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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 torch |
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import tensorflow as tf |
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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 |
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tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1") |
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model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1") |
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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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pred = transformers.pipeline("text-classification", model=model, |
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tokenizer=tokenizer, device=0, return_all_scores=True) |
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explainer = shap.Explainer(pred) |
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shap_values = explainer([x]) |
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shap_plot = shap.plots.text(shap_values) |
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return {"Severe Reaction": float(scores.numpy()[1]), "Non-severe Reaction": float(scores.numpy()[0])}, shap_plot |
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def main(text): |
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text = str(text).lower() |
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obj = adr_predict(text) |
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return obj[0],obj[1] |
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title = "Welcome to **ADR Detector** 🪐" |
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description1 = """ |
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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. |
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""" |
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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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text = 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.Column(visible=True) as output_col: |
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label = gr.Label(label = "Predicted Label") |
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shap_plot = gr.HighlightedText(label="Word Scores",combine_adjacent=False) |
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submit_btn.click( |
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main, |
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[text], |
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[label,shap_plot], api_name="adr" |
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) |
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gr.Markdown("### Click on any of the examples below to see to what extent they contain resilience messaging:") |
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gr.Examples([["I have minor pain."],["I have severe pain."]], [text], [label,shap_plot], main, cache_examples=True) |
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demo.launch() |
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