Connect Streamlit to TigerGraph

This guide explains how to securely access a TigerGraph database from Streamlit Cloud. It uses the pyTigerGraph library and Streamlit's secrets management.

First, follow the official tutorials to create a TigerGraph instance in TigerGraph Cloud, either as a blog or a video. Note your username, password, and subdomain.

For this tutorial, we will be using the COVID-19 starter kit. When setting up your solution, select the “COVID-19 Analysis” option.

TG_Cloud_COVID19

Once it is started, ensure your data is downloaded and queries are installed.

TG_Cloud_Schema

Your local Streamlit app will read secrets from a file .streamlit/secrets.toml in your app’s root directory. Create this file if it doesn’t exist yet and add your TigerGraph Cloud instance username, password, graph name, and subdomain as shown below:

# .streamlit/secrets.toml

[tigergraph]
host = "https://xxx.i.tgcloud.io/"
username = "xxx"
password = "xxx"
graphname = "xxx"
priority_high

Important

Add this file to .gitignore and don't commit it to your Github repo!

As the secrets.toml file above is not committed to Github, you need to pass its content to your deployed app (on Streamlit Cloud) separately. Go to the app dashboard and in the app's dropdown menu, click on Edit Secrets. Copy the content of secrets.toml into the text area. More information is available at Secrets Management.

Secrets manager screenshot

Add the pyTigerGraph package to your requirements.txt file, preferably pinning its version (replace x.x.x with the version you want installed):

# requirements.txt
pyTigerGraph==x.x.x

Copy the code below to your Streamlit app and run it. Make sure to adapt the name of your graph and query.

# streamlit_app.py

import streamlit as st
import pyTigerGraph as tg

# Initialize connection.
conn = tg.TigerGraphConnection(**st.secrets["tigergraph"])
conn.apiToken = conn.getToken(conn.createSecret())

# Pull data from the graph by running the "mostDirectInfections" query.
# Uses st.experimental_memo to only rerun when the query changes or after 10 min.
@st.experimental_memo(ttl=600)
def get_data():
    most_infections = conn.runInstalledQuery("mostDirectInfections")[0]["Answer"][0]
    return most_infections["v_id"], most_infections["attributes"]

items = get_data()

# Print results.
st.title(f"Patient {items[0]} has the most direct infections")
for key, val in items[1].items():
    st.write(f"Patient {items[0]}'s {key} is {val}.")

See st.experimental_memo above? Without it, Streamlit would run the query every time the app reruns (e.g. on a widget interaction). With st.experimental_memo, it only runs when the query changes or after 10 minutes (that's what ttl is for). Watch out: If your database updates more frequently, you should adapt ttl or remove caching so viewers always see the latest data. Read more about caching here.

If everything worked out (and you used the example data we created above), your app should look like this:

Final_App

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