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| 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 | |
| import matplotlib.pyplot as plt | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1") | |
| model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1").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 score_1 | |
| ## | |
| 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()]) | |
| local_plot = shap.plots.text(shap_values[0], display=False) | |
| med = med_score(classifier(x+str(", There is a medication."))[0]) | |
| return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, local_plot, {"Contains Medication": float(med), "No Medications": float(1-med)} | |
| 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.Column(visible=True) as output_col: | |
| label = gr.Label(label = "Predicted Label") | |
| local_plot = gr.HTML(label = 'Shap:') | |
| med = gr.Label(label = "Contains Medication") | |
| submit_btn.click( | |
| main, | |
| [prob1], | |
| [label | |
| ,local_plot, med | |
| ], api_name="adr" | |
| ) | |
| gr.Markdown("### Click on any of the examples below to see to what extent they contain resilience messaging:") | |
| gr.Examples([["I have severe pain."],["I have minor pain."]], [prob1], [label,local_plot, med | |
| ], main, cache_examples=True) | |
| demo.launch() | |