Update app.py

app.py

@@ -6,10 +6,7 @@ import scipy as sp
 import torch
 import transformers
 from transformers import pipeline
-from transformers import RobertaTokenizer, RobertaModel
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
 
 import matplotlib.pyplot as plt
 import sys
@@ -22,30 +19,15 @@ device = "cuda:0" if torch.cuda.is_available() else "cpu"
 tokenizer = AutoTokenizer.from_pretrained("jschwaller/ADRv2024")  
 model = AutoModelForSequenceClassification.from_pretrained("jschwaller/ADRv2024")
 
-# build a pipeline object to do predictions
+# Build a pipeline object for predictions
 pred = transformers.pipeline("text-classification", model=model, 
                              tokenizer=tokenizer, return_all_scores=True)
 
 explainer = shap.Explainer(pred)
 
-##
-# 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 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")
-
-ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
-#
+ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple")
 
 def adr_predict(x):
     encoded_input = tokenizer(x, return_tensors='pt')
@@ -54,31 +36,21 @@ def adr_predict(x):
     scores = torch.nn.functional.softmax(scores)
    
     shap_values = explainer([str(x).lower()])
-    # # Find the index of the class you want as the default reference (e.g., 'label_1')
-    # label_1_index = np.where(np.array(explainer.output_names) == 'label_1')[0][0]
-
-    # # Plot the SHAP values for a specific instance in your dataset (e.g., instance 0)
-    # shap.plots.text(shap_values[label_1_index][0])
-
     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])
-
     res = ner_pipe(x)
-    
     entity_colors = {
-    'Severity': 'red',
-    'Sign_symptom': 'green',
-    'Medication': 'lightblue',
-    'Age': 'yellow',
-    'Sex':'yellow',
-    'Diagnostic_procedure':'gray',
-    'Biological_structure':'silver'}
+        'Severity': 'red',
+        'Sign_symptom': 'green',
+        'Medication': 'lightblue',
+        'Age': 'yellow',
+        'Sex': 'yellow',
+        'Diagnostic_procedure': 'gray',
+        'Biological_structure': 'silver'
+    }
 
     htext = ""
     prev_end = 0
-
     for entity in res:
         start = entity['start']
         end = entity['end']
@@ -87,55 +59,50 @@ def adr_predict(x):
         
         htext += f"{x[prev_end:start]}<mark style='background-color:{color};'>{word}</mark>"
         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
 
 def main(prob1):
     text = str(prob1).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 = """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."""
+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 medications. Please do NOT use for medical diagnosis."
 
-with gr.Blocks(title=title) as demo:
+css = """
+body { font-family: 'Roboto', sans-serif; background-color: #fafafa; color: #333; }
+h1, h2, h3, h4, h5, h6 { color: #ff6347; }
+.textbox { width: 100%; border-radius: 10px; border: 1px solid #ccc; }
+.button { background-color: #ff6347; color: white; border: none; border-radius: 10px; padding: 10px 20px; cursor: pointer; }
+"""
+
+with gr.Blocks(css=css) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown(description1)
-    gr.Markdown("""---""")
-    prob1 = gr.Textbox(label="Enter Your Text Here:",lines=2, placeholder="Type it here ...")
+    gr.Markdown("---")
+    prob1 = gr.Textbox(label="Enter Your Text Here:", lines=2, placeholder="Type it here...")
     submit_btn = gr.Button("Analyze")
 
     with gr.Row():
-        
-        with gr.Column(visible=True) as output_col:
-            label = gr.Label(label = "Predicted Label")
-            
-
-        with gr.Column(visible=True) as output_col:
-            local_plot = gr.HTML(label = 'Shap:')
+        with gr.Column(visible=True):
+            label = gr.Label(label="Predicted Label")
+        with gr.Column(visible=True):
+            local_plot = gr.HTML(label='Shap:')
             htext = gr.HTML(label="NER")
-            # med = gr.Label(label = "Contains Medication")
-            # sym = gr.Label(label = "Contains Symptoms")
-            
+
     submit_btn.click(
         main,
         [prob1],
-        [label
-         ,local_plot, htext
-         # , med, sym
-        ], api_name="adr"
+        [label, local_plot, htext],
+        api_name="adr"
     )
     
     with gr.Row():
         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, htext
-         # , med, sym
-                             ], main, cache_examples=True)
-    
-demo.launch()
+                    [prob1], [label, local_plot, htext], main, cache_examples=True)
+
+demo.launch()