app.py

import streamlit as st
import gradio as gr
import shap
import numpy as np
import scipy as sp
import torch
import transformers
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification

import matplotlib.pyplot as plt
import sys
import csv

csv.field_size_limit(sys.maxsize)

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 for predictions
pred = transformers.pipeline("text-classification", model=model, 
                             tokenizer=tokenizer, return_all_scores=True)

explainer = shap.Explainer(pred)

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")

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)
   
    shap_values = explainer([str(x).lower()])
    local_plot = shap.plots.text(shap_values[0], display=False)

    res = ner_pipe(x)
    entity_colors = {
        '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']
        word = entity['word'].replace("##", "")
        color = entity_colors[entity['entity_group']]
        
        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

def main(prob1):
    text = str(prob1).lower()
    obj = adr_predict(text)
    return obj[0], obj[1], obj[2]

title = "Welcome to **ADR Tracker**"
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."

css = """
body { font-family: 'Roboto', sans-serif; background-color: #333; color: #87CEEB; }
h1, h2, h3, h4, h5, h6, p, label, .markdown { color: #87CEEB; } /* Ensuring that all text elements are consistently light blue */
.textbox { width: 100%; border-radius: 10px; border: 1px solid #ccc; background-color: white; color: black; }
.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...")
    submit_btn = gr.Button("Analyze")

    with gr.Row():
        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")

    submit_btn.click(
        main,
        [prob1],
        [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], main, cache_examples=True)

demo.launch()