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import streamlit as st |
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import streamlit.components.v1 as components |
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from transformers import (AutoModelForSequenceClassification, AutoTokenizer, |
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pipeline) |
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import shap |
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from PIL import Image |
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import time |
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st.set_option('deprecation.showPyplotGlobalUse', False) |
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output_width = 800 |
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output_height = 300 |
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rescale_logits = False |
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st.set_page_config(page_title='Text Classification with Shap') |
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st.title('Interpreting HF Pipeline Text Classification with Shap') |
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form = st.sidebar.form("Model Selection") |
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form.header('Model Selection') |
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model_name = form.text_input("Enter the name of the text classification LLM (note: model must be fine-tuned on a text classification task)", value = "Hate-speech-CNERG/bert-base-uncased-hatexplain") |
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form.form_submit_button("Submit") |
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@st.cache_data() |
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def load_model(model_name): |
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tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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return tokenizer, model |
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tokenizer, model = load_model(model_name) |
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pred = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None) |
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explainer = shap.Explainer(pred, rescale_to_logits = rescale_logits) |
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col1, col2, col3 = st.columns(3) |
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text = col1.text_area("Enter text input", value = "Classify me.") |
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start_time = time.time() |
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result = pred(text) |
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inference_time = time.time() - start_time |
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col3.write('') |
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col3.write(f'**Inference Time:** {inference_time: .4f}') |
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top_pred = result[0][0]['label'] |
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col2.write('') |
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for label in result[0]: |
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col2.write(f'**{label["label"]}**: {label["score"]: .2f}') |
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shap_values = explainer([text]) |
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explanation_time = shap_values.compute_time |
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col3.write('') |
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col3.write(f'**Explanation Time:** {explanation_time: .4f}') |
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force_plot = shap.plots.text(shap_values, display=False) |
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bar_plot = shap.plots.bar(shap_values[0, :, top_pred], order=shap.Explanation.argsort.flip, show=False) |
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st.markdown(""" |
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<style> |
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.big-font { |
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font-size:35px !important; |
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} |
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</style> |
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""", unsafe_allow_html=True) |
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st.markdown(f'<center><p class="big-font">Shap Bar Plot for <i>{top_pred}</i> Prediction</p></center>', unsafe_allow_html=True) |
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st.pyplot(bar_plot, clear_figure=True) |
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st.markdown('<center><p class="big-font">Shap Interactive Force Plot</p></center>', unsafe_allow_html=True) |
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components.html(force_plot, height=output_height, width=output_width, scrolling=True) |
