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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import pandas as pd
import numpy as np
from sklearn.preprocessing import MultiLabelBinarizer
# Check if a GPU is available
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the trained model and tokenizer
@st.cache_resource
def load_model():
model = AutoModelForSequenceClassification.from_pretrained(
"microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract",
num_labels=8, # Adjust based on your label count
problem_type="multi_label_classification"
)
model.load_state_dict(torch.load('best_model_v2.pth'))
model.eval()
tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract")
model = model.to(device) # Move the model to the correct device
return model, tokenizer
@st.cache_resource
def load_mlb():
# Define the classes based on your label set
# classes = ['E11.9', 'I10', 'J45.909', 'M54.5', 'N39.0', '81001.0', '99213.0', '99214.0']
classes = ['81001.0','99213.0','99214.0','E11.9','I10','J45.909','M54.5','N39.0']
# Initialize and fit the MultiLabelBinarizer
mlb = MultiLabelBinarizer(classes=classes)
mlb.fit([classes]) # Fit with the full list of labels as a single sample
return mlb
# # Load MultiLabelBinarizer
# @st.cache_resource
# def load_mlb():
# mlb = MultiLabelBinarizer()
# # mlb.classes_ = np.load('mlb_classes.npy') # Assuming you saved the classes array during training
# mlb = MultiLabelBinarizer(classes=['E11.9', 'I10', 'J45.909', 'M54.5',
# 'N39.0', '81001.0', '99213.0', '99214.0']) # Update with actual labels
# return mlb
model, tokenizer = load_model()
mlb = load_mlb()
# Streamlit UI
st.title("Automated Coding and Billing Prediction")
# st.write("Enter clinical notes to predict ICD and CPT codes.")
# Text input for Clinical Notes
clinical_note = st.text_area("Enter clinical notes to predict ICD and CPT codes")
# Prediction button
if st.button('Predict'):
if clinical_note:
# Tokenize the input clinical note
inputs = tokenizer(clinical_note, truncation=True, padding="max_length", max_length=512, return_tensors='pt')
# Move inputs to the GPU if available
inputs = {key: val.to(device) for key, val in inputs.items()}
# Model inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# Apply sigmoid and threshold the output (0.5 for multi-label classification)
pred_labels = (torch.sigmoid(logits) > 0.5).cpu().numpy()
# Get the predicted ICD and CPT codes
predicted_codes = mlb.inverse_transform(pred_labels)
# Format the results for better display
if predicted_codes:
st.write("**Predicted ICD and CPT Codes:**")
for codes in predicted_codes:
for code in codes:
if code in ['81001.0', '99213.0', '99214.0']: # Adjust based on your CPT code list
st.write(f"- **CPT Code:** {code}")
else:
st.write(f"- **ICD Code:** {code}")
else:
st.write("No codes predicted.")
# else:
# st.write("Please enter clinical notes for prediction.")
# # Prediction button
# if st.button('Predict'):
# if clinical_note:
# # Tokenize the input clinical note
# inputs = tokenizer(clinical_note, truncation=True, padding="max_length", max_length=512, return_tensors='pt')
# # Move inputs to the GPU if available
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# inputs = {key: val.to(device) for key, val in inputs.items()}
# # Model inference
# with torch.no_grad():
# outputs = model(**inputs)
# logits = outputs.logits
# # Apply sigmoid and threshold the output (0.5 for multi-label classification)
# pred_labels = (torch.sigmoid(logits) > 0.5).cpu().numpy()
# # Get the predicted ICD and CPT codes
# predicted_codes = mlb.inverse_transform(pred_labels)
# # Show the results
# st.write("Predicted ICD and CPT Codes:")
# st.write(predicted_codes)
# else:
# st.write("Please enter clinical notes for prediction.")
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