app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch.nn.functional as F
import os

# Load ICD and CPT codes from files
def load_codes_from_files(directory_path, code_type):
    codes = {}
    if os.path.exists(directory_path):
        for file_name in os.listdir(directory_path):
            if file_name.endswith(".txt"):
                file_path = os.path.join(directory_path, file_name)
                with open(file_path, "r", encoding="utf-8") as file:
                    for line in file:
                        parts = line.strip().split(maxsplit=1)
                        if len(parts) == 2:
                            code = parts[0].strip()
                            description = parts[1].strip()
                            codes[code] = description
    else:
        print(f"Directory {directory_path} does not exist!")
    return codes

# Load ICD and CPT codes
ICD_CODES = load_codes_from_files("./codes/icd_txt_files/", "ICD")
CPT_CODES = load_codes_from_files("./codes/cpt_txt_files/", "CPT")

# Check if codes were loaded
if not ICD_CODES or not CPT_CODES:
    raise ValueError("No ICD or CPT codes were loaded. Please check your files and directory structure.")

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModelForSequenceClassification.from_pretrained("emilyalsentzer/Bio_ClinicalBERT", num_labels=len(ICD_CODES))

# 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 for ICD and CPT
    top_k = min(3, len(ICD_CODES))
    top_icd = torch.topk(probs, k=top_k)
    
    # Format results
    result = "Recommended ICD-10 Codes:\n"
    for i, (prob, idx) in enumerate(zip(top_icd.values[0], top_icd.indices[0])):
        code = list(ICD_CODES.keys())[idx.item()]
        description = ICD_CODES[code]
        result += f"{i+1}. {code}: {description} (Confidence: {prob.item():.2f})\n"
    
    result += "\nRecommended CPT Codes:\n"
    for i, (prob, idx) in enumerate(zip(top_icd.values[0], top_icd.indices[0])):
        code = list(CPT_CODES.keys())[idx.item()]
        description = CPT_CODES[code]
        result += f"{i+1}. {code}: {description} (Confidence: {prob.item():.2f})\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."]
    ]
)

# Launch the interface
iface.launch(share=True)