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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
# Mock ICD and CPT data (replace with actual API calls or datasets)
def fetch_icd_codes(query):
# Mock ICD codes for demonstration
return [
{"code": "R50.9", "description": "Fever, unspecified"},
{"code": "A00", "description": "Cholera"},
{"code": "J06.9", "description": "Acute upper respiratory infection, unspecified"}
]
def fetch_cpt_codes(query):
# Mock CPT codes for demonstration
return [
{"code": "99213", "description": "Office or other outpatient visit"},
{"code": "87804", "description": "Infectious agent detection by immunoassay"},
{"code": "85025", "description": "Complete blood count (CBC)"}
]
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModelForSequenceClassification.from_pretrained("emilyalsentzer/Bio_ClinicalBERT", num_labels=1000) # Adjust num_labels as needed
# Prediction function
def predict_codes(text):
if not text.strip():
return "Please enter a medical summary."
# Tokenize input
inputs = tokenizer(
text,
return_tensors="pt",
max_length=512,
truncation=True,
padding=True
)
# Get predictions
model.eval()
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# Get probabilities
probs = F.softmax(logits, dim=1)
# Get top 3 predictions
top_k = torch.topk(probs, k=3)
# Fetch ICD and CPT codes using mock functions
icd_results = fetch_icd_codes(text)
cpt_results = fetch_cpt_codes(text)
# Format results
result = "Recommended ICD-10 Codes:\n"
for i, code in enumerate(icd_results[:3]): # Show top 3 ICD codes
result += f"{i+1}. {code.get('code', 'Unknown')}: {code.get('description', 'No description')}\n"
result += "\nRecommended CPT Codes:\n"
for i, code in enumerate(cpt_results[:3]): # Show top 3 CPT codes
result += f"{i+1}. {code.get('code', 'Unknown')}: {code.get('description', 'No description')}\n"
return result
# Create Gradio interface
iface = gr.Interface(
fn=predict_codes,
inputs=gr.Textbox(
lines=5,
placeholder="Enter medical summary here...",
label="Medical Summary"
),
outputs=gr.Textbox(
label="Predicted Codes",
lines=10
),
title="AutoRCM - Medical Code Predictor",
description="Enter a medical summary to get recommended ICD-10 and CPT codes.",
examples=[
["Patient presents with blood pressure 150/90. Complains of occasional headaches. History of hypertension."],
["Patient has elevated blood sugar levels. A1C is 7.8. History of type 2 diabetes."],
["Patient complains of chronic lower back pain, worse with movement. No radiation to legs."]
],
allow_flagging="never" # Disable caching
)
# Launch the interface
iface.launch(share=True) |