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app.py

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+import gradio as gr
+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 AutoTokenizer, AutoModelForTokenClassification
+import matplotlib.pyplot as plt
+import sys
+import csv
+import os
+
+HF_TOKEN = os.getenv("hf_token")
+
+csv.field_size_limit(sys.maxsize)
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Load models and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1", token=HF_TOKEN)
+model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1", token=HF_TOKEN).to(device)
+
+# Build a pipeline object for predictions
+pred = transformers.pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None, device=device) 
+
+# SHAP explainer
+explainer = shap.Explainer(pred)
+
+# NER pipeline
+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):
+def adr_predict(x):
+    # Ensure input is treated as a string
+    text_input = str(x).lower()
+
+    encoded_input = tokenizer(text_input, return_tensors='pt').to(device) # Move input to device
+    output = model(**encoded_input)
+    
+    scores = torch.softmax(output.logits, dim=-1)[0].detach().cpu().numpy() # Apply softmax on logits, move to cpu and convert to numpy
+
+    try:
+        shap_values = explainer([text_input])
+
+        local_plot = shap.plots.text(shap_values[0], display=False) 
+
+    except Exception as e:
+        print(f"SHAP explanation failed: {e}")
+        local_plot = "<p>SHAP explanation not available.</p>" # Provide a fallback
+
+    # NER processing
+    try:
+        res = ner_pipe(text_input)
+
+        entity_colors = {
+            'Severity': 'red',
+            'Sign_symptom': 'green',
+            'Medication': 'lightblue',
+            'Age': 'yellow',
+            'Sex':'yellow',
+            'Diagnostic_procedure':'gray',
+            'Biological_structure':'silver'
+        }
+        htext = ""
+        prev_end = 0
+
+        res = sorted(res, key=lambda x: x['start'])
+
+        for entity in res:
+            start = entity['start']
+            end = entity['end']
+            word = text_input[start:end] # Extract original text segment
+            entity_type = entity['entity_group']
+            color = entity_colors.get(entity_type, 'lightgray') # Use get with a default color
+
+            # Append text before the entity
+            htext += f"{text_input[prev_end:start]}"
+            # Append the highlighted entity
+            htext += f"<mark style='background-color:{color};'>{word}</mark>"
+            prev_end = end
+        # Append any remaining text after the last entity
+        htext += text_input[prev_end:]
+    except Exception as e:
+        print(f"NER processing failed: {e}")
+        htext = "<p>NER processing not available.</p>" # Provide a fallback
+
+    label_output = {"Severe Reaction": float(scores[1]), "Non-severe Reaction": float(scores[0])}
+
+    return label_output, local_plot, htext
+
+def main(prob1):
+    return adr_predict(prob1)
+
+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."""
+
+# Use the 'with' syntax for Blocks
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description1)
+    gr.Markdown("""---""")
+
+    # Define input and output components
+    prob1 = gr.Textbox(label="Enter Your Text Here:",lines=2, placeholder="Type it here ...")
+
+    # Output components matching the return values of the main function
+    label = gr.Label(label = "Predicted Label")
+    local_plot = gr.HTML(label = 'Shap Explanation') 
+    htext = gr.HTML(label="Named Entity Recognition") 
+
+    submit_btn = gr.Button("Analyze")
+
+    submit_btn.click(
+        fn=main, # The function to call
+        inputs=[prob1], # The input components
+        outputs=[label, local_plot, htext], # The output components
+        api_name="adr" # Keep the api_name if you intend to use the API
+    )
+
+    # Examples section
+    gr.Markdown("### Click on any of the examples below to see how it works:")
+    # Gradio 4.0+ Examples usage. Pass inputs and outputs components directly.
+    # cache_examples is recommended for faster loading of examples.
+    gr.Examples(
+        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."]
+        ],
+        inputs=[prob1],
+        outputs=[label, local_plot, htext],
+        fn=main, # Provide the function to run for caching examples
+        cache_examples=False,
+        run_on_click=True
+    )
+
+# Launch the demo
+demo.launch()

requirements.txt

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+torch>=2.0.0
+torchvision
+transformers==4.51.3
+gradio==5.27.0
+shap==0.47.2
+tensorflow_hub
+Pillow
+matplotlib
+spacy_streamlit
+streamlit
+scipy