Update app.py

app.py

@@ -4,9 +4,10 @@ import shap
 import numpy as np
 import scipy as sp
 import torch
-
+import tensorflow as tf
 import transformers
 from transformers import pipeline
+from transformers import RobertaTokenizer, RobertaModel
 from transformers import AutoModelForSequenceClassification
 from transformers import TFAutoModelForSequenceClassification
 from transformers import AutoTokenizer, AutoModelForTokenClassification
@@ -19,8 +20,8 @@ csv.field_size_limit(sys.maxsize)
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
-tokenizer = AutoTokenizer.from_pretrained("bert-based-case/ADR_Detector")  
-model = AutoModelForSequenceClassification.from_pretrained("bert-based-case/ADR_Detector").to(device)
+tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1")  
+model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1").to(device)
 
 # build a pipeline object to do predictions
 pred = transformers.pipeline("text-classification", model=model, 
@@ -41,11 +42,17 @@ explainer = shap.Explainer(pred)
 #     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")
+
+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')
     output = model(**encoded_input)
-    scores = output[0][0].detach()
-    scores = torch.nn.functional.softmax(scores)
+    scores = output[0][0].detach().numpy()
+    scores = tf.nn.softmax(scores)
    
     shap_values = explainer([str(x).lower()])
     # # Find the index of the class you want as the default reference (e.g., 'label_1')
@@ -83,10 +90,9 @@ def adr_predict(x):
         prev_end = end
 
     htext += x[prev_end:]
-   
-    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)}
 
+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()