Update app.py

app.py

@@ -14,20 +14,20 @@ from transformers_interpret import SequenceClassificationExplainer
 
 tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1")  
 model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1")
-modelc = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1").cuda
+# modelc = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1").cuda
 
 
 cls_explainer = SequenceClassificationExplainer(
     model,
     tokenizer)
 
-# define a prediction function
-def f(x):
-    tv = torch.tensor([tokenizer.encode(v, padding='max_length', max_length=500, truncation=True) for v in x]).cuda()
-    outputs = modelc(tv)[0].detach().cpu().numpy()
-    scores = (np.exp(outputs).T / np.exp(outputs).sum(-1)).T
-    val = sp.special.logit(scores[:,1]) # use one vs rest logit units
-    return val
+# # define a prediction function
+# def f(x):
+#     tv = torch.tensor([tokenizer.encode(v, padding='max_length', max_length=500, truncation=True) for v in x]).cuda()
+#     outputs = modelc(tv)[0].detach().cpu().numpy()
+#     scores = (np.exp(outputs).T / np.exp(outputs).sum(-1)).T
+#     val = sp.special.logit(scores[:,1]) # use one vs rest logit units
+#     return val
 
 def adr_predict(x):
     encoded_input = tokenizer(x, return_tensors='pt')
@@ -43,6 +43,7 @@ def adr_predict(x):
     # shap_plot = shap.plots.text(shap_values)
 
     word_attributions = cls_explainer(str(x))
+    # scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))
     letter = []
     score = []
     for i in word_attributions:
@@ -62,21 +63,23 @@ def adr_predict(x):
     
     word_attributions = [(letter[i], score[i]) for i in range(0, len(letter))]
 
-    # SHAP:
-    # build an explainer using a token masker
-    explainer = shap.Explainer(f, tokenizer)
-    shap_values = explainer(str(x), fixed_context=1)
-    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))
-    # plot the first sentence's explanation
-    # plt = shap.plots.text(shap_values[0],display=False)
+    # # SHAP:
+    # # build an explainer using a token masker
+    # explainer = shap.Explainer(f, tokenizer)
+    # shap_values = explainer(str(x), fixed_context=1)
+    # scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))
+    # # plot the first sentence's explanation
+    # # plt = shap.plots.text(shap_values[0],display=False)
 
 
-    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, word_attributions,scores
+    return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, word_attributions
+    # ,scores
 
 def main(text):
     text = str(text).lower()
     obj = adr_predict(text)
-    return obj[0],obj[1],obj[2]
+    return obj[0],obj[1]
+    # ,obj[2]
 
 title = "Welcome to **ADR Detector** 🪐"
 description1 = """
@@ -102,16 +105,20 @@ with gr.Blocks(title=title) as demo:
                                                 "--": "darkblue",
                                                 "-": "blue", "NA":"white"})
 
-        interpretation = gr.components.Interpretation(text)
+        # interpretation = gr.components.Interpretation(text)
 
 
     submit_btn.click(
         main,
         [text],
-        [label,intp,interpretation], api_name="adr"
+        [label,intp
+         # ,interpretation
+        ], 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,intp,interpretation], main, cache_examples=True)
+    gr.Examples([["I have minor pain."],["I have severe pain."]], [text], [label,intp
+                                                                           # ,interpretation
+                                                                          ], main, cache_examples=True)
     
 demo.launch()