Update app.py

app.py

@@ -12,7 +12,6 @@ from transformers import AutoModelForSequenceClassification
 from transformers import TFAutoModelForSequenceClassification
 from transformers import AutoTokenizer
 import matplotlib.pyplot as plt
-# from transformers_interpret import SequenceClassificationExplainer
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
@@ -25,90 +24,17 @@ pred = transformers.pipeline("text-classification", model=model,
 
 explainer = shap.Explainer(pred)
 
-def interpretation_function(text):
-    shap_values = explainer([text])
-    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))
-    return scores
-
-# model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1")
-# 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
-
 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)
-
-    # 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:
-    #     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))]
-
-    # # 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_scores = interpretation_function(str(x).lower())
-
+   
     shap_values = explainer([str(x).lower()])
     local_plot = shap.plots.text(shap_values[0], display=False)
-    
-    # local_plot = (
-    #     ""
-    #     + plot
-    #     + ""
-    # )
-
-    # plt.tight_layout()
-    # local_plot = plt.gcf()
-    # plt.rcParams['figure.figsize'] = 6,4
-    # plt.close()
-    
-
-
+   
     return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot
-    # shap_scores
-    # , word_attributions ,scores
+
 
 def main(prob1):
     text = str(prob1).lower()
@@ -128,31 +54,18 @@ with gr.Blocks(title=title) as demo:
 
     with gr.Column(visible=True) as output_col:
         label = gr.Label(label = "Predicted Label")
-        # impplot = gr.HighlightedText(label="Important Words", combine_adjacent=False).style(
-        # color_map={"+++": "royalblue","++": "cornflowerblue",
-        #  "+": "lightsteelblue", "NA":"white"})
-        # NER = gr.HTML(label = 'NER:')
-        # intp = gr.HighlightedText(label="Word Scores",
-        # combine_adjacent=False).style(color_map={"++": "darkred","+": "red", 
-        #                                         "--": "darkblue",
-        #                                         "-": "blue", "NA":"white"})
-
-        # interpretation = gr.components.Interpretation(prob1)
         local_plot = gr.HTML(label = 'Shap:')
-        # local_plot = gr.Plot(label = 'Shap:')
-
 
     submit_btn.click(
         main,
         [prob1],
         [label
-         # ,intp
          ,local_plot
         ], 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."]], [prob1], [label,local_plot
+    gr.Examples([["I have severe pain."],["I have minor pain."]], [prob1], [label,local_plot
                                                                           ], main, cache_examples=True)
     
 demo.launch()