Update app.py

app.py

@@ -10,7 +10,23 @@ model.eval()
 def predict(input_text):
     # Your preprocessing and prediction code
     # output = model(input_text)
-    return "Predicted output"  # Replace with actual prediction logic
+     tokens = tokenize(input_text)  # Define this function based on your tokenizer
+    input_tensor = torch.tensor(tokens).unsqueeze(0)  # Add batch dimension
 
+    with torch.no_grad():
+        output = model(input_tensor)
+    
+    # Postprocess output
+    predicted_tokens = torch.argmax(output, dim=-1).squeeze().tolist()
+    predicted_text = detokenize(predicted_tokens)  # Define this function based on your tokenizer
+    
+    return predicted_text  # Replace with actual prediction logic
+def tokenize(text):
+    # Example tokenizer function
+    return [ord(char) for char in text]  # Replace with actual tokenizer
+
+def detokenize(tokens):
+    # Example detokenizer function
+    return ''.join([chr(token) for token in tokens])  # Replace with actual detokenizer
 interface = gr.Interface(fn=predict, inputs="text", outputs="text")
 interface.launch()