How to evolve complex state (e.g., annotate data)?

Using Streamlit
13

This is all very cool and interesting. I was able to take @Lutz’s example and convert it to load a DataFrame, add annotations to the DataFrame, and finally save it for a current project. There are, of course, many possible embellishments (saving work so far, seeking up to elements not yet annotated, quitting early, etc).

I also ran into the (same) problem where it shows the last item twice. Additionally, the necessity of writing the same code twice to get it to “run” was weird but I just wrote a display() function. All of the global state is making the functional programmer in me twitch.

There are minor things (being able to put the buttons in a row) that I’d like to see, otherwise. I foresee some NLP applications where you might want to return the index of the selection (I’m trying to think about things I have done in the past).

I have no idea if it is “good”, though.

import streamlit as st
import pandas as pd

categories = {"good": 3, "ambiguous": 2, "skip": 1, "bad": 0}

@st.cache(ignore_hash=True)
def get_data():
    data = pd.read_csv("test.csv")
    data["annotation"] = None
    return data

@st.cache(ignore_hash=True)
def get_annotation():
    return {"row": 0}

row = st.empty()
match = st.empty()
buttons = {}

data = get_data()
annotation = get_annotation()

def detail():
    current_obs = data.loc[annotation["row"]]
    row.markdown(f"# {annotation['row'] + 1}")
    match.markdown(f"**{current_obs['location']}** matched **{current_obs['area']}**")

if annotation["row"] < len(data.index):
    for cat in categories.keys():
        buttons[cat] = st.button(cat)
    detail()
    for cat in categories.keys():
        if buttons[cat]:
            data.loc[annotation["row"], "annotation"] = categories[cat]
            annotation["row"] += 1
            if annotation["row"] < len(data.index):
                detail()
else:
    data.to_csv("test_annotated.csv")
    st.write("finished")