I am trying to build a dashboard of my NLP project. So I am using a BERT model for the predictions, the SHAP package for the visualization and Streamlit for creating dashboard:
tokenizer = AutoTokenizer.from_pretrained(model_name_cla)
model = AutoModelForSequenceClassification.from_pretrained(model_name_cla)
labels = ['1- Tarife','2- Dateneingabe','3- Bestätigungsmail','4- Kundenbetreuung','5- Aufwand vom Vergleich bis Abschluss',
'6- After-sales Wechselprozess','7 - Werbung/VX Kommunikation','8 - Sonstiges','9 - Nicht auswertbar']
def f(x):
tv = torch.tensor([tokenizer.encode(v, padding='max_length', max_length=128, truncation=True) for v in x])
attention_mask = (tv!=0).type(torch.int64)
outputs = model(tv,attention_mask=attention_mask)[0].detach().cpu().numpy()
scores = (np.exp(outputs).T / np.exp(outputs).sum(-1)).T
val = sp.special.logit(scores)
return val
text = ['This is just a test']
# build an explainer using a token masker
explainer = shap.Explainer(f, tokenizer, output_names=labels)
shap_values = explainer(text, fixed_context=1)
shap.plots.text(shap_values)
The code works fine in my jupyter notebook but when I am trying to execute this as .py file with streamlit nothing happens. It neither displays anything nor throwing an error. My console just returns this while execution:
<IPython.core.display.HTML object> >
How can I display my graph in streamlit?
This can be visualized with Streamlit Components
and latest SHAP v0.36+
(which define a new getjs
method), to plot JS SHAP plots
(some plots like
summary_plot
are actuallyMatplotlib
and can be plotted withst.pyplot
)
import streamlit as st
import streamlit.components.v1 as components
def st_shap(plot, height=None):
shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>"
components.html(shap_html, height=height)
st_shap(shap.plots.text(shap_values),400)
Find more detailed discussion on Visualizing Shap in streamlit
here - Display SHAP diagrams with Streamlit