import streamlit as st 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 TFAutoModelForSequenceClassification from transformers import AutoTokenizer, 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("MarkAdamsMSBA24/ADRv2024") model = AutoModelForSequenceClassification.from_pretrained("MarkAdamsMSBA24/ADRv2024").to(device) # build a pipeline object to do 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 # 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) 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'} 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]}{word}" 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)} def main(prob1): text = str(prob1).lower() obj = adr_predict(text) 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.""" with gr.Blocks(title=title) 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) as output_col: label = gr.Label(label = "Predicted Label") with gr.Column(visible=True) as output_col: 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" ) 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()