Update README.md

README.md

@@ -1,3 +1,84 @@
----
-license: mit
----
+---
+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)