asf

app.py

@@ -21,15 +21,84 @@ class BasicAgent:
     """A langgraph agent."""
     def __init__(self):
         print("BasicAgent initialized.")
-        self.graph = build_graph()
+        # Assumes build_graph() and sys_msg are available from your agent.py
+        # You need to import them here.
+        # from your_agent_file import build_graph, sys_msg as agent_system_message
+        # self.graph = build_graph()
+        # self.system_message = agent_system_message
+
+        # Let's assume for now agent.py is structured to be imported like this:
+        # Option 1: if agent.py defines them globally
+        # from agent import build_graph, sys_msg
+        # self.graph = build_graph(provider="huggingface") # Or your desired provider
+        # self.sys_msg_for_graph = sys_msg
+
+        # Option 2: If build_graph also returns the sys_msg or if sys_msg is part of the graph object
+        # This depends on how you refactor agent.py for importability
+
+        # For this example, I'll assume you import them directly:
+        from agent import build_graph, sys_msg as agent_sys_msg # Make sure agent.py is in PYTHONPATH or same dir
+        
+        self.graph = build_graph(provider="huggingface") # Specify the provider
+        self.agent_system_message = agent_sys_msg
+
 
     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:]
+        
+        # INCORRECT INVOCATION:
+        # messages = [HumanMessage(content=question)] # <-- MISSING SYSTEM MESSAGE
+        # messages = self.graph.invoke({"messages": messages})
+
+        # CORRECTED INVOCATION:
+        # You MUST include the system message that was loaded in agent.py
+        initial_graph_messages = [self.agent_system_message, HumanMessage(content=question)]
+        
+        print("Invoking graph with (system_message + human_question):")
+        # Optional: print the messages being sent for debugging
+        # for m in initial_graph_messages:
+        #     m.pretty_print()
+        # print("-" * 20)
+
+        try:
+            graph_output = self.graph.invoke({"messages": initial_graph_messages})
+        except Exception as e:
+            # This will catch the StopIteration if it propagates from the graph
+            print(f"ERROR during graph.invoke: {e}")
+            # Depending on how your outer loop handles errors,
+            # you might want to return a specific error string or re-raise
+            # For the Hugging Face course, it expects the AGENT ERROR string.
+            # The run_and_submit_all function already handles this by catching exceptions from agent().
+            raise  # Re-raise the exception to be caught by run_and_submit_all
+
+        # Parsing the answer
+        if graph_output and "messages" in graph_output and graph_output["messages"]:
+            final_ai_message = graph_output["messages"][-1] # Get the last message
+            
+            # Debug: print the final AI message object
+            # print("Final AI Message Object from Graph:")
+            # final_ai_message.pretty_print()
+
+            if hasattr(final_ai_message, 'content'):
+                raw_answer = str(final_ai_message.content)
+                
+                # Your specific parsing: "answer = messages['messages'][-1].content; return answer[14:]"
+                # This assumes the answer ALWAYS starts with "FINAL ANSWER: " (14 characters)
+                if raw_answer.upper().startswith("FINAL ANSWER: "):
+                    answer = raw_answer[14:].strip() # Remove "FINAL ANSWER: " and leading/trailing whitespace
+                else:
+                    # The LLM didn't follow the "FINAL ANSWER: " format
+                    print(f"Warning: LLM output did not start with 'FINAL ANSWER: '. Raw output: '{raw_answer}'")
+                    answer = raw_answer # Return the raw answer if format is not met, or handle as error
+            else:
+                answer = "Agent Error: Final message from graph has no content."
+                print(f"Final message object was: {final_ai_message}")
+        else:
+            answer = "Agent Error: Graph did not return expected messages structure."
+            print(f"Raw graph output: {graph_output}")
+            
+        print(f"Agent returning answer: {answer}")
+        return answer
 
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):