Update app.py

app.py

@@ -93,9 +93,10 @@ model = BertClassifier(base_model, log_reg, num_labels = N_UNIQUE_CLASSES)
 # Define a function to process the DNA sequence
 def analyze_dna(sequence):
     # Preprocess the input sequence
-    inputs = tokenizer(sequence, return_tensors='pt')
+    inputs = tokenizer(sequence, truncation=True, padding='max_length', max_length=512, return_tensors="pt", return_token_type_ids=False)
+
     # Get model predictions
-    outputs = model(**inputs)
+    _, outputs = model(**inputs)
     
     # Convert logits to probabilities
     probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1).squeeze().tolist()
@@ -107,7 +108,7 @@ def analyze_dna(sequence):
     # Prepare the output as a list of tuples (class_index, probability)
     result = [(index, prob) for index, prob in zip(top_5_indices, top_5_probs)]
     
-    return result
+    return probabilities
 
 # Create a Gradio interface
 demo = gr.Interface(fn=analyze_dna, inputs="text", outputs="json")