app.py

from io import BytesIO

import pandas as pd
import streamlit as st
import tokenizers
import torch
from transformers import Pipeline, pipeline

st.set_page_config(
    page_title="Zero-shot classification from tabular data",
    page_icon=None,
    layout="wide",
    initial_sidebar_state="auto",
    menu_items=None,
)


@st.cache(
    hash_funcs={
        torch.nn.parameter.Parameter: lambda _: None,
        tokenizers.Tokenizer: lambda _: None,
        tokenizers.AddedToken: lambda _: None,
    },
    allow_output_mutation=True,
    show_spinner=False,
)
def load_classifier() -> Pipeline:
    classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
    return classifier


with st.spinner(text="Setting stuff up related to the inference engine..."):
    classifier = load_classifier()

st.title("Zero-shot classification from tabular data")
st.text(
    "Upload an Excel table and perform zero-shot classification on a set of custom labels"
)

data = st.file_uploader(
    "Upload Excel file (it should contain a column named `text` in its header):"
)
labels = st.text_input("Enter comma-separated labels:")

# classify first N snippets only for faster inference

if st.button("Calculate labels"):

    try:
        labels_list = labels.split(",")
        table = pd.read_excel(data)
        table = table.loc[table["text"].apply(len) > 10].reset_index(drop=True)

        prog_bar = st.progress(0)
        preds = []

        for i in range(len(table)):
            preds.append(classifier(table.loc[i, "text"], labels)["labels"][0])
            prog_bar.progress((i + 1) / len(table))

        table["label"] = preds

        st.table(table[["text", "label"]])

        buf = BytesIO()
        table[["text", "label"]].to_excel(buf)

        st.download_button(
            label="Download table", data=buf.getvalue(), file_name="output.xlsx"
        )

    except:
        st.error(
            "Something went wrong. Make sure you upload an Excel file containing a column named `text` and a set of comma-separated labels is provided"
        )