Update app.py

app.py

@@ -3,32 +3,48 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from smolagents import CodeAgent, HfApiEngine, DuckDuckGoSearchTool
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+# --- Smart Agent Definition ---
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Initializing powerful CodeAgent for evaluation...")
+        # Using a highly capable reasoning model via serverless API
+        # It automatically looks for the HF_TOKEN secret in your environment variables
+        self.engine = HfApiEngine(model_id="meta-llama/Llama-3.3-70B-Instruct")
+        
+        # Initializing the CodeAgent with web search capabilities
+        # A CodeAgent can write and execute Python code blocks to solve complex reasoning/math
+        self.agent = CodeAgent(
+            tools=[DuckDuckGoSearchTool()], 
+            engine=self.engine,
+            max_steps=5  # Cap steps to prevent timeouts and infinite loops
+        )
+
     def __call__(self, question: str) -> str:
+        print(f"\n--- New Evaluation Task ---")
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        try:
+            # Run the agent through its thinking steps to find the correct answer
+            result = self.agent.run(question)
+            print(f"Agent successfully resolved answer: {str(result)[:100]}...")
+            return str(result)
+        except Exception as e:
+            print(f"Error during agent execution: {e}")
+            return "Error calculating answer."
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -38,13 +54,14 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate Agent
     try:
         agent = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+
+    # Link pointing toward your public codebase 
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
@@ -54,14 +71,14 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
-        if not questions_data:
+        if not questions_data: 
              print("Fetched questions list is empty.")
              return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
     except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
+    except requests.exceptions.JSONDecodeError as e: 
          print(f"Error decoding JSON response from questions endpoint: {e}")
          print(f"Response text: {response.text[:500]}")
          return f"Error decoding server response for questions: {e}", None
@@ -83,7 +100,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             submitted_answer = agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-        except Exception as e:
+        except Exception as e: 
              print(f"Error running agent on task {task_id}: {e}")
              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
 
@@ -139,10 +156,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# Smart Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
@@ -155,13 +171,9 @@ with gr.Blocks() as demo:
         This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
         """
     )
-
     gr.LoginButton()
-
     run_button = gr.Button("Run Evaluation & Submit All Answers")
-
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
@@ -171,24 +183,20 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
+    space_id_startup = os.getenv("SPACE_ID") 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
         print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    if space_id_startup: 
         print(f"✅ SPACE_ID found: {space_id_startup}")
         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
     else:
         print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
     print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    
+    print("Launching Gradio Interface for Smart Agent Evaluation...")
     demo.launch(debug=True, share=False)