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naufderheide
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Parent(s):
878af56
Update app.py
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app.py
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import gradio as gr
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demo.launch()
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import streamlit as st
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import gradio as gr
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import shap
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import numpy as np
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import scipy as sp
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import torch
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import tensorflow as tf
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import transformers
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from transformers import pipeline
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from transformers import RobertaTokenizer, RobertaModel
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from transformers import AutoModelForSequenceClassification
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from transformers import TFAutoModelForSequenceClassification
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from transformers import AutoTokenizer, AutoModelForTokenClassification
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import matplotlib.pyplot as plt
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import sys
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import csv
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csv.field_size_limit(sys.maxsize)
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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tokenizer = AutoTokenizer.from_pretrained("MSBA24-Team8/ADRv2024")
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model = AutoModelForSequenceClassification.from_pretrained("MSBA24-Team8/ADRv2024").to(device)
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# build a pipeline object to do predictions
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pred = transformers.pipeline("text-classification", model=model,
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tokenizer=tokenizer, return_all_scores=True)
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explainer = shap.Explainer(pred)
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##
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# classifier = transformers.pipeline("text-classification", model = "cross-encoder/qnli-electra-base")
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# def med_score(x):
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# label = x['label']
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# score_1 = x['score']
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# return round(score_1,3)
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# def sym_score(x):
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# label2sym= x['label']
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# score_1sym = x['score']
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# return round(score_1sym,3)
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ner_tokenizer = AutoTokenizer.from_pretrained("d4data/biomedical-ner-all")
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ner_model = AutoModelForTokenClassification.from_pretrained("d4data/biomedical-ner-all")
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ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple") # pass device=0 if using gpu
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#
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def adr_predict(x):
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encoded_input = tokenizer(x, return_tensors='pt')
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output = model(**encoded_input)
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scores = output[0][0].detach().numpy()
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scores = tf.nn.softmax(scores)
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shap_values = explainer([str(x).lower()])
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# # Find the index of the class you want as the default reference (e.g., 'label_1')
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# label_1_index = np.where(np.array(explainer.output_names) == 'label_1')[0][0]
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# # Plot the SHAP values for a specific instance in your dataset (e.g., instance 0)
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# shap.plots.text(shap_values[label_1_index][0])
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local_plot = shap.plots.text(shap_values[0], display=False)
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# med = med_score(classifier(x+str(", There is a medication."))[0])
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# sym = sym_score(classifier(x+str(", There is a symptom."))[0])
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res = ner_pipe(x)
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entity_colors = {
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'Severity': 'red',
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'Sign_symptom': 'green',
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'Medication': 'lightblue',
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'Age': 'yellow',
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'Sex':'yellow',
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'Diagnostic_procedure':'gray',
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'Biological_structure':'silver'}
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htext = ""
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prev_end = 0
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for entity in res:
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start = entity['start']
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end = entity['end']
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word = entity['word'].replace("##", "")
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color = entity_colors[entity['entity_group']]
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htext += f"{x[prev_end:start]}<mark style='background-color:{color};'>{word}</mark>"
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prev_end = end
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htext += x[prev_end:]
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return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot,htext
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# ,{"Contains Medication": float(med), "No Medications": float(1-med)} , {"Contains Symptoms": float(sym), "No Symptoms": float(1-sym)}
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def main(prob1):
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text = str(prob1).lower()
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obj = adr_predict(text)
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return obj[0],obj[1],obj[2]
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title = "Welcome to **ADR Detector** 🪐"
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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."""
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with gr.Blocks(title=title) as demo:
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gr.Markdown(f"## {title}")
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gr.Markdown(description1)
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gr.Markdown("""---""")
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prob1 = gr.Textbox(label="Enter Your Text Here:",lines=2, placeholder="Type it here ...")
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submit_btn = gr.Button("Analyze")
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with gr.Row():
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with gr.Column(visible=True) as output_col:
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label = gr.Label(label = "Predicted Label")
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with gr.Column(visible=True) as output_col:
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local_plot = gr.HTML(label = 'Shap:')
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htext = gr.HTML(label="NER")
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# med = gr.Label(label = "Contains Medication")
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# sym = gr.Label(label = "Contains Symptoms")
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submit_btn.click(
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main,
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[prob1],
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[label
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,local_plot, htext
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# , med, sym
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], api_name="adr"
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)
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with gr.Row():
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gr.Markdown("### Click on any of the examples below to see how it works:")
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gr.Examples([["A 35 year-old male had severe headache after taking Aspirin. The lab results were normal."],
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["A 35 year-old female had minor pain in upper abdomen after taking Acetaminophen."]],
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[prob1], [label,local_plot, htext
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# , med, sym
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], main, cache_examples=True)
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demo.launch()
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