Update app.py

app.py

@@ -24,9 +24,14 @@ pred = transformers.pipeline("text-classification", model=model,
 
 explainer = shap.Explainer(pred)
 
-
 ##
 classifier = transformers.pipeline("text-classification", model = "cross-encoder/qnli-electra-base")
+
+def med_score(x):
+    label = x['label']
+    score_1 = x['score']
+    return score_1
+
 ##
 
 def adr_predict(x):
@@ -38,9 +43,10 @@ def adr_predict(x):
     shap_values = explainer([str(x).lower()])
     local_plot = shap.plots.text(shap_values[0], display=False)
 
-    med = classifier(x+str("There is a medication."))[0]
+    med = med_scoreclassifier(x+str("There is a medication."))[0])
    
-    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot, med
+    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot, 
+    {"Contains Medication": float(med), "No Medications": float(1-med)}
 
 
 def main(prob1):
@@ -64,6 +70,7 @@ with gr.Blocks(title=title) as demo:
         label = gr.Label(label = "Predicted Label")
         local_plot = gr.HTML(label = 'Shap:')
         med = gr.Label(label = "Contains Medication")
+        
 
     submit_btn.click(
         main,