import gradio as gr
import tensorflow as tf
from transformers import TFAutoModel, AutoTokenizer
import os
import numpy as np
import shap
import scipy.special
model_name = 'cardiffnlp/twitter-roberta-base-sentiment-latest'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = tf.keras.models.load_model(
"model.h5",
custom_objects={
'TFRobertaModel': TFAutoModel.from_pretrained(model_name)
}
)
labels = [
'Cardiologist',
'Dermatologist',
'ENT Specialist',
'Gastro-enterologist',
'General-Physicians',
'Neurologist/Gastro-enterologist',
'Ophthalmologist',
'Orthopedist',
'Psychiatrist',
'Respirologist',
'Rheumatologist',
'Rheumatologist/Gastro-enterologist',
'Rheumatologist/Orthopedist',
'Surgeon'
]
seq_len = 152
def prep_data(text):
tokens = tokenizer(
text, max_length=seq_len, truncation=True,
padding='max_length',
add_special_tokens=True,
return_tensors='tf'
)
return {
'input_ids': tokens['input_ids'],
'attention_mask': tokens['attention_mask']
}
def inference(text):
encoded_text = prep_data(text)
probs = model.predict_on_batch(encoded_text)
probabilities = {i:j for i,j in zip(labels, list(probs.flatten()))}
return probabilities
def predictor(x):
input_ids = tokenizer(x, max_length=seq_len, truncation=True, padding='max_length', add_special_tokens=True, return_tensors='tf')['input_ids']
attention_mask = tokenizer(x, max_length=seq_len, truncation=True, padding='max_length', add_special_tokens=True, return_tensors='tf')['attention_mask']
outputs = model.predict([input_ids, attention_mask])
probas = tf.nn.softmax(outputs).numpy()
val = scipy.special.logit(probas[:,1])
return val
def f_batch(x):
val = np.array([])
for i in x:
val = np.append(val, predictor(i))
return val
explainer_roberta = shap.Explainer(f_batch, tokenizer)
shap_values = explainer_roberta(["When I remember her I feel down"])
def get_shap_data(input_text):
shap_values = explainer_roberta([input_text])
html_shap_content = shap.plots.text(shap_values, display=False)
return html_shap_content
css = """
textarea {
background-color: #00000000;
border: 1px solid #6366f160;
}
"""
with gr.Blocks(title="SpecX", css=css, theme=gr.themes.Soft()) as demo:
with gr.Row():
textmd = gr.Markdown('''
SpecX: Find the Right Specialist For Your Symptoms!
''')
with gr.Row():
with gr.Column(scale=1, min_width=600):
text_box = gr.Textbox(label="Explain your problem in one sentence.")
with gr.Row():
submit_btn = gr.Button("Submit", elem_id="warningk", variant='primary')
submit_btn_shap = gr.Button("Get SHAP Analysis", variant='secondary')
examples = gr.Examples(examples=[
"When I remember her I feel down",
"The area around my heart doesn't feel good.",
"I have a split on my thumb that will not heal."
], inputs=text_box)
with gr.Column():
label = gr.Label(num_top_classes=4, label="Recommended Specialist")
gr.Markdown("## SHAP Analysis")
shap_content_box = gr.HTML()
submit_btn.click(inference, inputs=text_box, outputs=label)
submit_btn_shap.click(get_shap_data, inputs=text_box, outputs=shap_content_box)
with gr.Row():
textmd1 = gr.Markdown('''
''')
with gr.Row():
with gr.Column():
textmd1 = gr.Markdown('''