Update app.py

app.py

@@ -53,16 +53,14 @@ def adr_predict(x):
     scores = output[0][0].detach()
     scores = torch.nn.functional.softmax(scores)
    
-    shap_values = explainer([str("The young woman had a severe drug reaction.").lower()])
+    shap_values = explainer([str(x).lower()])
     # # Find the index of the class you want as the default reference (e.g., 'label_1')
     # label_1_index = np.where(np.array(explainer.output_names) == 'label_1')[0][0]
-    label_1_index = np.where(np.array(explainer.output_names) == 'label_1')[0][0]
 
     # # Plot the SHAP values for a specific instance in your dataset (e.g., instance 0)
     # shap.plots.text(shap_values[label_1_index][0])
-    shap.plots.text(shap_values[label_1_index][1])
-
-    local_plot = shap.plots.text(shap_values[1], display=False)
+   
+    local_plot = shap.plots.text(shap_values[0], display=False)
 
     # med = med_score(classifier(x+str(", There is a medication."))[0])
     # sym = sym_score(classifier(x+str(", There is a symptom."))[0])