agent.py

# /home/user/app/agent.py
import os
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_openai_functions_agent

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import AIMessage, HumanMessage # SystemMessage can be implicitly created or explicit

# --- Import your defined tools FROM THE 'tools' PACKAGE ---
from tools import (
    BioPortalLookupTool,
    UMLSLookupTool,
    QuantumTreatmentOptimizerTool,
    # QuantumOptimizerInput, # Schemas are primarily used by tools themselves
    # GeminiTool, # Not using in this OpenAI-centric agent
)

from config.settings import settings
from services.logger import app_logger

# --- Initialize LLM (OpenAI) ---
llm = None # Initialize to None for robust error handling if init fails
try:
    if not settings.OPENAI_API_KEY:
        app_logger.error("CRITICAL: OPENAI_API_KEY not found in settings. Agent cannot initialize.")
        raise ValueError("OpenAI API Key not configured. Please set it in Hugging Face Space secrets as OPENAI_API_KEY.")

    llm = ChatOpenAI(
        model_name="gpt-4-turbo-preview", # More capable model for function calling
        # model_name="gpt-3.5-turbo-0125", # More cost-effective alternative
        temperature=0.1, # Lower for more deterministic tool use and function calls
        openai_api_key=settings.OPENAI_API_KEY
    )
    app_logger.info(f"ChatOpenAI ({llm.model_name}) initialized successfully for agent.")

except Exception as e:
    detailed_error_message = str(e)
    user_facing_error = f"OpenAI LLM initialization failed: {detailed_error_message}. Check API key and model name."
    if "api_key" in detailed_error_message.lower() or "authenticate" in detailed_error_message.lower():
        user_facing_error = "OpenAI LLM initialization failed: API key issue. Ensure OPENAI_API_KEY is correctly set in Hugging Face Secrets and is valid."
        app_logger.error(user_facing_error + f" Original: {detailed_error_message}", exc_info=False)
    else:
        app_logger.error(user_facing_error, exc_info=True)
    raise ValueError(user_facing_error) # Propagate error to stop further agent setup


# --- Initialize Tools List ---
tools_list = [
    UMLSLookupTool(),
    BioPortalLookupTool(),
    QuantumTreatmentOptimizerTool(),
]
app_logger.info(f"Agent tools initialized: {[tool.name for tool in tools_list]}")


# --- Agent Prompt (for OpenAI Functions Agent - Simplified System Prompt) ---
# The create_openai_functions_agent implicitly makes tool descriptions available to the LLM
# via the function-calling mechanism. Explicitly listing {tools} and {tool_names} in the
# system prompt string might be redundant or conflict with how this agent type works.
# We will still provide overall instructions and patient_context placeholder.
OPENAI_SYSTEM_PROMPT_TEXT_SIMPLIFIED = (
    "You are 'Quantum Health Navigator', an AI assistant for healthcare professionals. "
    "Your goal is to assist with medical information lookup, treatment optimization queries, and general medical Q&A. "
    "You have access to a set of specialized tools. Use them when a user's query can be best answered by one of them, based on their descriptions.\n"
    "Disclaimers: Always state that you are for informational support and not a substitute for clinical judgment. Do not provide direct medical advice for specific patient cases without using the 'quantum_treatment_optimizer' tool if relevant.\n"
    "Patient Context for this session (if provided by the user earlier): {patient_context}\n" # This variable is passed from invoke
    "When using the 'quantum_treatment_optimizer' tool, ensure you populate its 'patient_data' argument using the available {patient_context}.\n"
    "For `bioportal_lookup`, if the user doesn't specify an ontology, you may ask or default to 'SNOMEDCT_US'.\n"
    "Always be clear and concise. Cite tools if their output forms a key part of your answer."
)

# The ChatPromptTemplate defines the sequence of messages sent to the LLM.
# `create_openai_functions_agent` expects specific placeholders.
prompt = ChatPromptTemplate.from_messages([
    ("system", OPENAI_SYSTEM_PROMPT_TEXT_SIMPLIFIED), # System instructions, expects {patient_context}
    MessagesPlaceholder(variable_name="chat_history"),  # For past Human/AI messages
    ("human", "{input}"),                              # For the current user query
    MessagesPlaceholder(variable_name="agent_scratchpad") # For agent's internal work (function calls/responses)
])
app_logger.info("Agent prompt template (simplified for OpenAI Functions) created.")

# --- Create Agent ---
if llm is None: # Defensive check, should have been caught by earlier raise
    app_logger.critical("LLM object is None at agent creation (OpenAI). Application cannot proceed.")
    raise SystemExit("Agent LLM failed to initialize. Application cannot start.")

try:
    # `create_openai_functions_agent` will use the tools' Pydantic schemas to define
    # the "functions" that the OpenAI model can call.
    agent = create_openai_functions_agent(llm=llm, tools=tools_list, prompt=prompt)
    app_logger.info("OpenAI Functions agent created successfully.")
except Exception as e:
    app_logger.error(f"Failed to create OpenAI Functions agent: {e}", exc_info=True)
    # This is where the "Input to ChatPromptTemplate is missing variables" error would occur
    # if the prompt object was still expecting variables not provided by the agent constructor or invoke.
    raise ValueError(f"OpenAI agent creation failed: {e}")


# --- Create Agent Executor ---
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools_list,
    verbose=True,               # Essential for debugging tool usage and agent thoughts
    handle_parsing_errors=True, # Tries to gracefully handle LLM output parsing issues
    max_iterations=7,           # Prevent runaway agent loops
    # return_intermediate_steps=True, # Set to True to get detailed thought/action steps in the response
)
app_logger.info("AgentExecutor with OpenAI agent created successfully.")


# --- Getter Function for Streamlit App ---
_agent_executor_instance = agent_executor # Store the successfully initialized executor

def get_agent_executor():
    """
    Returns the configured agent executor for OpenAI.
    The executor is initialized when this module is first imported.
    """
    global _agent_executor_instance
    if _agent_executor_instance is None:
        # This indicates a failure during the initial module load (LLM or agent creation).
        app_logger.critical("CRITICAL: Agent executor is None when get_agent_executor is called (OpenAI). Initialization likely failed.")
        raise RuntimeError("Agent executor (OpenAI) was not properly initialized. Check application startup logs for errors (e.g., API key issues, prompt errors).")
    
    # Final check for API key, though LLM initialization should be the primary guard.
    if not settings.OPENAI_API_KEY:
        app_logger.error("OpenAI API Key is missing at get_agent_executor call. Agent will fail.")
        raise ValueError("OpenAI API Key not configured.")
        
    return _agent_executor_instance

# --- Example Usage (for local testing of this agent.py file) ---
if __name__ == "__main__":
    if not settings.OPENAI_API_KEY:
        print("🚨 Please set your OPENAI_API_KEY in .env file or as an environment variable to run the test.")
    else:
        print("\n🚀 Quantum Health Navigator (OpenAI Agent Test Console) 🚀")
        print("-----------------------------------------------------------")
        print("Type 'exit' or 'quit' to stop.")
        print("Example topics: medical definitions, treatment optimization (will use simulated patient context).")
        print("-" * 59)

        try:
            test_executor = get_agent_executor() # Get the executor
        except ValueError as e_init: # Catch errors from get_agent_executor or LLM/agent init
            print(f"⚠️ Agent initialization failed during test startup: {e_init}")
            print("Ensure your API key is correctly configured and prompt variables are set.")
            exit() # Exit if agent can't be initialized

        current_chat_history_for_test_run = [] # List of HumanMessage, AIMessage

        # Simulated patient context for testing the {patient_context} variable
        test_patient_context_summary_str = (
            "Age: 70; Gender: Male; Chief Complaint: Shortness of breath on exertion; "
            "Key Medical History: COPD, Atrial Fibrillation; "
            "Current Medications: Tiotropium inhaler, Apixaban 5mg BID; Allergies: Penicillin."
        )
        print(f"ℹ️  Simulated Patient Context for this test run: {test_patient_context_summary_str}\n")

        while True:
            user_input_str = input("👤 You: ").strip()
            if user_input_str.lower() in ["exit", "quit"]:
                print("👋 Exiting test console.")
                break
            if not user_input_str: # Skip empty input
                continue

            try:
                app_logger.info(f"__main__ test (OpenAI): Invoking with input: '{user_input_str}'")
                # These are the keys expected by the ChatPromptTemplate and agent:
                # "input", "chat_history", and "patient_context" (because it's in our system prompt)
                response_dict = test_executor.invoke({
                    "input": user_input_str,
                    "chat_history": current_chat_history_for_test_run,
                    "patient_context": test_patient_context_summary_str
                })
                
                ai_output_str = response_dict.get('output', "Agent did not produce an 'output' key.")
                print(f"🤖 Agent: {ai_output_str}")
                
                # Update history for the next turn
                current_chat_history_for_test_run.append(HumanMessage(content=user_input_str))
                current_chat_history_for_test_run.append(AIMessage(content=ai_output_str))
                
                # Optional: Limit history length to prevent overly long contexts
                if len(current_chat_history_for_test_run) > 10: # Keep last 5 pairs
                    current_chat_history_for_test_run = current_chat_history_for_test_run[-10:]

            except Exception as e_invoke:
                print(f"⚠️ Error during agent invocation: {type(e_invoke).__name__} - {e_invoke}")
                app_logger.error(f"Error in __main__ OpenAI agent test invocation: {e_invoke}", exc_info=True)