Update app.py

app.py

@@ -1,63 +1,63 @@
-import streamlit as st
-import streamlit.components.v1 as components
-from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
-                          pipeline)
-import shap
-from PIL import Image
-
-st.set_option('deprecation.showPyplotGlobalUse', False)
-output_width = 800
-output_height = 300
-rescale_logits = False
-
-
-
-st.set_page_config(page_title='Text Classification with Shap')
-logo = Image.open('Intel-logo.png')
-st.sidebar.image(logo)
-st.title('Interpreting HF Pipeline Text Classification with Shap')
-
-form = st.sidebar.form("Model Selection")
-form.header('Model Selection')
-
-model_name = form.text_area("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")
-form.form_submit_button("Submit")
-
-
-@st.cache_data()
-def load_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-    return tokenizer, model
-
-tokenizer, model = load_model(model_name)
-pred = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None)
-explainer = shap.Explainer(pred, rescale_to_logits = rescale_logits)
-
-col1, col2 = st.columns(2)
-text = col1.text_area("Enter text input", value = "Classify me.")
-
-result = pred(text)
-top_pred = result[0][0]['label']
-col2.write('')
-for label in result[0]:
-    col2.write(f'**{label["label"]}**: {label["score"]: .2f}')
-
-shap_values = explainer([text])
-
-force_plot = shap.plots.text(shap_values, display=False)
-bar_plot = shap.plots.bar(shap_values[0, :, top_pred], order=shap.Explanation.argsort.flip, show=False)
-
-st.markdown("""
-<style>
-.big-font {
-    font-size:35px !important;
-}
-</style>
-""", unsafe_allow_html=True)
-st.markdown(f'<center><p class="big-font">Shap Bar Plot for <i>{top_pred}</i> Prediction</p></center>', unsafe_allow_html=True)
-st.pyplot(bar_plot, clear_figure=True)
-
-st.markdown('<center><p class="big-font">Shap Interactive Force Plot</p></center>', unsafe_allow_html=True)
-components.html(force_plot, height=output_height, width=output_width, scrolling=True)
+import streamlit as st
+import streamlit.components.v1 as components
+from transformers import (AutoModelForSequenceClassification, AutoTokenizer,
+                          pipeline)
+import shap
+from PIL import Image
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+output_width = 800
+output_height = 300
+rescale_logits = False
+
+
+
+st.set_page_config(page_title='Text Classification with Shap')
+logo = Image.open('Intel-logo.png')
+st.sidebar.image(logo)
+st.title('Interpreting HF Pipeline Text Classification with Shap')
+
+form = st.sidebar.form("Model Selection")
+form.header('Model Selection')
+
+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")
+form.form_submit_button("Submit")
+
+
+@st.cache_data()
+def load_model(model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+    return tokenizer, model
+
+tokenizer, model = load_model(model_name)
+pred = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None)
+explainer = shap.Explainer(pred, rescale_to_logits = rescale_logits)
+
+col1, col2 = st.columns(2)
+text = col1.text_area("Enter text input", value = "Classify me.")
+
+result = pred(text)
+top_pred = result[0][0]['label']
+col2.write('')
+for label in result[0]:
+    col2.write(f'**{label["label"]}**: {label["score"]: .2f}')
+
+shap_values = explainer([text])
+
+force_plot = shap.plots.text(shap_values, display=False)
+bar_plot = shap.plots.bar(shap_values[0, :, top_pred], order=shap.Explanation.argsort.flip, show=False)
+
+st.markdown("""
+<style>
+.big-font {
+    font-size:35px !important;
+}
+</style>
+""", unsafe_allow_html=True)
+st.markdown(f'<center><p class="big-font">Shap Bar Plot for <i>{top_pred}</i> Prediction</p></center>', unsafe_allow_html=True)
+st.pyplot(bar_plot, clear_figure=True)
+
+st.markdown('<center><p class="big-font">Shap Interactive Force Plot</p></center>', unsafe_allow_html=True)
+components.html(force_plot, height=output_height, width=output_width, scrolling=True)