Add application file

app.py

@@ -0,0 +1,30 @@
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+import pandas as pd
+import matplotlib.pyplot as plt
+
+labels = ['AS-yes', 'AS-no', 'AS-no_information']
+
+# Change `transformersbook` to your Hub username
+model_id = "sarahflan/distilbert-base-uncased-finetuned-AS_sentences"
+classifier = pipeline("text-classification", model=model_id)
+
+st.title("Aortic Stenosis sentence classifier ")
+
+sentence = st.text_input('Echocardiogram demonstrated a heavily calcified aortic valve with limited leaflet mobility. Findings are consistent with severe aortic stenosis.','Patient has AS')
+
+preds = classifier(sentence, return_all_scores=True)
+
+preds_df = pd.DataFrame(preds[0], columns=['score'])
+preds_df['labels'] = labels
+
+sorted_preds_df = preds_df.sort_values(by='score', ascending=False).head(20)
+
+fig, ax = plt.subplots()
+ax.bar(sorted_preds_df['labels'], 100 * sorted_preds_df['score'], color='C0')
+ax.set_title(f'"{sentence}"')
+ax.set_ylabel("Class probability (%)")
+ax.tick_params(axis='x', labelrotation=90)
+
+st.pyplot(fig)