backend/llm_utils.py

from backend.config import (
    gpt4o_mini,
    gpt4o_mini_with_tools,
    gpt4o_with_tools,
    deepseek_with_tools,
    
)
from backend.goal_extraction import extract_goal_details, generate_confirmation_prompt
from backend.prompts.personas import PERSONA_PROMPTS
from tools.goal_tools import add_goal_tool, list_goal_categories
from langchain_core.messages import HumanMessage, SystemMessage, AIMessage, ToolMessage
from backend.rag_utils import format_profile_goals_and_moods
from langsmith import traceable
from backend.cache_utils import get_cached_route, cache_route
import time


def sanitize_history(history):
    sanitized = []
    for h in history:
        if hasattr(h, "role") and hasattr(h, "content"):
            sanitized.append({"role": h.role, "content": h.content})
        elif isinstance(h, dict):
            sanitized.append(h)
    return sanitized


async def route_message(user_message: str):
    start_time = time.time()
    
    # Try cache first
    cached_route = get_cached_route(user_message)
    if cached_route:
        print(f"[TIMING] Message routing (cached): {(time.time() - start_time) * 1000:.2f}ms (route: {cached_route})")
        return cached_route
    
    system = (
        "You are a routing assistant for a wellness chatbot. "
        "Given a user's message, decide which wellness domain it best fits. "
        "Reply with only one word (all lowercase) from this list: "
        "'mental', 'physical', 'spiritual', 'vocational', 'environmental', 'financial', 'social', or 'intellectual'."
        " If it does not fit any, reply with 'main'."
    )
    try:
        routing_response = await gpt4o_mini.ainvoke([
            SystemMessage(content=system),
            HumanMessage(content=user_message),
        ])
        route = routing_response.content.strip().lower()
        allowed = [
            "mental", "physical", "spiritual", "vocational", 
            "environmental", "financial", "social", "intellectual"
        ]
        final_route = route if route in allowed else "main"
        
        # Cache the result
        cache_route(user_message, final_route)
        
        print(f"[TIMING] Message routing (fresh): {(time.time() - start_time) * 1000:.2f}ms (route: {final_route})")
        return final_route
    except Exception as e:
        print(f"Routing error: {e}")
        return "main"


async def is_goal_setting_intent(user_message: str, conversation_history: list = None) -> bool:
    """Use LLM to determine if user is trying to set a goal"""
    
    system_prompt = (
        "Determine if the user is trying to SET A NEW GOAL or ADD A GOAL. "
        "Return 'true' only if they are explicitly trying to create/add/set a wellness goal. "
        "Return 'false' if they are just describing problems, sharing feelings, or having a conversation. "
        "Examples of goal-setting: 'I want to exercise more', 'Help me lose weight', 'Set a goal to meditate' "
        "Examples of NOT goal-setting: 'I'm stressed', 'Work is overwhelming', 'I feel anxious' "
        "Reply with only 'true' or 'false'."
    )
    
    try:
        response = await gpt4o_mini.ainvoke([
            SystemMessage(content=system_prompt),
            HumanMessage(content=user_message)
        ])
        return response.content.strip().lower() == 'true'
    except Exception:
        return False


async def execute_tool_call(tool_call, user_id):
    tool_name = tool_call["name"]
    tool_args = tool_call["args"]
    if user_id:
        tool_args["user_id"] = user_id  
    try:
        if tool_name in ("add_goal", "add_goal_tool"):
            return add_goal_tool.invoke(tool_args)
        elif tool_name == "list_goal_categories":
            return list_goal_categories.invoke(tool_args)
        else:
            return {"error": f"Unknown tool: {tool_name}"}
    except Exception as e:
        print(f"Tool execution error: {e}")
        import traceback; traceback.print_exc()
        return {"error": str(e)}


@traceable(tags=["persona", "tabi_chat"], metadata={"component": "persona_router"})
async def get_reply(agent_type, history, user_data=None, user_id=None):
    print(f"Getting reply for agent_type: {agent_type}, user_id: {user_id}")
    from langsmith.run_helpers import get_current_run_tree
    try:
        current_run = get_current_run_tree()
        if current_run:
            current_run.name = f"Persona: {agent_type}"
            current_run.metadata.update({
                "persona_type": agent_type,
                "user_id": user_id,
                "has_user_data": bool(user_data)
            })
    except:
        pass
    
    # ---- BEGIN: Improved Goal Clarification Logic ----
    CLARIFY_FIRST = {"physical", "mental", "spiritual", "social", "financial", "intellectual", "vocational", "environmental"}

    CATEGORY_OPTIONS = [
        "physical", "mental", "spiritual", "social",
        "financial", "intellectual", "vocational", "environmental"
    ]

    if agent_type in CLARIFY_FIRST and history:
        user_message = history[-1]["content"].strip().lower()
        if user_message in CATEGORY_OPTIONS:
            # 1. Find the previous user message (likely the goal description)
            prev_goal_msg = None
            for msg in reversed(history[:-1]):
                if msg["role"] == "user":
                    prev_goal_msg = msg["content"]
                    break
            # 2. Extract all details from previous message, then set the selected category
            details = await extract_goal_details(prev_goal_msg or "", history)
            details["category_slug"] = user_message
            if "category_slug" in details["missing_fields"]:
                details["missing_fields"].remove("category_slug")
            # 3. If other fields still missing, prompt for them
            if details["missing_fields"]:
                prompt = generate_confirmation_prompt(details)
                if prompt:
                    return prompt
        else:
            # Use LLM to determine if this is actually a goal-setting intent
            if await is_goal_setting_intent(user_message, history):
                details = await extract_goal_details(user_message, history)
                if details["missing_fields"]:
                    prompt = generate_confirmation_prompt(details)
                    if prompt:
                        return prompt
            # Otherwise, continue with normal conversation flow

    # ---- END: Improved Goal Clarification Logic ----

    lc_messages = []
    context_text = format_profile_goals_and_moods(user_data) if user_data else ""
    persona_prompt = PERSONA_PROMPTS.get(agent_type, PERSONA_PROMPTS["main"])
    lc_messages.append(SystemMessage(content=f"{context_text}\n{persona_prompt}"))
    for h in history:
        if h["role"] == "user":
            lc_messages.append(HumanMessage(content=h["content"]))
        else:
            lc_messages.append(AIMessage(content=h["content"]))

    model_router = {
        "physical": deepseek_with_tools,
        "mental": gpt4o_with_tools,  
        "spiritual": gpt4o_with_tools,
        "vocational": gpt4o_with_tools,
        "environmental": deepseek_with_tools,
        "financial": gpt4o_with_tools,
        "social": gpt4o_with_tools,
        "intellectual": gpt4o_with_tools,
        "main": gpt4o_mini_with_tools,
    }
    model = model_router.get(agent_type, gpt4o_with_tools)
    try:
        response = await model.ainvoke(lc_messages)
        if hasattr(response, "tool_calls") and response.tool_calls:
            tool_results = []
            for tool_call in response.tool_calls:
                result = await execute_tool_call(tool_call, user_id)
                tool_results.append(result)
            lc_messages.append(response)
            for i, tool_call in enumerate(response.tool_calls):
                tool_result = tool_results[i]
                tool_message = ToolMessage(
                    content=str(tool_result),
                    tool_call_id=tool_call["id"]
                )
                lc_messages.append(tool_message)
            final_response = await model.ainvoke(lc_messages)
            if hasattr(final_response, 'content') and final_response.content:
                return final_response.content
            else:
                if tool_results and isinstance(tool_results[0], dict):
                    if "error" not in tool_results[0]:
                       return f"I had trouble adding that goal: Could you clarify your goal or try again?"
                return "I've noted your goal request. What would you like to work on next?"
        if hasattr(response, 'content') and response.content:
            return response.content
        else:
            return "I'm here to help with your wellness journey! What would you like to work on today?"
    except Exception as model_error:
        print(f"Model invocation error: {model_error}")
        import traceback
        traceback.print_exc()
        print(f"[DEBUG] Using model: {model}")
        print(f"[DEBUG] Message history length: {len(lc_messages)}")
        print(f"[DEBUG] User data size: {len(str(user_data)) if user_data else 0}")
        return "I'm having trouble processing that right now. Could you try rephrasing your request?"


async def generate_chat_summary(messages):
    """
    Generate a short title/summary from recent chat messages.
    """
    lc_messages = [
        SystemMessage(
            content=(
                "You're a helpful assistant that creates short, concise titles (max 4 words) "
                "to summarize a conversation. Respond with only the title text."
            )
        )
    ]

    # Add only first few user+bot messages
    for msg in messages[:6]:  # up to 3 pairs
        role = msg.get("role")
        content = msg.get("content")
        if role == "user":
            lc_messages.append(HumanMessage(content=content))
        elif role == "assistant":
            lc_messages.append(AIMessage(content=content))

    try:
        response = await gpt4o_with_tools.ainvoke(lc_messages)
        summary = response.content.strip().strip('"')  # Remove extra quotes
        return summary[:50] or "Chat Summary"
    except Exception as e:
        print("Summary generation failed:", e)
        return "Chat Summary"