Update app.py

app.py

@@ -15,6 +15,8 @@ from tavily import TavilyClient
 # Load environment variables
 load_dotenv()
 
+
+# Agent
 class GAIAAgent:
     def __init__(self):
         print("Initializing GAIA Agent...")
@@ -36,8 +38,14 @@ class GAIAAgent:
         # Initialize tools
         self.tools = self._initialize_tools()
         
-        # GAIA-specific system prompt
-        self.system_prompt = """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""
+        # Load system prompt from file
+        try:
+            with open('system_prompt.txt', 'r') as f:
+                self.system_prompt = f.read()
+            print("✅ System prompt loaded successfully")
+        except Exception as e:
+            self.system_prompt = """You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""
+            print(f"⚠️ Couldn't load system prompt: {str(e)}. Using fallback prompt.")
         
         # Create agent
         self.agent = ReActAgent.from_tools(
@@ -155,31 +163,193 @@ class GAIAAgent:
             }
 
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+#def run_and_submit_all(profile: gr.OAuthProfile | None):
+#    """
+#    Fetches all questions, runs the GAIAAgent on them, submits all answers,
+#    and displays the results.
+#    """
+#    space_id = os.getenv("SPACE_ID")
+#
+#    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 = "https://agents-course-unit4-scoring.hf.space"
+#    questions_url = f"{api_url}/questions"
+#    submit_url = f"{api_url}/submit"
+#
+#    # 1. Instantiate Agent
+#    try:
+#        agent = GAIAAgent()
+#    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:
+#            agent_response = agent(question_text)
+#            answers_payload.append({
+#                "task_id": task_id,
+#                "model_answer": agent_response["model_answer"],
+#                "reasoning_trace": agent_response["reasoning_trace"]
+#            })
+#            results_log.append({
+#                "Task ID": task_id,
+#                "Question": question_text,
+#                "Submitted Answer": agent_response["model_answer"],
+#                "Reasoning": agent_response["reasoning_trace"]
+#            })
+#        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}",
+#                "Reasoning": f"Error occurred: {str(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 Exception as e:
+#        status_message = f"Submission Failed: {str(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("# GAIA Agent Evaluation Runner")
+#    gr.Markdown(
+#        """
+#        **Instructions:**
+#        1. Log in to your Hugging Face account using the button below.
+#        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+#        """
+#    )
+#
+#    gr.LoginButton()
+#
+#    run_button = gr.Button("Run Evaluation & Submit All Answers")
+#
+#    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+#    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)
+#    space_host_startup = os.getenv("SPACE_HOST")
+#    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")
+#
+#    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")
+#
+#    print("-"*(60 + len(" App Starting ")) + "\n")
+#
+#    print("Launching Gradio Interface for GAIA Agent Evaluation...")
+#    demo.launch(debug=True, share=True)
+
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the GAIAAgent on them, submits all answers,
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
-    space_id = os.getenv("SPACE_ID")
+    # --- 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.")
         return "Please Login to Hugging Face with the button.", None
 
-    api_url = "https://agents-course-unit4-scoring.hf.space"
+    api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = GAIAAgent()
+        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)
 
@@ -190,12 +360,19 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         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("Fetched questions list is empty.")
+             return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
-    except Exception as e:
+    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 = []
@@ -208,37 +385,19 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            agent_response = agent(question_text)
-            answers_payload.append({
-                "task_id": task_id,
-                "model_answer": agent_response["model_answer"],
-                "reasoning_trace": agent_response["reasoning_trace"]
-            })
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text,
-                "Submitted Answer": agent_response["model_answer"],
-                "Reasoning": agent_response["reasoning_trace"]
-            })
+            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}",
-                "Reasoning": f"Error occurred: {str(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
-    }
+    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)
 
@@ -258,21 +417,47 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         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"Submission Failed: {str(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 ---
+# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent Evaluation Runner")
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
-        1. Log in to your Hugging Face account using the button below.
-        2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+        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.
         """
     )
 
@@ -281,6 +466,7 @@ with gr.Blocks() as demo:
     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(
@@ -290,19 +476,24 @@ 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")
+    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:
+    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 GAIA Agent Evaluation...")
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)