Update app.py

app.py

@@ -66,11 +66,12 @@ def adr_predict(x):
     # build an explainer using a token masker
     explainer = shap.Explainer(f, tokenizer)
     shap_values = explainer(str(x), fixed_context=1)
+    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))
     # plot the first sentence's explanation
-    plt = shap.plots.text(shap_values[0],display=False)
+    # plt = shap.plots.text(shap_values[0],display=False)
 
 
-    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, word_attributions,plt
+    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, word_attributions,scores
 
 def main(text):
     text = str(text).lower()
@@ -101,16 +102,16 @@ with gr.Blocks(title=title) as demo:
                                                 "--": "darkblue",
                                                 "-": "blue", "NA":"white"})
 
-        shap = gr.HighlightedText(label="SHAP Scores",combine_adjacent=False)
+        interpretation = gr.components.Interpretation(text)
 
 
     submit_btn.click(
         main,
         [text],
-        [label,intp,shap], api_name="adr"
+        [label,intp,interpretation], api_name="adr"
     )
 
     gr.Markdown("### Click on any of the examples below to see to what extent they contain resilience messaging:")
-    gr.Examples([["I have minor pain."],["I have severe pain."]], [text], [label,intp,shap], main, cache_examples=True)
+    gr.Examples([["I have minor pain."],["I have severe pain."]], [text], [label,intp,interpretation], main, cache_examples=True)
     
 demo.launch()