handle no fields

app.py

@@ -94,29 +94,31 @@ def infer(code):
     )
 
     field_prompt_result, fields, field_helper_result = field_prompt(code)
-    field_input_ids = tokenizer.encode(field_prompt_result, return_tensors="pt").cuda()[
-        :, : 8192 - 1024
-    ]
+    if len(fields) == 0:
+        field_output = "No fields"
+    else:
+        field_input_ids = tokenizer.encode(field_prompt_result, return_tensors="pt").cuda()[
+            :, : 8192 - 1024
+        ]
+
+        field_output = fielddecoder_model.generate(
+            input_ids=field_input_ids,
+            max_new_tokens=1024,
+            num_beams=4,
+            num_return_sequences=1,
+            do_sample=False,
+            early_stopping=False,
+            pad_token_id=0,
+            eos_token_id=0,
+        )[0]
+        field_output = tokenizer.decode(
+            field_output[var_input_ids.size(1) :],
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=True,
+        )
 
-    field_output = fielddecoder_model.generate(
-        input_ids=field_input_ids,
-        max_new_tokens=1024,
-        num_beams=4,
-        num_return_sequences=1,
-        do_sample=False,
-        early_stopping=False,
-        pad_token_id=0,
-        eos_token_id=0,
-    )[0]
-    field_output = tokenizer.decode(
-        field_output[var_input_ids.size(1) :],
-        skip_special_tokens=True,
-        clean_up_tokenization_spaces=True,
-    )
-
-    var_output = first_var + ":" + var_output
-    if len(fields) > 0:
         field_output = fields[0] + ":" + field_output
+    var_output = first_var + ":" + var_output
     return var_output, field_output, varstring