Update app.py

app.py

@@ -24,7 +24,6 @@ from typing import TypedDict, Annotated, Literal
 # Constants
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# Define the state for the agent
 class MessagesState(TypedDict):
     messages: List[BaseMessage]
 
@@ -42,8 +41,6 @@ class AdvancedAgent:
     def __init__(self):
         print("Initializing AdvancedAgent with LangGraph, Wikipedia, Arxiv, and Gemini 2.0 Flash")
         load_dotenv()  # Load environment variables from .env file
-        
-        # Initialize the graph
         self.graph = self.build_graph()
         print("Graph successfully built")
         
@@ -51,130 +48,74 @@ class AdvancedAgent:
         """Build the LangGraph agent with necessary tools"""
 
         llm = ChatOpenAI(
-        model="google/gemini-2.0-flash-001",
-        temperature=0,
-        openai_api_key=os.getenv("OPENROUTER_API_KEY"),
-        openai_api_base="https://openrouter.ai/api/v1"
+            model="google/gemini-2.0-flash-001",
+            temperature=0,
+            openai_api_key=os.getenv("OPENROUTER_API_KEY"),
+            openai_api_base="https://openrouter.ai/api/v1"
         )
         print("LLM initialized: Gemini 2.5 Pro via OpenRouter")
-
-        
-        # Initialize LLM
-        # llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
-        # print("LLM initialized: Gemini 2.0 Flash")
         
-        # Initialize tools
         wikipedia_tool = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
         arxiv_tool = ArxivQueryRun()
         tavily_search = TavilySearchResults(max_results=5)
-        
         tools = [wikipedia_tool, arxiv_tool, tavily_search]
         print(f"Initialized {len(tools)} tools: Wikipedia, Arxiv, Tavily Search")
         
-        # System message
         sys_msg = SystemMessage(content=system_prompt)
-        
-        # Bind tools to LLM
         llm_with_tools = llm.bind_tools(tools)
-        
-        # Define nodes
+
+        # Assistant node must be able to access llm_with_tools.
         def assistant(state: MessagesState):
             """Assistant node that processes messages and generates responses"""
             messages = state["messages"]
             response = llm_with_tools.invoke(messages)
-            return {"messages": state["messages"] + [response]}
-        
-        # Create tool node
+            return {"messages": messages + [response]}
+
         tools_node = ToolNode(tools)
-        
-        # Build the graph
+
         builder = StateGraph(MessagesState)
-        
-        # Add nodes
         builder.add_node("assistant", assistant)
         builder.add_node("tools", tools_node)
-        
-        # Add edges
-        builder.add_edge("assistant", "tools")
+
+        # The correct flow is: assistant -> tools (if tool needed) -> assistant (after tool used) -> END (if done)
         builder.set_entry_point("assistant")
-        
-        # Add conditional edges
+        builder.add_edge("assistant", "tools")
+        builder.add_edge("tools", "assistant")
+        builder.add_edge("assistant", END)
+
+        # Conditional: if tools_condition is met, go to tools, else END
         builder.add_conditional_edges(
             "assistant",
             tools_condition,
             {"tools": "tools", END: END}
         )
-        
-        # # Set entry point
-        # builder.set_entry_point("assistant")
-        
-        # Compile graph
+
         return builder.compile()
     
     def __call__(self, question: str) -> str:
-        """Process a question through the agent graph and return the response"""
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        
-        # Create initial state with system message and human question
         messages = [
             SystemMessage(content=system_prompt),
             HumanMessage(content=question)
         ]
-
-
-        
-        # Run the graph
         try:
             result = self.graph.invoke({"messages": messages})
-            
-            # Extract the final AI message as the answer
             final_messages = result["messages"]
-            
-            # Find the last non-tool AI message
             ai_messages = [msg for msg in final_messages if isinstance(msg, AIMessage)]
             if not ai_messages:
                 return "I wasn't able to generate a proper response. Please try again."
-            
-            # Get the last AI message that's not a tool call
             for msg in reversed(ai_messages):
-                # Check if this is not a tool call message
                 if not hasattr(msg, 'tool_calls') or not msg.tool_calls:
                     return msg.content
-            
-            # If we only have tool call messages, return the content of the last AI message
             return ai_messages[-1].content if ai_messages else "I wasn't able to generate a proper response. Please try again."
-            
         except Exception as e:
             print(f"Error running agent graph: {e}")
             return f"Sorry, I encountered an error while processing your question: {str(e)}"
 
-
-        
-        # # Run the graph
-        # try:
-        #     result = self.graph.invoke({"messages": messages})
-        #     # Extract the last AI message as the answer
-        #     for msg in reversed(result["messages"]):
-        #         if isinstance(msg, AIMessage) and not getattr(msg, "tool_call_id", None):
-        #             return msg.content
-            
-        #     # Fallback if no valid AI message found
-        #     return "I wasn't able to generate a proper response. Please try again."
-        # except Exception as e:
-        #     print(f"Error running agent graph: {e}")
-        #     return f"Sorry, I encountered an error while processing your question: {str(e)}"
-
-# Function to run and submit all questions
 def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the AdvancedAgent 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}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -184,39 +125,35 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent
     try:
         agent = AdvancedAgent()
     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
+
     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("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
+        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...")
@@ -231,19 +168,17 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             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
     print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -286,8 +221,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
-# --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
     gr.Markdown("# Advanced Agent Evaluation Runner")
     gr.Markdown(
@@ -303,9 +236,7 @@ with gr.Blocks() as demo:
     )
 
     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)
 
@@ -316,24 +247,19 @@ 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") # Get SPACE_ID at startup
-
+    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")
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    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")
     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 Advanced Agent Evaluation...")
     demo.launch(debug=True, share=False)