Update app.py

app.py

@@ -30,17 +30,17 @@ pred = transformers.pipeline("text-classification", model=model,
 explainer = shap.Explainer(pred)
 
 ##
-classifier = transformers.pipeline("text-classification", model = "cross-encoder/qnli-electra-base")
+# classifier = transformers.pipeline("text-classification", model = "cross-encoder/qnli-electra-base")
 
-def med_score(x):
-    label = x['label']
-    score_1 = x['score']
-    return round(score_1,3)
+# def med_score(x):
+#     label = x['label']
+#     score_1 = x['score']
+#     return round(score_1,3)
 
-def sym_score(x):
-    label2sym= x['label']
-    score_1sym = x['score']
-    return round(score_1sym,3)
+# def sym_score(x):
+#     label2sym= x['label']
+#     score_1sym = x['score']
+#     return round(score_1sym,3)
 
 ner_tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
 ner_model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
@@ -57,8 +57,8 @@ def adr_predict(x):
     shap_values = explainer([str(x).lower()])
     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])
+    # med = med_score(classifier(x+str(", There is a medication."))[0])
+    # sym = sym_score(classifier(x+str(", There is a symptom."))[0])
 
     res = ner_pipe(x)
     
@@ -69,7 +69,7 @@ def adr_predict(x):
     'Age': 'yellow',
     'Sex':'yellow',
     'Diagnostic_procedure':'gray',
-    'Biological_structure':'black'}
+    'Biological_structure':'silver'}
 
     htext = ""
     prev_end = 0
@@ -85,13 +85,14 @@ def adr_predict(x):
 
     htext += x[prev_end:]
    
-    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)} , {"Contains Symptoms": float(sym), "No Symptoms": float(1-sym)},htext
+    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot,htext
+    # ,{"Contains Medication": float(med), "No Medications": float(1-med)} , {"Contains Symptoms": float(sym), "No Symptoms": float(1-sym)}
 
 
 def main(prob1):
     text = str(prob1).lower()
     obj = adr_predict(text)
-    return obj[0],obj[1],obj[2],obj[3],obj[4]
+    return obj[0],obj[1],obj[2]
 
 title = "Welcome to **ADR Detector** 🪐"
 description1 = """This app takes text (up to a few sentences) and predicts to what extent the text describes severe (or non-severe) adverse reaction to medicaitons. Please do NOT use for medical diagnosis."""
@@ -107,18 +108,20 @@ with gr.Blocks(title=title) as demo:
         
         with gr.Column(visible=True) as output_col:
             label = gr.Label(label = "Predicted Label")
-            local_plot = gr.HTML(label = 'Shap:')
-            htext = gr.HTML(label="NER")
+            
 
         with gr.Column(visible=True) as output_col:
-            med = gr.Label(label = "Contains Medication")
-            sym = gr.Label(label = "Contains Symptoms")
+            local_plot = gr.HTML(label = 'Shap:')
+            htext = gr.HighlightedText(label="NER")
+            # med = gr.Label(label = "Contains Medication")
+            # sym = gr.Label(label = "Contains Symptoms")
             
     submit_btn.click(
         main,
         [prob1],
         [label
-         ,local_plot, med, sym, htext
+         ,local_plot, htext
+         # , med, sym
         ], api_name="adr"
     )
     
@@ -126,6 +129,8 @@ with gr.Blocks(title=title) as demo:
         gr.Markdown("### Click on any of the examples below to see how it works:")
         gr.Examples([["A 35 year-old male had severe headache after taking Aspirin. The lab results were normal."],
                      ["A 35 year-old female had minor pain in upper abdomen after taking Acetaminophen."]], 
-                    [prob1], [label,local_plot, med, sym,htext], main, cache_examples=True)
+                    [prob1], [label,local_plot, htext
+         # , med, sym
+                             ], main, cache_examples=True)
     
 demo.launch()