app.py

import os
import pandas as pd
import streamlit as st
from PIL import Image
from streamlit import components
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers_interpret import SequenceClassificationExplainer

@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=1)

def load_model(model_name):
    return (
        AutoModelForSequenceClassification.from_pretrained(model_name),
        AutoTokenizer.from_pretrained(model_name),
    )


st.title("Transformers Interpet Demo App")

image = Image.open("./images/tight@1920x_transparent.png")
st.sidebar.image(image, use_column_width=True)
st.sidebar.markdown(
"Check out the package on [Github](https://github.com/cdpierse/transformers-interpret)"
)

# uncomment the options below to test out the app with a variety of classification models.
models = {
     "mrm8488/bert-mini-finetuned-age_news-classification": "BERT-Mini finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
     "nateraw/bert-base-uncased-ag-news": "BERT finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
    "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT model finetuned on SST-2 sentiment analysis task. Predicts positive/negative sentiment.",
     "ProsusAI/finbert": "BERT model finetuned to predict sentiment of financial text. Finetuned on Financial PhraseBank data. Predicts positive/negative/neutral.",
    "sampathkethineedi/industry-classification": "DistilBERT Model to classify a business description into one of 62 industry tags.",
    "MoritzLaurer/policy-distilbert-7d": "DistilBERT model finetuned to classify text into one of seven political categories.",
    # # "MoritzLaurer/covid-policy-roberta-21": "(Under active development ) RoBERTA model finetuned to identify COVID policy measure classes ",
     "mrm8488/bert-tiny-finetuned-sms-spam-detection": "Tiny bert model finetuned for spam detection. 0 == not spam, 1 == spam",
}
model_name = st.sidebar.selectbox(
    "Choose a classification model", list(models.keys())
)
model, tokenizer = load_model(model_name)

model.eval()

cls_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer)

if cls_explainer.accepts_position_ids:
    emb_type_name = st.sidebar.selectbox(
        "Choose embedding type for attribution.", ["word", "position"]
    )
    if emb_type_name == "word":
        emb_type_num = 0
    if emb_type_name == "position":
        emb_type_num = 1
else:
    emb_type_num = 0

explanation_classes = ["predicted"] + list(model.config.label2id.keys())
explanation_class_choice = st.sidebar.selectbox(
    "Explanation class: The class you would like to explain output with respect to.",
    explanation_classes,
)
my_expander = st.expander(
    "Click here for a description of models and their tasks"
)
with my_expander:
    st.json(models)

# st.info("Max char limit of 350 (memory management)")
text = st.text_area(
    "Enter text to be interpreted",
    "I like you, I love you",
    height=400,
    max_chars=850,
)

if st.button("Interpret Text"):
    st.text("Output")
    with st.spinner("Interpreting your text (This may take some time)"):
        print ("Interpreting text")
        if explanation_class_choice != "predicted":
            word_attributions = cls_explainer(
                text,
                class_name=explanation_class_choice,
                embedding_type=emb_type_num,
                internal_batch_size=2,
            )
        else:
            word_attributions = cls_explainer(
                text, embedding_type=emb_type_num, internal_batch_size=2
            )

    if word_attributions:
        print ("Word Attributions")
        word_attributions_expander = st.expander(
            "Click here for raw word attributions"
        )
        with word_attributions_expander:
            st.json(word_attributions)
        components.v1.html(
            cls_explainer.visualize()._repr_html_(), scrolling=True, height=350
        )