Update app.py

app.py

@@ -8,23 +8,50 @@ from transformers import RobertaTokenizer, RobertaModel
 from transformers import AutoModelForSequenceClassification
 from transformers import TFAutoModelForSequenceClassification
 from transformers import AutoTokenizer
+from transformers_interpret import SequenceClassificationExplainer
 
 tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1")  
 model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1")
 
+cls_explainer = SequenceClassificationExplainer(
+    model,
+    tokenizer)
+
 def adr_predict(x):
     encoded_input = tokenizer(x, return_tensors='pt')
     output = model(**encoded_input)
     scores = output[0][0].detach().numpy()
     scores = tf.nn.softmax(scores)
     
-    # build a pipeline object to do predictions
-    pred = transformers.pipeline("text-classification", model=model, 
-                             tokenizer=tokenizer, device=0, return_all_scores=True)
-    explainer = shap.Explainer(pred)
-    shap_values = explainer([x])
-    shap_plot = shap.plots.text(shap_values)
-    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, shap_plot
+    # # build a pipeline object to do predictions
+    # pred = transformers.pipeline("text-classification", model=model, 
+    #                          tokenizer=tokenizer, device=0, return_all_scores=True)
+    # explainer = shap.Explainer(pred)
+    # shap_values = explainer([x])
+    # shap_plot = shap.plots.text(shap_values)
+
+    word_attributions = cls_explainer(str(x))
+    letter = []
+    score = []
+    for i in word_attributions:
+        if i[1]>0.5:
+            a = "++"
+        elif (i[1]<=0.5) and (i[1]>0.1):
+            a = "+"
+        elif (i[1]>=-0.5) and (i[1]<-0.1):
+            a = "-"
+        elif i[1]<-0.5:
+            a = "--"
+        else: 
+            a = "NA"
+    
+        letter.append(i[0])
+        score.append(a)
+    
+    word_attributions = [(letter[i], score[i]) for i in range(0, len(letter))]
+
+
+    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, word_attributions
 
 def main(text):
     text = str(text).lower()
@@ -50,15 +77,19 @@ with gr.Blocks(title=title) as demo:
         # color_map={"+++": "royalblue","++": "cornflowerblue",
         #  "+": "lightsteelblue", "NA":"white"})
         # NER = gr.HTML(label = 'NER:')
-        shap_plot = gr.HighlightedText(label="Word Scores",combine_adjacent=False)
+        intp = gr.HighlightedText(label="Word Scores",
+        combine_adjacent=False).style(color_map={"++": "darkgreen","+": "green", 
+                                                "--": "darkred",
+                                                "-": "red", "NA":"white"})
+
 
     submit_btn.click(
         main,
         [text],
-        [label,shap_plot], api_name="adr"
+        [label,intp], 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,shap_plot], main, cache_examples=True)
+    gr.Examples([["I have minor pain."],["I have severe pain."]], [text], [label,intp], main, cache_examples=True)
     
 demo.launch()