README.md

---
license: mit
---
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch
import numpy as np

# Define the model and tokenizer
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)

# Define the key words and their corresponding labels
key_words = ['ascites', 'cirrhosis', 'liver disease']
labels = [0, 1]

# Define a function to preprocess the input text
def preprocess_text(text):
    inputs = tokenizer.encode_plus(
        text,
        add_special_tokens=True,
        max_length=512,
        return_attention_mask=True,
        return_tensors='pt'
    )
    return inputs

# Define a function to make predictions
def make_prediction(text):
    inputs = preprocess_text(text)
    outputs = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
    logits = outputs.logits
    probabilities = torch.nn.functional.softmax(logits, dim=1)
    predicted_class = torch.argmax(probabilities)
    return predicted_class.item()

# Define a function to get the clinic that the referral should be directed to
def get_clinic(text):
    predicted_class = make_prediction(text)
    if predicted_class == 1:
        return 'Liver Clinic'
    else:
        return 'Kidney Clinic'

# Define the model's configuration
model_config = {
   'model_type': 'distilbert',
    'num_labels': 2,
    'key_words': key_words,
    'labels': labels
}

# Define the model's metadata
model_metadata = {
    'name': 'Referral Clinic Classifier',
    'description': 'A model that classifies referrals to either the Liver Clinic or Kidney Clinic based on the presence of certain key words.',
    'author': 'Your Name',
   'version': '1.0'
}

# Train the model
train_data = [
    ('Patient has ascites and cirrhosis.', 1),
    ('Patient has liver disease.', 1),
    ('Patient has kidney disease.', 0),
    ('Patient has liver failure.', 1),
    ('Patient has kidney failure.', 0),
]

for text, label in train_data:
    inputs = preprocess_text(text)
    labels = torch.tensor(label)
    outputs = model(inputs['input_ids'], attention_mask=inputs['attention_mask'], labels=labels)
    loss = outputs.loss
    model.zero_grad()
    loss.backward()
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
    optimizer.step()

# Save the model to a file
torch.save(model.state_dict(),'referral_clinic_classifier.pth')
with open('model_config.json', 'w') as f:
    json.dump(model_config, f)
with open('model_metadata.json', 'w') as f:
    json.dump(model_metadata, f)