words

Hello.py

@@ -8,3 +8,88 @@ st.set_page_config(
 
 st.sidebar.success("Select a Page")
 
+st.title('Getting Setup for Streamlit Spaces')
+
+st.markdown("""
+## Step 1: 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.  
+
+```
+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. """)
+
+st.markdown("""
+## Step 2: Clone the App files -- Option 1 (HuggingFace)
+""")
+
+st.markdown("""
+### Step 2.1: Duplicate the HuggingFace App
+ """)
+
+st.markdown("""
+If you want to be able to deploy your own app on your own HuggingFace account, 
+you first need to duplicate this space.            
+
+To duplicate, right click on the 3 dots to the left of your profile icon: """)
+
+st.image("images/duplicateSpace_p1.png")
+
+st.markdown("""
+Make sure your user name is selected, and it is set to `Public` for visibility.  Name the space whatever you want:
+ """)
+
+st.image("images/duplicateSpace_p2.png")
+
+st.markdown("""
+### Step 2.2: Clone *your duplicated* Space
+ """)
+
+st.markdown("""
+First, be sure that `git-lfs` is installed, and then clone *your* duplicated space repo.
+            
+You can find instructions for this by once again right-clicking the 3 dots and clicking on "Clone Repository":
+ """)
+
+st.image("images/clone_the_repo.png")
+
+st.markdown("""
+Then you will be given this set of instructions (note your repo name will be different!):
+ """)
+
+st.image("images/gitclone_hf.png")
+
+st.markdown("""
+ """)
+
+st.markdown("""
+Now you should have a repository folder on your local computer that has your repo name and the files for this app.
+ """)
+
+st.markdown("""
+## Step 2: Clone the App files -- Option 2 (GitHub)
+""")
+
+st.markdown("""
+If you don't want to mess around with HuggingFace and just want to play around with Streamlit locally, 
+you can also download these files from GitHub by [cloning this repository right here](https://github.com/jnaiman/shadisClassApp).
+            
+For info about using git commands to clone a repository, [see this link right here](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).
+ """)

app.py

@@ -1,432 +0,0 @@
-
-#######################################################
-# 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
- """)