Improve JSON extraction with fallback methods

tinyllama_inference.py

@@ -38,22 +38,17 @@ def extract_json(response_text):
 
 def evaluate_code(question, code):
     prompt = f"""You are an expert code evaluator.
-Evaluate the following solution for the given problem.
-Rate the solution as follows:
-  - 5 stars: Perfect solution; it is correct, efficient, and follows best practices.
-  - 4 stars: Correct solution with minor issues or improvements possible.
-  - 3 stars: Partially correct solution with noticeable issues.
-  - 2 stars: Incorrect solution with some correct elements.
-  - 1 star: Mostly incorrect solution.
-  - 0 stars: Completely incorrect solution.
-Respond with exactly one JSON object (with no extra text) that has exactly two keys:
-  "stars": an integer between 0 and 5,
-  "feedback": a concise string message explaining your rating.
-The JSON must start with '{{' and end with '}}'.
-Do not output any additional text.
+For the following problem and solution, provide exactly one JSON object that contains your evaluation.
+The JSON object must have exactly two keys:
+  "stars": an integer between 0 and 5, where 5 means a perfect solution and 0 means completely incorrect.
+  "feedback": a concise explanation of your rating.
+Do not include any extra text, examples, or multiple responses.
+Only evaluate the code provided below.
 Question: "{question}"
 Solution: "{code}"
 Your response:"""
+    # ... rest of the code remains unchanged ...
+
     
     tokenizer, model = load_model()
     inputs = tokenizer(prompt, return_tensors="pt")