Update app.py

app.py

@@ -1,57 +1,92 @@
 import os
 import gradio as gr
 import requests
-import inspect
+# import inspect # No longer needed
 import pandas as pd
+from dotenv import load_dotenv # Keep for consistency if final_agent uses it
+import traceback # For better error logging
 
-# (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 ------
-class BasicAgent:
+# ----- THIS IS WHERE YOU INTEGRATE YOUR AGENT -----
+
+# 1. IMPORT your agent function from your agent file
+try:
+    # Make sure the filename 'final_agent' matches the file you uploaded
+    from final_agent import answer_gaia_task
+    print("Successfully imported answer_gaia_task from final_agent.py")
+except ImportError:
+    print("ERROR: Could not import answer_gaia_task from final_agent.py. Check filename and function name.")
+    # Define a dummy function for graceful failure of the app
+    def answer_gaia_task(question: str, file_path: Optional[str] = None) -> str:
+        return "ERROR: Agent function 'answer_gaia_task' not found in final_agent.py."
+except Exception as e:
+    print(f"ERROR during import or initial setup in final_agent.py: {e}")
+    traceback.print_exc()
+    # Define a dummy function
+    def answer_gaia_task(question: str, file_path: Optional[str] = None) -> str:
+        return f"ERROR: Agent setup failed - {e}"
+
+class AgentRunner: # Renamed from BasicAgent
     def __init__(self):
-        print("BasicAgent initialized.")
+        # Agent initialization (LLM, tools, graph compile) happens globally
+        # when final_agent.py is imported. No specific actions needed here now.
+        print("AgentRunner initialized. LangGraph agent from final_agent.py should be ready.")
+        # Optional: Add checks here if needed (e.g., API key validation)
+        if not os.getenv("GEMINI_API_KEY") or not os.getenv("TAVILY_API_KEY"):
+             print("WARNING: GEMINI_API_KEY or TAVILY_API_KEY might not be set in Space secrets.")
+
     def __call__(self, question: str) -> str:
-        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
+        """Runs the imported agent function on a single question."""
+        print(f"AgentRunner received question (first 50 chars): {question[:50]}...")
+        try:
+            # Call the imported function. Assume file_path handling is within answer_gaia_task or via question text.
+            final_answer = answer_gaia_task(question=question, file_path=None)
+            # Ensure result is always a string for submission
+            final_answer_str = str(final_answer)
+            print(f"Agent function returned answer: {final_answer_str}")
+            return final_answer_str
+        except Exception as e:
+             print(f"ERROR calling answer_gaia_task: {e}")
+             traceback.print_exc() # Log the full error to Space logs
+             return f"ERROR: Agent failed during execution - {e}"
+
+# ----- END OF AGENT INTEGRATION SECTION -----
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    Fetches all questions, runs the integrated agent 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
+    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
+        return "Please Login to Hugging Face using 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)
+    # 1. Instantiate Agent Runner (which uses your imported function)
     try:
-        agent = BasicAgent()
+        agent = AgentRunner() # This now sets up to use your LangGraph 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)
+        print(f"Error instantiating AgentRunner: {e}")
+        return f"Error initializing agent runner: {e}", None
+
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Code URL not available (SPACE_ID not set)"
+    print(f"Agent code reference: {agent_code}")
 
     # 2. Fetch Questions
     print(f"Fetching questions from: {questions_url}")
     try:
-        response = requests.get(questions_url, timeout=15)
+        response = requests.get(questions_url, timeout=30)
         response.raise_for_status()
         questions_data = response.json()
         if not questions_data:
@@ -69,29 +104,36 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # 3. Run your Agent on each question
     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")
+        # file_path = item.get("file_path") # Check if needed/provided
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
+            # This call triggers AgentRunner.__call__, which runs your answer_gaia_task
             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:
+             # Catch errors during the agent's execution on a specific task
              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}"})
+             traceback.print_exc()
+             # Log the error but continue to the next question
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT RUN ERROR: {e}"})
+             # Optionally submit the error message, or skip submitting for this task_id
+             answers_payload.append({"task_id": task_id, "submitted_answer": f"AGENT RUN 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 
+    # 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)
@@ -114,83 +156,53 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         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
+        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
+        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
+        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
+        status_message = f"An unexpected error occurred during submission: {e}"; print(status_message)
+        traceback.print_exc(); results_df = pd.DataFrame(results_log); return status_message, results_df
 
 
-# --- Build Gradio Interface using Blocks ---
+# --- Build Gradio Interface (Unchanged) ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# GAIA Agent Evaluation Runner") # Updated Title
     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.
-
+        1. Ensure your agent logic is in `final_agent.py` and dependencies in `requirements.txt`. Set secrets in Space settings.
+        2. Log in to Hugging Face using the button below.
+        3. Click 'Run Evaluation & Submit All Answers' to run your agent. Check Logs for detailed progress.
         ---
-        **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.
+        **Disclaimers:** Execution can take significant time.
         """
     )
-
     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]
     )
 
+# --- Main execution block (Unchanged) ---
 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.")
-
+    space_id_startup = os.getenv("SPACE_ID")
+    if space_host_startup: print(f"✅ SPACE_HOST found: {space_host_startup}")
+    else: print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+    if space_id_startup: print(f"✅ SPACE_ID found: {space_id_startup}")
+    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 GAIA Agent Evaluation...")
+    # Set debug=False for less verbose logs once stable
     demo.launch(debug=True, share=False)