readme

README.md

@@ -1,5 +1,5 @@
 ---
-title: My IS445 Example ID13111
+title: Prep notebook -- My Streamlit App
 emoji: 🏢
 colorFrom: blue
 colorTo: gray
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 ---
 
 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

@@ -1,73 +1,104 @@
-# INSTRUCTIONS:
-# 1. Open a "Terminal" by: View --> Terminal OR just the "Terminal" through the hamburger menu
-# 2. run in terminal with: streamlit run app.py
-# 3. click the "Open in Browser" link that pops up OR click on "Ports" and copy the URL
-# 4. Open a Simple Browswer with View --> Command Palette --> Simple Browser: Show
-# 5. use the URL from prior steps as intput into this simple browser
 
 
+
+# day 2/3 -- "grab bag" of other things
+# touch on widgets! -- do quick example of plot + widget, say we'll focus on Altair
+# multi-page apps? ==> maybe day 2? ==> does this work with HF apps??
+# matplotlib plots
+# other plotting tools in ST (like the defaults) + widgets
+#* https://docs.streamlit.io/develop/tutorials/databases <- touch on but say we'll be just doing csv files
+# embedding streamlit spaces on other webpages? wait until Jekyll? https://huggingface.co/docs/hub/en/spaces-sdks-streamlit#embed-streamlit-spaces-on-other-webpages
+# how to search/duplicate other spaces on HF (make sure you cite this!)
+
+# start with "this is how we publish with streamlit" -- README file, requirements, etc
+# ---> make sure to mention the "yaml-ness" of the README file
+# ---> say that the easiest way to start is with an already hosted app on HF -- luckily we alread have a lab on this!
+# ---> make this like the "jekyll updates" folders that have all these prep and in class files in them
+# Then: more streamlit extras with all of those ones listed above
+
+################################################
+# 1. Review of where we got to last time
+################################################
+
+
+# Let's start by copying things we did last time
 import streamlit as st
 import altair as alt
-from vega_datasets import data
- 
-st.title('Streamlit App for IS445: ID46435')
 
-st.text("The URL for this app is: https://huggingface.co/spaces/jnaiman/my_test_demo")
+# 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'
 
-source = data.seattle_weather()
+st.title('This is my fancy app for HuggingFace!!')
 
-scale = alt.Scale(
-    domain=["sun", "fog", "drizzle", "rain", "snow"],
-    range=["#e7ba52", "#a7a7a7", "#aec7e8", "#1f77b4", "#9467bd"],
+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))
 )
-color = alt.Color("weather:N", scale=scale)
-
-# We create two selections:
-# - a brush that is active on the top panel
-# - a multi-click that is active on the bottom panel
-brush = alt.selection_interval(encodings=["x"])
-click = alt.selection_point(encodings=["color"])
-
-# Top panel is scatter plot of temperature vs time
-points = (
-    alt.Chart()
-    .mark_point()
-    .encode(
-        alt.X("monthdate(date):T", title="Date (Month Year)"),
-        alt.Y(
-            "temp_max:Q",
-            title="Maximum Daily Temperature (C)",
-            scale=alt.Scale(domain=[-5, 40]),
-        ),
-        color=alt.condition(brush, color, alt.value("lightgray")),
-        size=alt.Size("precipitation:Q", scale=alt.Scale(range=[5, 200])),
-    )
-    .properties(width=550, height=300)
-    .add_params(brush)
-    .transform_filter(click)
+
+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
 )
 
-# Bottom panel is a bar chart of weather type
-bars = (
-    alt.Chart()
-    .mark_bar()
-    .encode(
-        x="count()",
-        y="weather:N",
-        color=alt.condition(click, color, alt.value("lightgray")),
-    )
-    .transform_filter(brush)
-    .properties(
-        width=550,
-    )
-    .add_params(click)
+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 = alt.vconcat(points, bars, data=source, title="Seattle Weather - 2007 to 2013")
+chart = (chart1.properties(width=300) | chart2.properties(width=300))
 
-tab1, tab2 = st.tabs(["Streamlit theme (default)", "Altair native theme"])
+tab1, tab2 = st.tabs(["Mobility interactive", "Scatter plot"])
 
 with tab1:
-    st.altair_chart(chart, theme="streamlit", use_container_width=True)
+    st.altair_chart(chart, theme=None, use_container_width=True)
 with tab2:
-    st.altair_chart(chart, theme=None, use_container_width=True)
+    st.altair_chart(scatters, theme=None, use_container_width=True)
+
+
+################################################
+# 2. Adding features, Pushing to HF
+################################################
+
+st.header('Requirements, README file, Pushing to HuggingFace')
+
+### 2.1 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)
+
+### 2.2 Push these changes to HF ###
+# 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.
+
+# While we're doing this, let's also take a look at the README.md file!

requirements.txt

@@ -1,3 +1,5 @@
 streamlit
 altair
-vega_datasets
+numpy
+pandas
+matplotlib