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import streamlit as st
import torch
import json
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np
import re
# ----------------------------------------------------------------------
# Text Preprocessing (same as during training)
# ----------------------------------------------------------------------
def preprocess_text(text: str) -> str:
text = text.lower()
text = re.sub(r"\[\*\*.*?\*\*\]", " ", text)
text = re.sub(r"([!?.,])\1+", r"\1", text)
text = re.sub(r"[\r\n\t]+", " ", text)
text = re.sub(r"\s+", " ", text)
text = text.strip()
return text
# ----------------------------------------------------------------------
# Load Trained Model and Artifacts
# ----------------------------------------------------------------------
@st.cache_resource
def load_trained_model(model_dir: str):
model = AutoModelForSequenceClassification.from_pretrained(model_dir)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_dir)
with open(f"{model_dir}/mlb_classes.json", "r") as f:
top_codes_list = json.load(f)
mlb = MultiLabelBinarizer(classes=top_codes_list)
mlb.fit([[]])
return model, tokenizer, mlb
# ----------------------------------------------------------------------
# Predict ICD-9 Codes
# ----------------------------------------------------------------------
def predict_icd9(input_text: str, model, tokenizer, mlb, max_length=512, threshold=0.5):
processed_text = preprocess_text(input_text)
inputs = tokenizer(
processed_text,
return_tensors="pt",
truncation=True,
max_length=max_length,
padding="max_length"
)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).squeeze().cpu().numpy()
y_pred = (probs > threshold).astype(int)
predicted_codes = mlb.inverse_transform(np.array([y_pred]))
return predicted_codes[0]
# ----------------------------------------------------------------------
# Streamlit App
# ----------------------------------------------------------------------
st.title("ICD-9 Code Prediction")
model_dir = "./final_mode4l"
st.sidebar.header("Model Settings")
threshold = st.sidebar.slider("Prediction Threshold", min_value=0.1, max_value=1.0, value=0.5, step=0.1)
st.write("Enter clinical text below to predict ICD-9 codes.")
input_text = st.text_area("Clinical Text", height=200)
if st.button("Predict"):
if not input_text.strip():
st.error("Please enter valid clinical text.")
else:
st.write("Loading model...")
model, tokenizer, mlb = load_trained_model(model_dir)
st.write("Predicting...")
predicted_codes = predict_icd9(input_text, model, tokenizer, mlb, threshold=threshold)
if predicted_codes:
st.success("Predicted ICD-9 Codes:")
st.write(predicted_codes)
else:
st.warning("No codes were predicted. Try lowering the threshold or using a different input.")
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