Last active
May 30, 2023 17:18
This demonstrates how piping cached functions into one another automatically sets up an efficient directed acyclic computational graph.
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| import streamlit as st | |
| import pandas as pd | |
| @st.cache | |
| def load_metadata(): | |
| DATA_URL = "https://streamlit-self-driving.s3-us-west-2.amazonaws.com/labels.csv.gz" | |
| return pd.read_csv(DATA_URL, nrows=1000) | |
| @st.cache | |
| def create_summary(metadata, summary_type): | |
| one_hot_encoded = pd.get_dummies(metadata[["frame", "label"]], columns=["label"]) | |
| return getattr(one_hot_encoded.groupby(["frame"]), summary_type)() | |
| # Piping one st.cache function into another forms a computation DAG. | |
| summary_type = st.selectbox("Type of summary:", ["sum", "any"]) | |
| metadata = load_metadata() | |
| summary = create_summary(metadata, summary_type) | |
| st.write('## Metadata', metadata, '## Summary', summary) |
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