Update app.py

app.py

@@ -24,8 +24,8 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name, num_label
 
 # Define a tokenization function
 def preprocess_function(examples):
-    # Concatenate relevant columns into a single input string for tokenization
-    inputs = examples["age"].astype(str) + " " + examples["bmi"].astype(str) + " " + examples["HbA1c_level"].astype(str)
+    # Convert each feature to a string and concatenate them
+    inputs = [f"{age} {bmi} {hba1c}" for age, bmi, hba1c in zip(examples["age"], examples["bmi"], examples["HbA1c_level"])]
     return tokenizer(inputs, padding="max_length", truncation=True, max_length=32)
 
 # Apply the tokenization function to the datasets
@@ -53,3 +53,4 @@ trainer = Trainer(
 # Train and evaluate the model
 trainer.train()
 trainer.evaluate()
+