Update app.py

app.py

@@ -39,7 +39,7 @@ try: import whisper; WHISPER_AVAILABLE = True
 except ImportError: WHISPER_AVAILABLE = False; print("WARNING: OpenAI Whisper not found, Audio Transcription tool will be disabled.")
 
 # Google GenAI SDK types
-from google.genai.types import HarmCategory, HarmBlockThreshold # CORRECTED IMPORT
+from google.genai.types import HarmCategory, HarmBlockThreshold
 from google.ai import generativelanguage as glm # For FileState enum
 
 # LangChain
@@ -61,14 +61,14 @@ if TYPE_CHECKING:
 LANGGRAPH_FLAVOR_AVAILABLE = False
 LG_StateGraph: Optional[Type[Any]] = None
 LG_ToolExecutor_Class: Optional[Type[Any]] = None
-LG_END: Optional[Any]] = None
+LG_END: Optional[Any] = None
 LG_ToolInvocation: Optional[Type[Any]] = None
 add_messages: Optional[Any] = None
 MemorySaver_Class: Optional[Type[Any]] = None
 
 AGENT_INSTANCE: Optional[Union[AgentExecutor, Any]] = None
 TOOLS: List[BaseTool] = []
-LLM_INSTANCE: Optional[ChatGoogleGenerativeAI]] = None
+LLM_INSTANCE: Optional[ChatGoogleGenerativeAI] = None
 LANGGRAPH_MEMORY_SAVER: Optional[Any] = None
 
 # google-genai Client SDK
@@ -255,7 +255,6 @@ def _download_file(file_identifier: str, task_id_for_file: Optional[str] = None)
             name_without_ext, current_ext = os.path.splitext(effective_save_path)
             if not current_ext:
                 content_type_header = r.headers.get('content-type', '')
-                # FIX: Handle split correctly and take first part before stripping
                 content_type_val = content_type_header.split(';')[0].strip() if content_type_header else ''
                 if content_type_val:
                     guessed_ext = mimetypes.guess_extension(content_type_val)
@@ -366,9 +365,7 @@ TOOL USAGE:
 - 'python_repl': `args` is like '{{"query": "python code string"}}'. Use print() for output.
 RESPONSE FORMAT:
 Final AIMessage should contain ONLY the answer in 'content' and NO 'tool_calls'. If using tools, 'content' can be thought process, with 'tool_calls'.
-Begin!
-Current Task Details (including Task ID and any associated files):
-{input}"""
+Begin!"""
 
 REACT_PROMPT_TEMPLATE_STR = """You are a highly intelligent agent for the GAIA benchmark.
 Goal: EXACT MATCH answer. No extra text/markdown.
@@ -396,7 +393,6 @@ def initialize_agent_and_tools(force_reinit=False):
             model=GEMINI_MODEL_NAME,
             google_api_key=GOOGLE_API_KEY,
             temperature=0.0,
-            # safety_settings parameter is removed to use model's default settings.
             timeout=120,
             convert_system_message_to_human=False # Explicitly set to False
         )
@@ -413,29 +409,25 @@ def initialize_agent_and_tools(force_reinit=False):
     else: logger.warning("'direct_multimodal_gemini_tool' NOT added (client or PIL missing).")
     try: search_tool = DuckDuckGoSearchRun(name="web_search"); search_tool.description = "Web search. Input: query."; TOOLS.append(search_tool)
     except Exception as e: logger.warning(f"DuckDuckGoSearchRun init failed: {e}")
-    try: python_repl = PythonREPLTool(name="python_repl"); python_repl.description = "Python REPL. print() for output."; TOOLS.append(python_repl)
+    try: python_repl = PythonREPLTool(name="python_repl"); python_repl.description = "Python REPL. print() for output. The input is a single string of code."; TOOLS.append(python_repl)
     except Exception as e: logger.warning(f"PythonREPLTool init failed: {e}")
     logger.info(f"Final tools list for agent: {[t.name for t in TOOLS]}")
 
-    if LANGGRAPH_FLAVOR_AVAILABLE and all([LG_StateGraph, LG_ToolExecutor_Class, LG_END, LLM_INSTANCE, add_messages]): # LG_ToolInvocation removed
+    if LANGGRAPH_FLAVOR_AVAILABLE and all([LG_StateGraph, LG_ToolExecutor_Class, LG_END, LLM_INSTANCE, add_messages]):
         if not LANGGRAPH_MEMORY_SAVER and MemorySaver_Class: LANGGRAPH_MEMORY_SAVER = MemorySaver_Class(); logger.info("LangGraph MemorySaver initialized.")
         try:
             logger.info(f"Attempting LangGraph init (Tool Executor type: {LG_ToolExecutor_Class.__name__ if LG_ToolExecutor_Class else 'None'})")
             _TypedDict = getattr(__import__('typing_extensions'), 'TypedDict', dict)
-            # FIX: Remove 'input' key from state, only use 'messages' for conversational flow
             class AgentState(_TypedDict):
                 messages: Annotated[List[Any], add_messages]
 
-            # System prompt template - this describes the agent's role and tools.
-            # The {input} placeholder for the actual task will be filled by the HumanMessage.
-            base_system_prompt_content_lg = LANGGRAPH_PROMPT_TEMPLATE_STR.split("{input}")[0].strip()
+            base_system_prompt_content_lg = LANGGRAPH_PROMPT_TEMPLATE_STR
 
             def agent_node(state: AgentState):
                 system_message_content = base_system_prompt_content_lg.format(
                     tools="\n".join([f"- {t.name}: {t.description}" for t in TOOLS])
                 )
                 
-                # FIX: Construct message list from state, don't re-add original prompt
                 messages_for_llm = [SystemMessage(content=system_message_content)]
                 messages_for_llm.extend(state['messages'])
                 
@@ -451,7 +443,7 @@ def initialize_agent_and_tools(force_reinit=False):
             if not LG_ToolExecutor_Class: raise ValueError("LG_ToolExecutor_Class is None for LangGraph.")
             tool_executor_instance_lg = LG_ToolExecutor_Class(tools=TOOLS)
 
-            def tool_node(state: AgentState): # Custom tool node that expects ToolInvocation if available
+            def tool_node(state: AgentState):
                 last_msg = state['messages'][-1] if state.get('messages') and isinstance(state['messages'][-1], AIMessage) else None
                 if not last_msg or not last_msg.tool_calls: return {"messages": []}
                 tool_results = []
@@ -463,10 +455,10 @@ def initialize_agent_and_tools(force_reinit=False):
                         continue
                     try:
                         logger.info(f"LG Tool Invoking: '{name}' with {args} (ID: {tc_id})")
-                        if LG_ToolInvocation and type(LG_ToolExecutor_Class).__name__ != 'ToolNode': # Check if ToolInvocation exists and we're not using ToolNode directly
+                        if LG_ToolInvocation and type(LG_ToolExecutor_Class).__name__ != 'ToolNode':
                             invocation = LG_ToolInvocation(tool=name, tool_input=args)
                             output_lg = tool_executor_instance_lg.invoke(invocation) # type: ignore
-                        else: # Assume ToolNode or compatible executor can take the dict directly if LG_ToolInvocation is None
+                        else:
                             output_lg = tool_executor_instance_lg.invoke(tc) # type: ignore
                         tool_results.append(ToolMessage(content=str(output_lg), tool_call_id=tc_id, name=name))
                     except Exception as e_tool_node_lg:
@@ -474,7 +466,6 @@ def initialize_agent_and_tools(force_reinit=False):
                         tool_results.append(ToolMessage(content=f"Error for tool {name}: {str(e_tool_node_lg)}", tool_call_id=tc_id, name=name))
                 return {"messages": tool_results}
 
-
             workflow_lg = LG_StateGraph(AgentState) # type: ignore
             workflow_lg.add_node("agent", agent_node)
             workflow_lg.add_node("tools", tool_node)
@@ -506,7 +497,6 @@ def initialize_agent_and_tools(force_reinit=False):
     if not AGENT_INSTANCE: raise RuntimeError("CRITICAL: Agent initialization completely failed.")
     logger.info(f"Agent init finished. Active agent type: {type(AGENT_INSTANCE).__name__}")
 
-# --- get_agent_response, construct_prompt_for_agent, run_and_submit_all (Unchanged) ---
 def get_agent_response(prompt: str, task_id: Optional[str]=None, thread_id: Optional[str]=None) -> str:
     global AGENT_INSTANCE, LLM_INSTANCE
     thread_id_to_use = thread_id or (f"gaia_task_{task_id}" if task_id else hashlib.md5(prompt.encode()).hexdigest()[:8])
@@ -524,7 +514,6 @@ def get_agent_response(prompt: str, task_id: Optional[str]=None, thread_id: Opti
     try:
         if is_langgraph_agent_get:
             logger.debug(f"Using LangGraph agent for thread: {thread_id_to_use}")
-            # FIX: The input should be a list of messages for the 'add_messages' reducer.
             input_for_lg_get = {"messages": [HumanMessage(content=prompt)]}
             logger.debug(f"Invoking LangGraph with input: {input_for_lg_get}")
             final_state_lg_get = AGENT_INSTANCE.invoke(input_for_lg_get, {"configurable": {"thread_id": thread_id_to_use}})