Update app.py

app.py

@@ -33,14 +33,14 @@ llm = ChatGoogleGenerativeAI(
 llm_with_tools = llm.bind_tools(toolset)
 
 sys_prompt_file = open("sys_prompt.txt")
-SYSTEM_PROMPT = sys_prompt_file.read()
+sys_prompt = sys_prompt_file.read()
 
 class AgentState(TypedDict):
     messages: Annotated[list[AnyMessage], add_messages]
 
 def assistant(state: AgentState):
     return {
-        "messages": [llm_with_tools.invoke(state["messages"])],
+        "messages": [llm_with_tools.invoke([sys_prompt]+state["messages"])],
     }
 
 builder = StateGraph(AgentState)
@@ -57,250 +57,141 @@ builder.add_edge("tools","assistant")
 
 gaia_agent = builder.compile()
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
+class BasicAgent:
+    """A langgraph agent."""
+    def __init__(self):
+        print("BasicAgent initialized.")
+        self.graph = gaia_agent
+
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
+
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the agent on them, submits all answers,
-    and displays the results. Handles attachments if present.
+    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
-    
-    # 1. Instantiate Agent (modify this part to create your agent)
+
+    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 = gaia_agent
+        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 (useful for others so please keep it public)
+    # 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
-    questions_data = get_all_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
-        
-        # 2.2 Handle attachment if present
-        attachment_info = None
-        if "file_name" in item and item["file_name"]:
-            file_name = item.get("file_name")
-            attachment_info = handle_attachment(task_id, file_name)
-            print(f"Attachment handling result: {attachment_info['status']}")
-        
         try:
-            # Prepare messages based on attachment handling
-            messages = [
-                SystemMessage(content=SYSTEM_PROMPT),
-                SystemMessage(content=f"Current task id: {task_id}")
-            ]
-            
-            # If we have an attachment that Claude can process directly
-            if attachment_info and attachment_info["status"] == "success" and attachment_info["handling"] == "direct":
-                # Encode content for direct inclusion
-                encoded_content = base64.b64encode(attachment_info["raw_content"]).decode('utf-8')
-                content_type = attachment_info["content_type"]
-                
-                # Create multimodal message
-                if content_type.startswith('image/'):
-                    multimodal_content = [
-                        {"type": "text", "text": question_text},
-                        {
-                            "type": "image", 
-                            "source": {
-                                "type": "base64", 
-                                "media_type": content_type,
-                                "data": encoded_content
-                            }
-                        }
-                    ]
-                elif content_type == "application/pdf" or "spreadsheet" in content_type or "excel" in content_type or "csv" in content_type:
-                    multimodal_content = [
-                        {"type": "text", "text": question_text},
-                        {
-                            "type": "file", 
-                            "source": {
-                                "type": "base64", 
-                                "media_type": content_type,
-                                "data": encoded_content
-                            },
-                            "name": attachment_info["file_name"]
-                        }
-                    ]
-                
-                messages.append(HumanMessage(content=multimodal_content))
-                
-            # If we have an attachment that needs tool processing
-            elif attachment_info and attachment_info["status"] == "success" and attachment_info["handling"] == "tool":
-                # Add info about the file to the question
-                file_info = (
-                    f"{question_text}\n\n"
-                    f"Note: This task has an attached file that can be accessed at: {attachment_info['file_path']}\n"
-                    f"File type: {attachment_info['content_type']}"
-                )
-                messages.append(HumanMessage(content=file_info))
-                
-            # If no attachment or error with attachment
-            else:
-                messages.append(HumanMessage(content=question_text))
-            
-            # Invoke the agent with the prepared messages
-            agent_answer = agent.invoke({"messages": messages},{"recursion_limit": 50})
-            submitted_answer = extract_final_answer(agent_answer['messages'][-1].content)
+            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}"})
-    
+             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
-    return submit(submission_data, results_log)
-
-def run_and_submit_one( profile: gr.OAuthProfile | None):
-    # --- 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
-    
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = gaia_agent
-    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
-    questions_data = get_one_random_question()
-    print("questions_data:", questions_data)
-    
-    # 2.2 Handle attachment if present
-    attachment_info = None
-    if "file_name" in questions_data and questions_data["file_name"]:
-        task_id = questions_data.get("task_id")
-        file_name = questions_data.get("file_name")
-        attachment_info = handle_attachment(task_id, file_name)
-        print(f"Attachment handling result: {attachment_info['status']}")
 
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-
-    task_id = questions_data.get("task_id")
-    question_text = questions_data.get("question")
-
-    if not task_id or question_text is None:
-        print(f"Skipping item with missing task_id or question")
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-            # Prepare messages based on attachment handling
-            messages = [
-                SystemMessage(content=SYSTEM_PROMPT),
-                SystemMessage(content=f"Current task id: {task_id}")
-            ]
-            
-            # If we have an attachment that Claude can process directly
-            if attachment_info and attachment_info["status"] == "success" and attachment_info["handling"] == "direct":
-                # Encode content for direct inclusion
-                encoded_content = base64.b64encode(attachment_info["raw_content"]).decode('utf-8')
-                content_type = attachment_info["content_type"]
-                
-                # Create multimodal message
-                if content_type.startswith('image/'):
-                    multimodal_content = [
-                        {"type": "text", "text": question_text},
-                        {
-                            "type": "image", 
-                            "source": {
-                                "type": "base64", 
-                                "media_type": content_type,
-                                "data": encoded_content
-                            }
-                        }
-                    ]
-                elif content_type == "application/pdf" or "spreadsheet" in content_type or "excel" in content_type or "csv" in content_type:
-                    multimodal_content = [
-                        {"type": "text", "text": question_text},
-                        {
-                            "type": "file", 
-                            "source": {
-                                "type": "base64", 
-                                "media_type": content_type,
-                                "data": encoded_content
-                            },
-                            "name": attachment_info["file_name"]
-                        }
-                    ]
-                
-                messages.append(HumanMessage(content=multimodal_content))
-                
-            # If we have an attachment that needs tool processing
-            elif attachment_info and attachment_info["status"] == "success" and attachment_info["handling"] == "tool":
-                # Add info about the file to the question
-                file_info = (
-                    f"{question_text}\n\n"
-                    f"Note: This task has an attached file that can be accessed at: {attachment_info['file_path']}\n"
-                    f"File type: {attachment_info['content_type']}"
-                )
-                messages.append(HumanMessage(content=file_info))
-                
-            # If no attachment or error with attachment
-            else:
-                messages.append(HumanMessage(content=question_text))
-            
-            # Invoke the agent with the prepared messages
-            agent_answer = agent.invoke({"messages": messages},{"recursion_limit": 50})
-            submitted_answer = extract_final_answer(agent_answer['messages'][-1].content)
-            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})
+        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:
-            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)
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
 
-    # 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)
-
-    return submit(submission_data, results_log)
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
@@ -321,7 +212,6 @@ with gr.Blocks() as demo:
     gr.LoginButton()
 
     run_button = gr.Button("Run Evaluation & Submit All Answers")
-    run_one_button = gr.Button("Run one question and submit")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     # Removed max_rows=10 from DataFrame constructor
@@ -331,10 +221,6 @@ with gr.Blocks() as demo:
         fn=run_and_submit_all,
         outputs=[status_output, results_table]
     )
-    run_one_button.click(
-        fn=run_and_submit_one,
-        outputs=[status_output, results_table]
-    )
 
 if __name__ == "__main__":
     print("\n" + "-"*30 + " App Starting " + "-"*30)