Update app.py

app.py

@@ -25,16 +25,16 @@ async def health_check():
 
 @app.post("/evaluate")
 async def evaluate_code(request: EvalRequest):
-    prompt = f"Task Description:\n{request.task_description}\n\nSubmitted Code:\n{request.python_code}\n\nEvaluate the code against the task. Assign a final score out of 10. Keep your feedback concise and helpful."
+    # Removed the extra instructions here to save tokens and speed up reading time
+    prompt = f"{request.task_description}\n\nStudent Code:\n{request.python_code}"
     
     response = llm.create_chat_completion(
         messages=[
-            # Framing the model specifically for grading student submissions
-            {"role": "system", "content": "You are an expert Python instructor. You evaluate student code submissions accurately, checking for logical correctness and task completion."},
+            {"role": "system", "content": "You are a Frendly Python grader. Output only valid JSON."},
             {"role": "user", "content": prompt}
         ],
-        max_tokens=250, # Limit response length to keep API fast
-        temperature=0.2 # Low temperature for consistent scoring
+        max_tokens=250, 
+        temperature=0.1 # Lowered slightly for stricter JSON formatting
     )
     
     return {"evaluation": response['choices'][0]['message']['content']}