Update app.py

app.py

@@ -65,177 +65,178 @@ class BasicAgent:
             print("Detected likely reversed riddle.")
             return self.solve_riddle(question)
         return "FINAL ANSWER: NOT_A_RIDDLE"
+        
     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}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your 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)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        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:
-         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
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        try:
-            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:
-             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}"})
-
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        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(
-            """
-            **Instructions:**
-            1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-            2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-            3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-            ---
-            **Disclaimers:**
-            Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-            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.
-            """
-        )
+        """
+        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}"
+            print(f"User logged in: {username}")
+        else:
+            print("User not logged in.")
+            return "Please Login to Hugging Face with the button.", None
     
-        gr.LoginButton()
+        api_url = DEFAULT_API_URL
+        questions_url = f"{api_url}/questions"
+        submit_url = f"{api_url}/submit"
     
-        run_button = gr.Button("Run Evaluation & Submit All Answers")
+        # 1. Instantiate Agent ( modify this part to create your 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)
+        agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+        print(agent_code)
     
-        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)
+        # 2. Fetch Questions
+        print(f"Fetching questions from: {questions_url}")
+        try:
+            response = requests.get(questions_url, timeout=15)
+            response.raise_for_status()
+            questions_data = response.json()
+            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:
+             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
+        except Exception as e:
+            print(f"An unexpected error occurred fetching questions: {e}")
+            return f"An unexpected error occurred fetching questions: {e}", None
     
-        run_button.click(
-            fn=run_and_submit_all,
-            outputs=[status_output, results_table]
-        )
+        # 3. Run your Agent
+        results_log = []
+        answers_payload = []
+        print(f"Running agent on {len(questions_data)} questions...")
+        for item in questions_data:
+            task_id = item.get("task_id")
+            question_text = item.get("question")
+            if not task_id or question_text is None:
+                print(f"Skipping item with missing task_id or question: {item}")
+                continue
+            try:
+                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:
+                 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}"})
     
-    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
+        if not answers_payload:
+            print("Agent did not produce any answers to submit.")
+            return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
     
-        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?).")
+        # 4. Prepare Submission 
+        submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+        status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+        print(status_update)
     
-        if space_id_startup: # Print repo URLs if SPACE_ID is found
-            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.")
+        # 5. Submit
+        print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+        try:
+            response = requests.post(submit_url, json=submission_data, timeout=60)
+            response.raise_for_status()
+            result_data = response.json()
+            final_status = (
+                f"Submission Successful!\n"
+                f"User: {result_data.get('username')}\n"
+                f"Overall Score: {result_data.get('score', 'N/A')}% "
+                f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+                f"Message: {result_data.get('message', 'No message received.')}"
+            )
+            print("Submission successful.")
+            results_df = pd.DataFrame(results_log)
+            return final_status, results_df
+        except requests.exceptions.HTTPError as e:
+            error_detail = f"Server responded with status {e.response.status_code}."
+            try:
+                error_json = e.response.json()
+                error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+            except requests.exceptions.JSONDecodeError:
+                error_detail += f" Response: {e.response.text[:500]}"
+            status_message = f"Submission Failed: {error_detail}"
+            print(status_message)
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
+        except requests.exceptions.Timeout:
+            status_message = "Submission Failed: The request timed out."
+            print(status_message)
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
+        except requests.exceptions.RequestException as e:
+            status_message = f"Submission Failed: Network error - {e}"
+            print(status_message)
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
+        except Exception as e:
+            status_message = f"An unexpected error occurred during submission: {e}"
+            print(status_message)
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
     
-        print("-"*(60 + len(" App Starting ")) + "\n")
     
-        print("Launching Gradio Interface for Basic Agent Evaluation...")
-        demo.launch(debug=True, share=False)
+        # --- Build Gradio Interface using Blocks ---
+        with gr.Blocks() as demo:
+            gr.Markdown("# Basic Agent Evaluation Runner")
+            gr.Markdown(
+                """
+                **Instructions:**
+                1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+                2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+                3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+                ---
+                **Disclaimers:**
+                Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+                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(
+                fn=run_and_submit_all,
+                outputs=[status_output, results_table]
+            )
+        
+        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
+        
+            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
+                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...")
+            demo.launch(debug=True, share=False)