"""
RAG Agent FastAPI Server using OpenAI Agents SDK

Provides POST /chat endpoint for grounded Q&A using OpenAI Agents SDK
and retrieval from Qdrant via Spec-2's retrieve.py module.
"""

import os
import sys
import uuid
import asyncio
from datetime import datetime
from typing import List, Dict, Any, Optional

from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field, validator
from dotenv import load_dotenv

# Load environment first
load_dotenv()

from agents import OpenAIChatCompletionsModel
from openai import AsyncOpenAI

# Get OpenRouter API key from environment
OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
if not OPENROUTER_API_KEY:
    raise ValueError(
        "OPENROUTER_API_KEY environment variable must be set. "
        "Get a free key from https://openrouter.ai/"
    )

# Configure AsyncOpenAI client for OpenRouter
client = AsyncOpenAI(
    api_key=OPENROUTER_API_KEY,
    base_url="https://openrouter.ai/api/v1",
)

# Use OpenRouter's free model: tencent/hy3-preview:free
third_party_model = OpenAIChatCompletionsModel(
    openai_client=client, model="tencent/hy3-preview:free"
)

# Make backend package importable
current_dir = os.path.dirname(os.path.abspath(__file__))
if current_dir not in sys.path:
    sys.path.insert(0, current_dir)

# Import backend modules
from config import get_config
from retrieve import search as retrieve_search
from logging_config import setup_logging

# Import OpenAI Agents SDK (must be installed separately)
try:
    from agents import Agent, Runner, function_tool, ModelSettings, ToolCallOutputItem
except ImportError:
    raise ImportError(
        "openai-agents package required. Install: pip install openai-agents"
    )

# Setup logging
logger = setup_logging("agent")

# Initialize FastAPI app
app = FastAPI(
    title="RAG Book Chatbot API",
    version="1.0.0",
    description="Chatbot for humanoid robotics book using OpenAI Agents SDK",
)

# ============ CORS Configuration ============

app.add_middleware(
    CORSMiddleware,
    allow_origins=[
        "http://localhost:3000",
        "http://127.0.0.1:3000",
        "https://hackathon-1-humanoid-ai-robotics.vercel.app",
        "https://*.vercel.app",
    ],
    allow_credentials=True,
    allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
    allow_headers=["Content-Type", "Authorization"],
)

# ============ Pydantic Models ============


class ChatRequest(BaseModel):
    question: str = Field(..., min_length=1, max_length=1000)

    @validator("question")
    def validate_question(cls, v):
        if not v or not v.strip():
            raise ValueError("Question cannot be empty")
        return v.strip()


class Source(BaseModel):
    url: str
    chunk_index: int
    text_snippet: str


class ChatResponse(BaseModel):
    answer: str
    sources: List[Source]
    tokens_used: int
    agent_trace: Optional[str] = None


class HealthStatus(BaseModel):
    status: str
    qdrant: str
    openai: str
    timestamp: str


# ============ Retrieval Tool ============


@function_tool
def retrieve_chunks(query: str, top_k: int = 5) -> List[Dict[str, Any]]:
    """
    Retrieve relevant book chunks from Qdrant.

    Args:
        query: User's question
        top_k: Number of chunks to retrieve (default: 5, max: 10)

    Returns:
        List of chunks with url, chunk_index, text, score, and source_number
    """
    logger.info(
        f"[Tool] retrieve_chunks called: query='{query[:100]}...', top_k={top_k}"
    )

    try:
        import cohere
        from qdrant_client import QdrantClient

        cfg = get_config()
        cohere_client = cohere.ClientV2(api_key=cfg["cohere_api_key"])
        qdrant_client = QdrantClient(
            url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"]
        )
        collection_name = cfg["qdrant_collection"]

        results = retrieve_search(
            query_text=query,
            cohere_client=cohere_client,
            qdrant_client=qdrant_client,
            collection_name=collection_name,
            top_k=top_k,
        )

        chunks = []
        for i, result in enumerate(results):
            payload = result.get("payload", {})
            chunks.append(
                {
                    "url": payload.get("url", ""),
                    "chunk_index": payload.get("chunk_index", i),
                    "text": payload.get("text", ""),
                    "score": result.get("score", 0.0),
                    "source_number": i + 1,
                }
            )

        logger.info(f"[Tool] Retrieved {len(chunks)} chunks")
        return chunks

    except Exception as e:
        logger.error(f"[Tool] Retrieval failed: {e}", exc_info=True)
        raise


# ============ Agent Definition ============


def get_agent_instructions() -> str:
    return """You are a helpful assistant answering questions about a humanoid robotics book.

IMPORTANT GROUNDING RULES:
1. Answer ONLY using the retrieved book content provided by the retrieve_chunks tool.
2. Do NOT use external knowledge or make up information.
3. If the retrieved content does not contain relevant information, say "I couldn't find relevant information in the book."
4. Always cite your sources using the format [Source 1], [Source 2], etc. Each source number corresponds to the chunk number from the tool.
5. Be concise and accurate.

Your responses should be helpful, clear, and grounded exclusively in the provided context."""


def create_agent():
    return Agent(
        name="RAG Book Assistant",
        instructions=get_agent_instructions(),
        tools=[retrieve_chunks],
        model=third_party_model,
        model_settings=ModelSettings(temperature=0.7, max_tokens=500),
    )


_agent_instance = None


def get_agent():
    """Lazy singleton agent instance."""
    global _agent_instance
    if _agent_instance is None:
        _agent_instance = create_agent()
    return _agent_instance


# ============ Health Checks ============


def check_qdrant_health() -> str:
    try:
        from qdrant_client import QdrantClient

        cfg = get_config()
        client = QdrantClient(url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"])
        client.get_collection(cfg["qdrant_collection"])
        return "connected"
    except Exception as e:
        logger.warning(f"Qdrant health check failed: {e}")
        return "disconnected"


def check_openai_health() -> str:
    try:
        api_key = os.getenv("OPENAI_API_KEY")
        if not api_key:
            return "disconnected"
        import openai

        client = openai.OpenAI(api_key=api_key)
        # Simple models.list call to verify API connectivity
        client.models.list()
        return "connected"
    except Exception as e:
        logger.warning(f"OpenAI health check failed: {e}")
        return "disconnected"


# ============ FastAPI Endpoints ============


@app.post("/chat")
async def chat_endpoint(request: ChatRequest):
    request_id = str(uuid.uuid4())[:8]
    question = request.question.strip()

    logger.info(f"[{request_id}] Received chat: {question[:100]}...")

    try:
        logger.info(f"[{request_id}] Initializing agent...")
        agent = get_agent()
        logger.info(f"[{request_id}] Agent initialized successfully")

        # Use async Runner.run (native async, no blocking)
        logger.info(f"[{request_id}] Starting agent run...")
        result = await asyncio.wait_for(
            Runner.run(agent, question),
            timeout=60.0,  # Increased to 60s to handle large questions
        )
        logger.info(f"[{request_id}] Agent run completed")

        # Extract sources from tool call outputs
        sources = []
        if result.new_items:
            for item in result.new_items:
                if isinstance(item, ToolCallOutputItem):
                    output = item.output
                    if isinstance(output, list):
                        for chunk in output:
                            sources.append(
                                Source(
                                    url=chunk.get("url", ""),
                                    chunk_index=chunk.get("chunk_index", 0),
                                    text_snippet=chunk.get("text", "")[:200],
                                )
                            )

        # Get token usage
        tokens_used = 0
        if result.context_wrapper and hasattr(result.context_wrapper, "usage"):
            tokens_used = result.context_wrapper.usage.total_tokens

        response = ChatResponse(
            answer=result.final_output,
            sources=sources,
            tokens_used=tokens_used,
            agent_trace=f"{request_id}: completed",
        )

        logger.info(
            f"[{request_id}] Completed: tokens={tokens_used}, sources={len(sources)}"
        )
        return response

    except asyncio.TimeoutError:
        logger.error(f"[{request_id}] Timeout after 60s")
        return JSONResponse(
            status_code=504,
            content={
                "error": "timeout",
                "message": "The chatbot is taking too long to respond. Please try a shorter or simpler question.",
            },
        )

    except Exception as e:
        logger.error(
            f"[{request_id}] Error during chat: {type(e).__name__}: {e}", exc_info=True
        )

        # Check for specific error types
        error_str = str(e).lower()
        if (
            "api" in error_str
            or "authentication" in error_str
            or "401" in error_str
            or "403" in error_str
        ):
            status_code = 503
            error_code = "api_auth_failed"
            message = "Authentication failed with API provider. Check your OPENROUTER_API_KEY."
        elif "openrouter" in error_str or "openai" in error_str:
            status_code = 503
            error_code = "openai_failed"
            message = f"API service error: {str(e)[:100]}"
        else:
            status_code = 500
            error_code = "internal_error"
            message = f"Internal error: {str(e)[:100]}"

        logger.error(f"[{request_id}] Returning {status_code}: {error_code}")
        return JSONResponse(
            status_code=status_code,
            content={"error": error_code, "message": message, "request_id": request_id},
        )


@app.get("/health", response_model=HealthStatus)
async def health_check():
    request_id = str(uuid.uuid4())[:8]
    qdrant = check_qdrant_health()
    openai = check_openai_health()  # sync call
    status = (
        "healthy" if qdrant == "connected" and openai == "connected" else "degraded"
    )
    return HealthStatus(
        status=status,
        qdrant=qdrant,
        openai=openai,
        timestamp=datetime.utcnow().isoformat() + "Z",
    )


@app.get("/test-agent")
async def test_agent_endpoint():
    """Test endpoint to verify agent can be initialized."""
    request_id = str(uuid.uuid4())[:8]
    try:
        logger.info(f"[{request_id}] Testing agent initialization...")
        agent = get_agent()
        logger.info(f"[{request_id}] Agent initialized successfully")
        return {
            "status": "ok",
            "message": "Agent initialized successfully",
            "agent_name": agent.name if hasattr(agent, "name") else "unknown",
        }
    except Exception as e:
        logger.error(f"[{request_id}] Agent init test failed: {e}", exc_info=True)
        return JSONResponse(
            status_code=500,
            content={
                "status": "error",
                "message": f"Agent initialization failed: {str(e)}",
                "request_id": request_id,
            },
        )


@app.on_event("startup")
async def startup_event():
    logger.info("=" * 60)
    logger.info("RAG Agent FastAPI Server Starting")
    logger.info("=" * 60)

    if not os.getenv("OPENROUTER_API_KEY"):
        logger.error("OPENROUTER_API_KEY not set - chat will fail!")
    else:
        logger.info("OPENROUTER_API_KEY is configured")

    # Test retrieval
    try:
        import cohere
        from qdrant_client import QdrantClient

        cfg = get_config()
        cohere_client = cohere.ClientV2(api_key=cfg["cohere_api_key"])
        qdrant_client = QdrantClient(
            url=cfg["qdrant_url"], api_key=cfg["qdrant_api_key"]
        )
        test_result = retrieve_search(
            query_text="test",
            cohere_client=cohere_client,
            qdrant_client=qdrant_client,
            collection_name=cfg["qdrant_collection"],
            top_k=1,
        )
        logger.info(f"Retrieval test OK: {len(test_result)} results")
    except Exception as e:
        logger.error(f"Retrieval test failed: {e}")

    logger.info("Server startup complete")


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)