Upload app.py

app.py

@@ -121,17 +121,19 @@ def _classify_generate(question: str, solution: str) -> Tuple[str, str, str]:
     # Prompt must match the format you used in tuning
     prompt = (
         "You are a mathematics tutor.\n"
-        "You are given a math word problem and a student's solution. Decide whether the solution is correct.\n\n"
-        "- Correct  = all reasoning and calculations are correct.\n"
-        "- Conceptual Error = reasoning is wrong.\n"
-        "- Computational Error = reasoning okay but arithmetic off.\n\n"
+        "You are given a math word problem and a student's solution. "
+        "Decide whether the solution is correct.\n\n"
+        "- Correct            = all reasoning and calculations are correct.\n"
+        "- Conceptual Error   = reasoning is wrong.\n"
+        "- Computational Error= reasoning okay but arithmetic off.\n\n"
         "Reply with ONLY one of these JSON lines:\n"
         '{"verdict": "correct"}\n'
         '{"verdict": "conceptual"}\n'
-        '{"verdict": "computational"}\n\n"
+        '{"verdict": "computational"}\n\n'
         f"Question: {question}\n\nSolution:\n{solution}\n\nAnswer:"
     )
 
+  
     inputs = tokenizer(prompt, return_tensors="pt").to(device)
     with torch.no_grad():
         out_ids = model.generate(
@@ -139,8 +141,11 @@ def _classify_generate(question: str, solution: str) -> Tuple[str, str, str]:
             max_new_tokens=32,
             pad_token_id=tokenizer.eos_token_id,
         )
-    generated = tokenizer.decode(out_ids[0][inputs["input_ids"].shape[1]:],
-                                 skip_special_tokens=True).strip()
+    generated = tokenizer.decode(
+        out_ids[0][inputs["input_ids"].shape[1]:],
+        skip_special_tokens=True,
+    ).strip()
+
 
     # Try to parse last JSON line
     verdict = "Unparsed"