #factor = len(table.index)*1.0/df['State'].nunique()
+    #if factor == 0: factor = 1 # for non-selections
+    #fig,ax = plt.subplots(figsize=(base_size,2*base_size*factor)) # this changed too for different size
+    fig,ax = plt.subplots(figsize=(base_size,2*base_size)) # this changed too for different size
+    # extent is (xmin, xmax, ymax (buttom), ymin (top))
+    extent = [bins.min(), bins.max(), 0, len(table_sub.index)]
+    ax.imshow(table_sub.values, cmap='hot', interpolation='nearest', 
+              extent=extent)
+    ax.set_yticks(range(len(table_sub.index)))
+    ax.set_yticklabels(table_sub.index)
+    #ax.set_xticklabels(bins)
+
+    buf = BytesIO()
+    fig.tight_layout()
+    fig.savefig(buf, format="png")
+    fig_col.image(buf, width = 400) # changed here to fit better
+else:
+    fig,ax = plt.subplots(figsize=(4,8)) # this changed
+    extent = [bins.min(), bins.max(), 0, len(table.index)]
+    ax.imshow(table.values, cmap='hot', interpolation='nearest', extent=extent)
+    ax.set_yticks(range(len(table.index)))
+    ax.set_yticklabels(table.index)
+    #ax.set_xticklabels(bins)
+
+    buf = BytesIO()
+    fig.tight_layout()
+    fig.savefig(buf, format="png")
+    fig_col.image(buf, width = 500) # can mess around with width, figsize/etc
+
+
+st.markdown(""" 
+Now let's add more in by including a [range slider](https://docs.streamlit.io/develop/api-reference/widgets/st.slider) 
+            widget.
+""")
+
+# vertical alignment so they end up side by side
+fig_col2, controls_col2 = st.columns([2,1], vertical_alignment='center')
+
+# multi-select
+states_selected2 = controls_col2.multiselect('Which states do you want to view?', 
+                                             table.index.values, key='unik1155') 
+#                                            had to pass unique key to have double widgets with same value
+
+# range slider -- added
+student_teacher_ratio_range = controls_col2.slider("Range of student teacher ratio:", 
+                                                   df['Student_teacher_ratio'].min(), 
+                                                   df['Student_teacher_ratio'].max(), 
+                                                   (0.25*df['Student_teacher_ratio'].mean(), 
+                                                    0.75*df['Student_teacher_ratio'].mean()))
+
+# note all the "2's" here, probably will just update the original one
+if len(states_selected2) > 0: # here we set a default value for the slider, so no need to have a tag
+    min_range = student_teacher_ratio_range[0] # added
+    max_range = student_teacher_ratio_range[1] # added
+
+    df_subset2 = df[(df['State'].isin(states_selected2)) & (df['Student_teacher_ratio'] >= min_range) & (df['Student_teacher_ratio']<=max_range)] # changed
+
+    # just 10 bins over the full range --> changed
+    bins2 = 10 #np.linspace(df['Student_teacher_ratio'].min(),df['Student_teacher_ratio'].max(), 10)
+
+    # make pivot table -- changed
+    table_sub2 = df_subset2.pivot_table(index='State', 
+                                  columns=pd.cut(df_subset2['Student_teacher_ratio'], bins2), 
+                                  aggfunc='size')
+
+    base_size = 4
+    fig2,ax2 = plt.subplots(figsize=(base_size,2*base_size)) # this changed too for different size
+    extent2 = [df_subset2['Student_teacher_ratio'].min(), 
+               df_subset2['Student_teacher_ratio'].max(), 
+               0, len(table_sub2.index)]
+    ax2.imshow(table_sub2.values, cmap='hot', interpolation='nearest', extent=extent2)
+    ax2.set_yticks(range(len(table_sub2.index)))
+    ax2.set_yticklabels(table_sub2.index)
+    #ax2.set_xticklabels()
+
+    buf2 = BytesIO()
+    fig2.tight_layout()
+    fig2.savefig(buf2, format="png")
+    fig_col2.image(buf2, width = 400) # changed here to fit better
+else:
+    min_range = student_teacher_ratio_range[0] # added
+    max_range = student_teacher_ratio_range[1] # added
+
+    df_subset2 = df[(df['Student_teacher_ratio'] >= min_range) & (df['Student_teacher_ratio']<=max_range)] # changed
+
+    # just 10 bins over the full range --> changed
+    bins2 = 10 #np.linspace(df['Student_teacher_ratio'].min(),df['Student_teacher_ratio'].max(), 10)
+
+    # make pivot table -- changed
+    table_sub2 = df_subset2.pivot_table(index='State', 
+                                  columns=pd.cut(df_subset2['Student_teacher_ratio'], bins2), 
+                                  aggfunc='size')
+
+    base_size = 4
+    fig2,ax2 = plt.subplots(figsize=(base_size,2*base_size)) # this changed too for different size
+    extent2 = [df_subset2['Student_teacher_ratio'].min(), 
+               df_subset2['Student_teacher_ratio'].max(), 
+               0, len(table_sub2.index)]
+    ax2.imshow(table_sub2.values, cmap='hot', interpolation='nearest', extent=extent2)
+    ax2.set_yticks(range(len(table_sub2.index)))
+    ax2.set_yticklabels(table_sub2.index)
+    #ax2.set_xticklabels()
+
+    buf2 = BytesIO()
+    fig2.tight_layout()
+    fig2.savefig(buf2, format="png")
+    fig_col2.image(buf2, width = 400) # changed here to fit better
+
+st.header('Push final page to HF')
+st.markdown("""When ready, do:""")
+st.code("""
+git add -A
+git commit -m "final push of day 1"
+git push
+ """)

pages/1_Data_Introduction.py

@@ -0,0 +1,5 @@
+import streamlit as st
+
+st.set_page_config(page_title="Introduction to Data", page_icon=":1234:") # not sure what this adds?
+st.sidebar.header("Introduction to Data")
+

pages/2_Widget_Exploration.py

@@ -0,0 +1,5 @@
+import streamlit as st
+
+st.set_page_config(page_title="Widget Exploration", page_icon=":1234:")
+st.sidebar.header("Widget Exploration")
+

pages/3_Other_Tools.py

@@ -0,0 +1,12 @@
+import streamlit as st
+
+st.set_page_config(page_title="Other Tools", page_icon=":1234:")
+st.sidebar.header("Other Tools")
+
+st.title("Other cool things to check out")
+
+st.markdown(""" While we won't have time to cover everything, a few things you might want to check out later:
+1. You can connect databases to Streamlit [like AWS, MongoDB, etc](https://docs.streamlit.io/develop/tutorials/databases)
+1. You can embed Streamlit Spaces [within other webpages](https://huggingface.co/docs/hub/en/spaces-sdks-streamlit#embed-streamlit-spaces-on-other-webpages)
+1. If you have models hosted on HuggingFace, you can build [apps that use those models](https://huggingface.co/blog/streamlit-spaces) (like LLMs) and let others use them.  
+ """)

requirements.txt

@@ -0,0 +1,5 @@
+streamlit==1.39.0
+altair
+numpy
+pandas
+matplotlib