Enhance SYSTEM_PROMPT in SmolAgent for clarity and accuracy, update model ID, and simplify response handling.

app.py

@@ -10,20 +10,21 @@ from smolagents import CodeAgent, InferenceClientModel, DuckDuckGoSearchTool
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 HF_TOKEN = os.getenv("HF_TOKEN")
 SYSTEM_PROMPT = """
-You are an expert assistant answering questions for evaluation where answers are checked using exact string match.
+You are a helpful assistant answering evaluation questions that will be scored using exact string match.
 
-Rules:
+Instructions:
 - Return ONLY the final answer on a single line.
-- Do NOT include reasoning or extra text.
-- If the answer is a number, return the plain number with no commas, no currency symbols, and no units unless explicitly required.
-- If the answer is a string, use lowercase, no articles, no abbreviations, and digits written in plain text unless specified.
-- If the answer is a comma-separated list, follow the above rules for each element, with no spaces after commas.
+- Do NOT include explanations or reasoning.
 - Do NOT write "Answer:" or "FINAL ANSWER:" before your response.
+- If the answer is a number, return the plain number with no commas, currency symbols, or units unless explicitly required.
+- If the answer is a string, use lowercase, no articles, no abbreviations, and digits in plain text unless specified otherwise.
+- If the answer is a comma-separated list, follow the above rules for each element with no spaces after commas.
 - If unsure, guess concisely.
 
-Always strictly follow these instructions to ensure exact match.
+Always strictly follow these instructions to maximize exact match accuracy.
 """
 
+
 # --- Smol Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class SmolAgent:
@@ -37,7 +38,7 @@ class SmolAgent:
         
         # 3. Initialize the SmolLM model
         model = InferenceClientModel(
-            model_id="HuggingFaceTB/SmolLM-360M-Instruct",
+            model_id="HuggingFaceTB/SmolLM-1.7B-Instruct",
             token=hf_token,
         )
         
@@ -50,30 +51,14 @@ class SmolAgent:
         print("SmolAgent initialized")
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        print(f"\n🪐 Running on question:\n{question}\n")
         try:
-            # The system prompt is already set, just pass the question.
-            full_response = self._agent.run(question)
-            print(f"Agent full response: {full_response}")
-
-            # Parse the response to extract the final answer
-            if "FINAL ANSWER:" in full_response:
-                # Split and get the part after "FINAL ANSWER:"
-                final_answer_part = full_response.split("FINAL ANSWER:")[1]
-                # Remove brackets if they exist and strip whitespace
-                final_answer = final_answer_part.strip()
-                if final_answer.startswith('[') and final_answer.endswith(']'):
-                    final_answer = final_answer[1:-1]
-                
-                print(f"Agent returning parsed answer: {final_answer}")
-                return final_answer
-            else:
-                print("Warning: 'FINAL ANSWER:' not found in response. Returning full response.")
-                return full_response
-
+            answer = self._agent.run(prompt)
+            print(f"Agent returning answer: {answer}")
+            return str(answer)
         except Exception as e:
-            print(f"Error running agent: {e}")
-            return f"AGENT ERROR: {e}"
+            print(f"Agent error: {e}")
+            return f"Agent error: {e}"
 
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):