agent.py

# /home/user/app/agent.py
import os
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents import AgentExecutor, create_structured_chat_agent
# from langchain_google_genai import HarmBlockThreshold, HarmCategory # Optional for safety settings

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage

# --- Import your defined tools FROM THE 'tools' PACKAGE ---
# This relies on tools/__init__.py correctly exporting these names.
from tools import (
    BioPortalLookupTool,
    UMLSLookupTool,
    QuantumTreatmentOptimizerTool,
    # QuantumOptimizerInput, # Only if needed for type hints directly in this file for some reason
    # GeminiTool, # Assuming not used for now as main LLM is Gemini
)

from config.settings import settings # This loads your HF secrets into the settings object
from services.logger import app_logger

# --- Initialize LLM (Gemini) ---
# This block is critical for ensuring the API key is used.
llm = None # Initialize to None in case of failure
try:
    # Prioritize the API key from settings (loaded from HF Secrets)
    # settings.GEMINI_API_KEY should be populated by Pydantic BaseSettings from the HF Secret
    gemini_api_key_from_settings = settings.GEMINI_API_KEY
    
    # Fallback to environment variable GOOGLE_API_KEY if settings.GEMINI_API_KEY is not found/set
    # (though ideally, settings.GEMINI_API_KEY should be the primary source via HF Secrets)
    api_key_to_use = gemini_api_key_from_settings or os.getenv("GOOGLE_API_KEY")

    if not api_key_to_use:
        app_logger.error(
            "CRITICAL: Gemini API Key not found. "
            "Ensure GEMINI_API_KEY is set in Hugging Face Space secrets and loaded into settings, "
            "or GOOGLE_API_KEY is set as an environment variable."
        )
        raise ValueError(
            "Gemini API Key not configured. Please set it in Hugging Face Space secrets "
            "as GEMINI_API_KEY or ensure GOOGLE_API_KEY environment variable is available."
        )

    llm = ChatGoogleGenerativeAI(
        model="gemini-1.5-pro-latest", # Using a more capable model
        # model="gemini-pro", # Fallback if 1.5-pro is not available or for cost reasons
        temperature=0.2,
        google_api_key=api_key_to_use, # *** EXPLICITLY PASS THE KEY HERE ***
        convert_system_message_to_human=True, # Often useful for non-OpenAI models
        # Example safety settings (optional, adjust as needed)
        # safety_settings={
        #     HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
        #     HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
        # }
    )
    app_logger.info(f"ChatGoogleGenerativeAI ({llm.model}) initialized successfully using provided API key.")

except Exception as e:
    # This broad exception catch is to provide a clear error message if LLM init fails for any reason.
    detailed_error_message = str(e)
    user_facing_error = f"Gemini LLM initialization failed: {detailed_error_message}. " \
                        "Check API key validity, model name, and configurations in Hugging Face Secrets."
    
    if "default credentials were not found" in detailed_error_message.lower() or \
       "could not find default credentials" in detailed_error_message.lower() or \
       "api_key" in detailed_error_message.lower(): # Catch common API key related messages
        user_facing_error = "Gemini LLM initialization failed: API key issue or missing credentials. " \
                            "Ensure GEMINI_API_KEY is correctly set in Hugging Face Secrets and is valid."
        app_logger.error(user_facing_error + f" Original error details: {detailed_error_message}", exc_info=False)
    else:
        app_logger.error(user_facing_error, exc_info=True) # Log full traceback for other errors
    
    # Re-raise to stop agent setup if LLM fails. This will be caught in get_agent_executor.
    raise ValueError(user_facing_error)


# --- Initialize Tools List ---
tools_list = [
    UMLSLookupTool(),
    BioPortalLookupTool(),
    QuantumTreatmentOptimizerTool(),
    # GeminiTool(), # Add if you have a specific reason to use Gemini as a sub-tool
]
app_logger.info(f"Agent tools initialized: {[tool.name for tool in tools_list]}")


# --- Agent Prompt (for Structured Chat with Gemini) ---
SYSTEM_PROMPT_TEMPLATE = (
    "You are 'Quantum Health Navigator', an advanced AI assistant for healthcare professionals. "
    "Your primary goal is to provide accurate information and insights based on user queries and available tools. "
    "You must adhere to the following guidelines:\n"
    "1.  Disclaimers: Always remind the user that you are an AI, not a human medical professional, and your information "
        "is for support, not a substitute for clinical judgment. Do not provide direct medical advice for specific patient cases "
        "unless it's the direct output of a specialized tool like 'quantum_treatment_optimizer'.\n"
    "2.  Patient Context: The user may provide patient context at the start of the session. This context is available as: {patient_context}. "
        "You MUST consider this context when it's relevant to the query, especially for the 'quantum_treatment_optimizer' tool.\n"
    "3.  Tool Usage: You have access to the following tools:\n{tools}\n" # {tools} is filled by the agent
    "   To use a tool, respond *only* with a JSON markdown code block with 'action' and 'action_input' keys. "
    "   The 'action_input' must match the schema for the specified tool. Examples:\n"
    "   For `umls_lookup`: ```json\n{{\"action\": \"umls_lookup\", \"action_input\": \"myocardial infarction\"}}\n```\n"
    "   For `bioportal_lookup`: ```json\n{{\"action\": \"bioportal_lookup\", \"action_input\": {{\"term\": \"diabetes mellitus\", \"ontology\": \"SNOMEDCT\"}}}}\n```\n"
    "   For `quantum_treatment_optimizer`: ```json\n{{\"action\": \"quantum_treatment_optimizer\", \"action_input\": {{\"patient_data\": {{\"age\": 55, \"gender\": \"Male\", \"symptoms\": [\"chest pain\"]}}, \"current_treatments\": [\"metformin\"], \"conditions\": [\"Type 2 Diabetes\"]}}}}\n```\n"
    "   Ensure the `action_input` for `quantum_treatment_optimizer` includes a `patient_data` dictionary populated from the overall {patient_context}.\n"
    "4.  Responding to User: After using a tool, you will receive an observation. Use this observation and your knowledge to formulate a comprehensive final answer to the human. Cite the tool if you used one (e.g., 'According to UMLS Lookup...'). Do not output a tool call again unless necessary for a multi-step process.\n"
    "5.  Specific Tool Guidance:\n"
    "   - If asked about treatment optimization for a specific patient (especially if patient context is provided), you MUST use the `quantum_treatment_optimizer` tool.\n"
    "   - For definitions, codes, or general medical concepts, `umls_lookup` or `bioportal_lookup` are appropriate.\n"
    "6.  Conversation Flow: Refer to the `Previous conversation history` to maintain context.\n\n"
    "Begin!\n\n"
    "Previous conversation history:\n"
    "{chat_history}\n\n"
    "New human question: {input}\n"
    "{agent_scratchpad}" # Placeholder for agent's internal thoughts, tool calls, and tool observations
)

prompt = ChatPromptTemplate.from_messages([
    ("system", SYSTEM_PROMPT_TEMPLATE),
    MessagesPlaceholder(variable_name="agent_scratchpad"),
])
app_logger.info("Agent prompt template created for Gemini structured chat agent.")

# --- Create Agent ---
# This assumes `llm` was successfully initialized above.
if llm is None:
    # This case should ideally not be reached if the ValueError was raised during LLM init,
    # but as a defensive measure:
    app_logger.critical("LLM object is None at agent creation stage. Cannot proceed.")
    # The ValueError from LLM init should have already stopped the module loading.
    # If somehow execution reaches here with llm=None, something is very wrong.
    raise SystemExit("Agent LLM failed to initialize. Application cannot start.")

try:
    agent = create_structured_chat_agent(llm=llm, tools=tools_list, prompt=prompt)
    app_logger.info("Structured chat agent created successfully with Gemini LLM and tools.")
except Exception as e:
    app_logger.error(f"Failed to create structured chat agent: {e}", exc_info=True)
    raise ValueError(f"Gemini agent creation failed: {e}")


# --- Create Agent Executor ---
agent_executor = AgentExecutor(
    agent=agent,
    tools=tools_list,
    verbose=True,
    handle_parsing_errors=True,
    max_iterations=10,
    early_stopping_method="generate",
    # return_intermediate_steps=True, # Good for debugging, makes response a dict with 'intermediate_steps'
)
app_logger.info("AgentExecutor with Gemini agent created successfully.")


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

def get_agent_executor():
    """
    Returns the configured agent executor for Gemini.
    The executor is initialized when this module is first imported.
    """
    global _agent_executor_instance
    if _agent_executor_instance is None:
        # This should not happen if module initialization was successful.
        # It might indicate an issue where the module is reloaded or init failed silently.
        app_logger.critical("CRITICAL: Agent executor is None when get_agent_executor is called. Re-initialization attempt or fundamental error.")
        # You could try to re-initialize here, but it's better to ensure init works on first load.
        # For now, raise an error to make it obvious.
        raise RuntimeError("Agent executor was not properly initialized. Check application startup logs.")
    return _agent_executor_instance

# --- Example Usage (for local testing of this agent.py file) ---
if __name__ == "__main__":
    # Check if the API key is available for the test
    main_test_api_key = settings.GEMINI_API_KEY or os.getenv("GOOGLE_API_KEY")
    if not main_test_api_key:
        print("🚨 Please set your GOOGLE_API_KEY (for Gemini) in .env file or as an environment variable to run the test.")
    else:
        print("\n🚀 Quantum Health Navigator (Gemini 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:
            print(f"⚠️ Agent initialization failed during test startup: {e_init}")
            print("Ensure your API key is correctly configured.")
            exit() # Exit if agent can't be initialized

        current_chat_history_for_test_run = []

        test_patient_context_summary_str = (
            "Age: 58; Gender: Female; Chief Complaint: Recent onset of blurry vision and fatigue; "
            "Key Medical History: Prediabetes, Mild dyslipidemia; "
            "Current Medications: None; Allergies: None known."
        )
        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:
                continue

            try:
                app_logger.info(f"__main__ test: Invoking agent with input: '{user_input_str}'")
                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}")
                
                current_chat_history_for_test_run.append(HumanMessage(content=user_input_str))
                current_chat_history_for_test_run.append(AIMessage(content=ai_output_str))
                
                if len(current_chat_history_for_test_run) > 10:
                    current_chat_history_for_test_run = current_chat_history_for_test_run[-10:]

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