Update app.py

app.py

@@ -25,10 +25,11 @@ pred = transformers.pipeline("text-classification", model=model,
 
 explainer = shap.Explainer(pred)
 
-ner_tokenizer = AutoTokenizer.from_pretrained("Clinical-AI-Apollo/Medical-NER")
-ner_model = AutoModelForTokenClassification.from_pretrained("Clinical-AI-Apollo/Medical-NER")
+ner_tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
+ner_model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
 
 ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
+#
 
 def adr_predict(x):
     encoded_input = tokenizer(x, return_tensors='pt')
@@ -41,14 +42,14 @@ def adr_predict(x):
 
     res = ner_pipe(x)
     entity_colors = {
-        'Severity': 'red',
-        'Sign_symptom': 'green',
-        'Medication': 'lightblue',
-        'Age': 'yellow',
-        'Sex': 'yellow',
-        'Diagnostic_procedure': 'gray',
-        'Biological_structure': 'silver'
-    }
+        'Severity': '#E63946',  # a vivid red
+        'Sign_symptom': '#2A9D8F',  # a deep teal
+        'Medication': '#457B9D',  # a dusky blue
+        'Age': '#F4A261',  # a sandy orange
+        'Sex': '#F4A261',  # same sandy orange for consistency with 'Age'
+        'Diagnostic_procedure': '#9C6644',  # a brown
+        'Biological_structure': '#BDB2FF',  # a light pastel purple
+        }
 
     htext = ""
     prev_end = 0