from fastapi import FastAPI, HTTPException, BackgroundTasks, Response
from pydantic import BaseModel, Field, ConfigDict
from typing import Optional, List, Dict
import sys
import re
import json
from huggingface_hub import repo_info
from db import db_client
from agents import create_archivist, create_librarian, run_recruiter, create_registrar, narrator, get_model, IMAGE_MODELS, AUDIO_MODELS, REASONING_MODELS
from persistence import save_to_dataset, load_from_dataset, list_saves, delete_save, persistence_manager, get_cached_media, save_cached_media
from vector import vector_model
import os
import asyncio
import httpx

app = FastAPI(title="Grim Fable: World Memory")

@app.on_event("startup")
async def startup_event():
    # Verify environment on startup
    token = os.getenv("HF_TOKEN")
    dataset_id = os.getenv("DATASET_ID")
    if not token:
        print("CRITICAL: HF_TOKEN is missing!")
    if not dataset_id:
        print("CRITICAL: DATASET_ID is missing!")

    # Check dataset accessibility
    if token and dataset_id:
        try:
            repo_info(repo_id=dataset_id, repo_type="dataset", token=token)
            print(f"Verified accessibility of dataset: {dataset_id}")
        except Exception as e:
            print(f"WARNING: Could not access dataset {dataset_id}: {e}")

    # Bootstrap rules into the vector DB before starting
    try:
        print("Starting rule bootstrapping...")
        await perform_bootstrap()
        print("Rule bootstrapping completed.")
    except Exception as e:
        print(f"FAILED to bootstrap world: {e}")
        import traceback
        traceback.print_exc()

    asyncio.create_task(inactivity_autosave_loop())

async def inactivity_autosave_loop():
    while True:
        try:
            await asyncio.sleep(60)
            if persistence_manager.should_autosave(interaction_happened=False):
                print(f"Periodic autosave triggered for: {persistence_manager.current_save_name}")
                await perform_save(persistence_manager.current_save_name)
        except Exception as e:
            print(f"Error in inactivity_autosave_loop: {e}")
            await asyncio.sleep(10) # Wait a bit before retrying

class InteractRequest(BaseModel):
    user_input: str
    narrator_model: Optional[str] = None
    reasoning_model: Optional[str] = None
    image_model: Optional[str] = None
    audio_model: Optional[str] = None

class ChatMessageDto(BaseModel):
    text: str
    is_user: bool

class ConsultGmRequest(BaseModel):
    user_input: str
    history: List[ChatMessageDto]
    reasoning_model: Optional[str] = None

class CharacterDraft(BaseModel):
    model_config = ConfigDict(populate_by_name=True)

    name: str
    race: str
    class_name: str = Field(alias="class")
    stats: Dict[str, int]
    skills: List[str]
    items: List[str]
    starting_context: str

class WorldInitRequest(BaseModel):
    prompt: Optional[str] = None
    history: Optional[List[ChatMessageDto]] = None
    save_name: Optional[str] = None
    description: Optional[str] = None
    reasoning_model: Optional[str] = None

class ConfirmRequest(BaseModel):
    draft: CharacterDraft
    save_name: str
    description: Optional[str] = None
    reasoning_model: Optional[str] = None

@app.get("/")
async def root():
    return {"status": "online", "engine": "FalkorDB", "mock_mode": db_client.is_mock}

@app.get("/health")
async def health():
    try:
        # Simple query to verify DB connectivity
        db_client.query("MATCH (n) RETURN count(n)")
        return {"status": "ready", "database": "connected"}
    except Exception as e:
        return {"status": "degraded", "database": "disconnected", "error": str(e)}

@app.post("/consult_gm")
async def consult_gm(request: ConsultGmRequest):
    try:
        print(f"Received GM consultation request: {request.user_input}")

        librarian = create_librarian(request.reasoning_model)
        if not librarian:
             raise HTTPException(status_code=500, detail="Failed to initialize Librarian for GM consultation.")

        # Search for rules and context to answer the question
        rules_context = await asyncio.to_thread(
            librarian.run, f"Use vector_search on index 'Rule' to find information that helps answer this GM question: {request.user_input}"
        )

        # Get world state context if possible
        world_context = ""
        try:
            player_data = db_client.query("MATCH (p:Player) RETURN p.name, p.hp, p.max_hp, p.strength, p.dexterity, p.constitution, p.intelligence, p.wisdom, p.charisma, p.x, p.y")
            if player_data:
                world_context = f"Current Player Stats: {player_data}"
        except: pass

        history_str = "\n".join([f"{'User' if m.is_user else 'GM'}: {m.text}" for m in request.history[-5:]]) # Last 5 messages for context

        model = get_model(request.reasoning_model)
        prompt = f"""
        You are the Game Master (GM). A player is consulting you for information or rule clarification.

        Relevant Rules: {rules_context}
        World Context: {world_context}
        Recent Consultation History:
        {history_str}

        Current Question: {request.user_input}

        Provide a helpful, concise, and in-character response based on the rules and world state. Do NOT change any data in the database.
        """

        # model is an HfApiModel, it has a generate method
        response = await asyncio.to_thread(model.generate, messages=[{"role": "user", "content": prompt}])
        response_text = response.content if hasattr(response, 'content') else str(response)

        return {"response": response_text}
    except Exception as e:
        print(f"Error in /consult_gm: {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/interact")
async def interact(request: InteractRequest, background_tasks: BackgroundTasks):
    try:
        print(f"Received interaction request: {request.user_input}")
        persistence_manager.update_interaction()
        should_save = persistence_manager.should_autosave(interaction_happened=True)

        librarian = create_librarian(request.reasoning_model)
        archivist = create_archivist(request.reasoning_model)

        if not librarian or not archivist:
            raise HTTPException(status_code=500, detail="Failed to initialize Librarian or Archivist agents.")

        rules_context = await asyncio.to_thread(
            librarian.run, f"Use vector_search on index 'Rule' to find relevant game mechanics for: {request.user_input}"
        )
        memories_context = await asyncio.to_thread(
            librarian.run, f"Use vector_search on index 'Memory' to find relevant past events for: {request.user_input}"
        )
        context = f"Rules:\n{rules_context}\n\nMemories:\n{memories_context}"

        # Archivist agent execution is blocking, run in thread
        changes = await asyncio.to_thread(
            archivist.run, f"Context: {context}\nUser Action: {request.user_input}\nUpdate the world state in FalkorDB. Use the smart_validator to ensure the Cypher respects game rules and user intent. Coordinate tracking (x,y) is mandatory. Summarize the changes."
        )

        response = await asyncio.to_thread(
            narrator.run, context=context, user_input=request.user_input, changes=changes, model_id=request.narrator_model
        )

        background_tasks.add_task(record_memory, request.user_input, response)

        if should_save:
            background_tasks.add_task(perform_save, persistence_manager.current_save_name)

        return {"response": response}
    except Exception as e:
        print(f"Error in /interact: {e}")
        import traceback
        traceback.print_exc()
        if isinstance(e, HTTPException): raise e
        raise HTTPException(status_code=500, detail=str(e))

async def record_memory(user_input: str, response: str):
    try:
        model = get_model()
        summary_prompt = f"Summarize this game exchange for long-term memory:\nUser: {user_input}\nNarrator: {response}"
        # Model inference is blocking, run in thread
        summary = await asyncio.to_thread(model.generate, messages=[{"role": "user", "content": summary_prompt}])
        summary_text = summary.content if hasattr(summary, 'content') else str(summary)

        content = f"User: {user_input} | Narrator: {response}"
        embedding = vector_model.encode(summary_text)

        query = "CREATE (m:Memory {content: $content, summary: $summary, embedding: vecf32($embedding), timestamp: timestamp()})"
        await asyncio.to_thread(db_client.query, query, {
            "content": content,
            "summary": summary_text,
            "embedding": embedding
        })
    except Exception as e:
        print(f"Error recording memory: {e}")

async def perform_save(save_name: str, description: str = None):
    try:
        db_path = "/data/world.db"
        await asyncio.to_thread(db_client.save_db, db_path)
        if await asyncio.to_thread(save_to_dataset, save_name, db_path, description=description):
            persistence_manager.needs_save = False
            persistence_manager.last_save_time = os.path.getmtime(db_path) if os.path.exists(db_path) else persistence_manager.last_save_time
    except Exception as e:
        print(f"Error performing save: {e}")

@app.post("/world/save")
async def save_world(save_name: str, background_tasks: BackgroundTasks, description: Optional[str] = None):
    persistence_manager.current_save_name = save_name
    background_tasks.add_task(perform_save, save_name, description)
    return {"status": "save_initiated", "save_name": save_name}

@app.delete("/world/save/{save_name}")
async def delete_world_save(save_name: str):
    if delete_save(save_name):
        return {"status": "deleted", "save_name": save_name}
    raise HTTPException(status_code=404, detail="Save not found or could not be deleted")

@app.post("/world/load")
async def load_world(save_name: str, background_tasks: BackgroundTasks):
    try:
        db_path = "/data/world.db"
        if await asyncio.to_thread(load_from_dataset, save_name, db_path):
            await asyncio.to_thread(db_client.load_db, db_path)
            persistence_manager.current_save_name = save_name
            # Restart after sending response to apply new RDB file
            background_tasks.add_task(restart_server)
            return {"status": "loaded", "save_name": save_name, "notice": "Server restarting to apply world state"}
        raise HTTPException(status_code=404, detail="Save not found")
    except Exception as e:
        print(f"Error in /world/load: {e}")
        import traceback
        traceback.print_exc()
        if isinstance(e, HTTPException): raise e
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/world/saves")
async def get_saves():
    try:
        return {"saves": list_saves()}
    except Exception as e:
        print(f"Error in /world/saves: {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/world/media")
async def get_media(entity_id: str, media_type: str, prompt: str, model_id: Optional[str] = None):
    """Fetches cached media or generates new media for an entity asynchronously with fallback logic."""
    if model_id and model_id.lower() == "disabled":
        raise HTTPException(status_code=400, detail="Media generation is disabled")

    save_name = persistence_manager.current_save_name
    cached_content = get_cached_media(save_name, entity_id, media_type)
    if cached_content:
        mime = "image/webp" if media_type == "image" else "audio/mpeg"
        return Response(content=cached_content, media_type=mime)

    # Preparation for generation
    models_to_try = []
    if model_id:
        models_to_try.append(model_id)

    base_list = IMAGE_MODELS if media_type == "image" else AUDIO_MODELS
    for m in base_list:
        if m not in models_to_try:
            models_to_try.append(m)

    headers = {"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"}

    async with httpx.AsyncClient(timeout=60.0) as client:
        last_error = "Unknown error"
        for mid in models_to_try:
            try:
                api_url = f"https://api-inference.huggingface.co/models/{mid}"
                response = await client.post(api_url, headers=headers, json={"inputs": prompt})
                if response.status_code == 200:
                    save_cached_media(save_name, entity_id, media_type, response.content)
                    mime = "image/webp" if media_type == "image" else "audio/mpeg"
                    return Response(content=response.content, media_type=mime)
                else:
                    last_error = f"Model {mid} failed: {response.status_code} {response.text}"
                    continue
            except Exception as e:
                last_error = str(e)
                continue

        raise HTTPException(status_code=500, detail=f"All generation attempts failed. Last error: {last_error}")

@app.get("/world/npc/{npc_id}")
async def get_npc(npc_id: str):
    # Support retrieving both Players and NPCs, and match by id or name for legacy compatibility
    # RETURN properties(n) ensures we get a dictionary in both mock and live DB
    query = "MATCH (n) WHERE (n:NPC OR n:Player) AND (n.id = $id OR n.name = $id) RETURN properties(n)"
    result = db_client.query(query, {"id": npc_id})
    if not result or len(result) == 0:
        raise HTTPException(status_code=404, detail="Entity not found")

    # result[0][0] is the node properties dictionary
    entity = result[0][0]
    return {"npc": entity}

@app.get("/world/map")
async def get_map():
    # Only return Player and Location nodes. NPCs are tracked but not displayed.
    query = """
    MATCH (n)
    WHERE n:Player OR n:Location
    RETURN labels(n)[0], n.id, n.name, n.x, n.y
    """
    print(f"Executing map query: {query}")
    result = db_client.query(query)
    print(f"Map query result: {result}")
    entities = []
    if result:
        for row in result:
            entities.append({
                "type": row[0],
                "id": row[1] or row[2], # Fallback to name if id is missing
                "name": row[2],
                "x": int(row[3]) if row[3] is not None else 0,
                "y": int(row[4]) if row[4] is not None else 0
            })
    return {"entities": entities}

def robust_json_extract(text: str):
    """Extracts JSON and returns (draft_dict, clean_text)."""
    draft = None
    clean_text = text

    # Try markdown block first
    json_match = re.search(r"(```(?:json)?\s*(.*?)\s*```)", text, re.DOTALL)
    if json_match:
        full_block = json_match.group(1)
        content = json_match.group(2).strip()
        clean_text = text.replace(full_block, "").strip()
        try:
            draft = json.loads(content)
        except:
            try:
                content = re.sub(r",\s*([\]}])", r"\1", content)
                draft = json.loads(content)
            except: pass
        if draft: return draft, clean_text

    # Try searching for any { ... } block
    brace_match = re.search(r"({.*})", text, re.DOTALL)
    if brace_match:
        full_match = brace_match.group(1)
        try:
            draft = json.loads(full_match.strip())
            clean_text = text.replace(full_match, "").strip()
            return draft, clean_text
        except: pass

    return None, text

@app.post("/world/init")
async def init_world(request: WorldInitRequest):
    try:
        print(f"Character creation step for: {request.prompt}")
        user_input = request.prompt or "Hello"
        history = request.history or []

        response_text = await asyncio.to_thread(run_recruiter, user_input, history, request.reasoning_model)

        draft, clean_text = robust_json_extract(response_text)

        return {
            "response": clean_text,
            "draft": draft
        }
    except Exception as e:
        print(f"Error in /world/init: {e}")
        if "BadRequestError" in str(type(e)):
             try:
                 # Attempt to log response content if it exists
                 if hasattr(e, 'response') and hasattr(e.response, 'text'):
                     print(f"Bad Request Response Body: {e.response.text}")
             except: pass
        import traceback
        traceback.print_exc()
        if isinstance(e, HTTPException): raise e
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/world/confirm")
async def confirm_world(request: ConfirmRequest, background_tasks: BackgroundTasks):
    try:
        print(f"Confirming character: {request.draft.name}")
        registrar = create_registrar(request.reasoning_model)
        if not registrar:
            raise HTTPException(status_code=500, detail="Failed to initialize Registrar agent.")

        # Registrar handles DB creation
        # Use by_alias=True to ensure 'class' is used in the JSON passed to the registrar
        await asyncio.to_thread(registrar.run, f"Validated Character Draft: {request.draft.model_dump_json(by_alias=True)}")

        # Use character name as the save slot name
        save_name = request.draft.name
        persistence_manager.current_save_name = save_name

        # Character confirmed - immediate save
        await perform_save(save_name, request.description)

        return {"status": "initialized", "save_name": save_name}
    except Exception as e:
        print(f"Error in /world/confirm: {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/world/bootstrap")
async def bootstrap_world(background_tasks: BackgroundTasks):
    background_tasks.add_task(perform_bootstrap)
    return {"status": "bootstrap_initiated"}

async def perform_bootstrap():
    import json
    rules_path = "dnd_srd_rules.json"
    if not os.path.exists(rules_path):
        print(f"Rules file not found at {rules_path}")
        return

    with open(rules_path, "r") as f:
        rules = json.load(f)

    # Attempt to create indices, ignore if they already exist
    # Using the new syntax for FalkorDB 4.x+
    try:
        db_client.query("CREATE VECTOR INDEX FOR (r:Rule) ON (r.embedding) OPTIONS {dimension: 384, similarityFunction: 'cosine'}")
    except Exception as e:
        if "already exists" not in str(e).lower(): print(f"Notice creating Rule index: {e}")
    try:
        db_client.query("CREATE VECTOR INDEX FOR (m:Memory) ON (m.embedding) OPTIONS {dimension: 384, similarityFunction: 'cosine'}")
    except Exception as e:
        if "already exists" not in str(e).lower(): print(f"Notice creating Memory index: {e}")

    # Fetch all existing rule titles once to optimize bootstrapping
    existing_rules = db_client.query("MATCH (r:Rule) RETURN r.title")
    existing_titles = set()
    if existing_rules:
        for row in existing_rules:
            if isinstance(row, list) and len(row) > 0:
                existing_titles.add(row[0])

    for rule in rules:
        title = rule.get("title")
        if title in existing_titles:
            continue

        content = rule.get("content")
        tags = rule.get("tags", [])
        embedding = vector_model.encode(content)

        query = """
        CREATE (r:Rule {title: $title, content: $content, tags: $tags, embedding: vecf32($embedding)})
        """
        db_client.query(query, {
            "title": title,
            "content": content,
            "tags": tags,
            "embedding": embedding
        })

    print(f"Successfully bootstrapped {len(rules)} rules.")

async def restart_server():
    print("Initiating server restart in 2 seconds...")
    await asyncio.sleep(2)
    sys.exit(0)