hopefully app.py fixed

app.py

@@ -5,11 +5,11 @@ import inspect
 import pandas as pd
 
 # Import necessary libraries for LangChain Agent
-from langchain_huggingface import HuggingFaceEndpoint # NEW IMPORT!
+from langchain_huggingface import HuggingFaceEndpoint
 from langchain.agents import AgentExecutor, create_react_agent
 from langchain import hub
-# Removed from langchain.tools import tool, as SerpAPIWrapper is a direct tool
 from langchain_community.utilities import SerpAPIWrapper
+from langchain.tools import Tool # Moved to top-level import for clarity and consistent access
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -37,7 +37,6 @@ class GAIAAgent:
 
         # Define a LangChain tool function that uses the SerpAPIWrapper
         # The description is crucial for the LLM to know when to use this tool
-        from langchain.tools import Tool # Re-import Tool if needed for explicit wrapping
         web_search_tool = Tool(
             name="Serpapi Search",
             description="useful for when you need to answer questions about current events or facts. Input should be a search query.",
@@ -47,9 +46,16 @@ class GAIAAgent:
 
         self.prompt = hub.pull("hwchase17/react")
 
+        # Increased max_iterations and max_execution_time for better performance on complex GAIA questions
         self.agent = create_react_agent(self.llm, self.tools, self.prompt)
-
-        self.agent_executor = AgentExecutor(agent=self.agent, tools=self.tools, verbose=True, handle_parsing_errors=True)
+        self.agent_executor = AgentExecutor(
+            agent=self.agent,
+            tools=self.tools,
+            verbose=True,
+            handle_parsing_errors=True,
+            max_iterations=25, # Increased from default, allow more thinking steps
+            max_execution_time=180.0 # Increased from default, allow more time per question (3 minutes)
+        )
 
 
     def __call__(self, question: str) -> str:
@@ -61,9 +67,10 @@ class GAIAAgent:
             return agent_answer
         except Exception as e:
             print(f"Error during agent execution: {e}")
-            return f"An error occurred while processing your request: {e}. Please ensure API keys are set correctly."
+            # Ensure a clean error message is returned when agent execution fails
+            return f"Agent execution failed: {e}. Check tool outputs and LLM reasoning."
 
-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.
@@ -71,12 +78,14 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID")
 
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
+    # Robust username handling
+    current_username = "anonymous_user" # Default for local testing or if not logged in
+    if profile and profile.username:
+        current_username = profile.username
+        print(f"User logged in: {current_username}")
     else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+        print("User not logged in through Gradio OAuth, using default username for submission.")
+        # Do NOT return here, allow the app to run locally without HF login
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
@@ -85,7 +94,11 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     try:
         agent = GAIAAgent()
     except Exception as e:
-        return f"Failed to initialize agent: {e}", None
+        # Improved error handling for agent initialization
+        error_message = f"Failed to initialize agent: {e}. Please ensure all required API keys (HUGGINGFACEHUB_API_TOKEN, SERPAPI_API_KEY, GOOGLE_API_KEY) are set in Hugging Face Space secrets, and model terms accepted."
+        print(error_message)
+        # Return empty DataFrame with the error message to display in Gradio UI
+        return error_message, pd.DataFrame([{"Question ID": "N/A", "Question": "Agent Initialization Failed", "Agent Answer": str(e)}])
 
     try:
         response = requests.get(questions_url)
@@ -94,7 +107,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         questions = questions_data # Assume questions_data is directly the list of questions
         print(f"Fetched {len(questions)} questions.")
     except requests.exceptions.RequestException as e:
-        return f"Failed to fetch questions: {e}", None
+        return f"Failed to fetch questions: {e}", pd.DataFrame() # Return empty DataFrame on fetch error
 
     all_answers = []
     results_for_display = []
@@ -107,8 +120,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             continue
 
         print(f"\n--- Processing Question ID: {q_id} ---")
-        agent_answer = agent(q_text)
+        agent_answer = agent(q_text) # This calls the GAIAAgent.__call__ method
 
+        # Ensured submission keys are correct as per GAIA benchmark expectations
         all_answers.append({"task_id": q_id, "submitted_answer": agent_answer}) 
         results_for_display.append({"Question ID": q_id, "Question": q_text, "Agent Answer": agent_answer})
 
@@ -117,7 +131,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     submission_data = {
         "answers": all_answers,
         "space_id": space_id, # Include SPACE_ID for the leaderboard link
-        "username": username, # Add the username here
+        "username": current_username, # Use the robustly determined username
         "agent_code": inspect.getsource(GAIAAgent), # Add agent code (for debugging on leaderboard)
     }