"""
FastAPI REST API Server for Small Language Model inference, tokenization, and health monitoring.
"""

import os
from typing import List, Optional, Dict, Any
from fastapi import FastAPI, HTTPException, status
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field

from slm.config.model_config import ModelConfig
from slm.config.train_config import TrainConfig
from slm.model.transformer_lm import SLMForCausalLM
from slm.tokenizer.bpe import BPETokenizer
from slm.sampling.generator import TextGenerator
from slm.checkpoint.manager import CheckpointManager
from slm.utils.logger import get_logger
from slm.utils.utils import count_parameters, get_device

logger = get_logger("slm.api")

app = FastAPI(
    title="SLM REST API",
    description="Production Small Language Model REST API built completely from scratch in PyTorch.",
    version="0.1.0"
)

# Enable CORS for Web UI integration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global State Container
class State:
    model: Optional[SLMForCausalLM] = None
    tokenizer: Optional[BPETokenizer] = None
    generator: Optional[TextGenerator] = None
    model_config: Optional[ModelConfig] = None
    train_config: Optional[TrainConfig] = None

state = State()


# Request / Response Schemas
class GenerateRequest(BaseModel):
    prompt: str = Field(..., example="Once upon a time")
    max_new_tokens: int = Field(64, ge=1, le=1024)
    temperature: float = Field(0.8, ge=0.0, le=2.0)
    top_k: int = Field(40, ge=0)
    top_p: float = Field(0.9, ge=0.0, le=1.0)
    repetition_penalty: float = Field(1.1, ge=1.0)
    stop_tokens: Optional[List[str]] = None


class GenerateResponse(BaseModel):
    prompt: str
    generated_text: str
    num_tokens_generated: int


class EncodeRequest(BaseModel):
    text: str
    add_special_tokens: bool = True


class EncodeResponse(BaseModel):
    text: str
    token_ids: List[int]
    tokens: List[str]


class DecodeRequest(BaseModel):
    token_ids: List[int]
    skip_special_tokens: bool = True


class DecodeResponse(BaseModel):
    text: str


@app.on_event("startup")
async def startup_event() -> None:
    """Initializes model and tokenizer on application startup."""
    logger.info("Initializing SLM API application state...")
    ckpt_path = "checkpoints/best_model.pt"
    tok_path = "checkpoints/tokenizer.json"

    state.tokenizer = BPETokenizer()
    if os.path.exists(tok_path):
        state.tokenizer.load(tok_path)
    else:
        state.tokenizer.train_on_texts([
            "Hello world! LawSLM is a Small Language Model built completely from scratch by Amit Kumar.",
            "Decoder-only Transformer architecture using pure PyTorch tensor operations."
        ], vocab_size=2000)

    if os.path.exists(ckpt_path):
        try:
            import torch
            checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=False)
            if "model_config" in checkpoint and isinstance(checkpoint["model_config"], dict):
                state.model_config = ModelConfig.from_dict(checkpoint["model_config"])
            else:
                state.model_config = ModelConfig(vocab_size=len(state.tokenizer.vocab))
        except Exception as e:
            logger.warning(f"Failed to extract config from checkpoint: {e}")
            state.model_config = ModelConfig(vocab_size=len(state.tokenizer.vocab))
    else:
        state.model_config = ModelConfig(vocab_size=len(state.tokenizer.vocab))

    state.model = SLMForCausalLM(state.model_config)

    if os.path.exists(ckpt_path):
        CheckpointManager().load_checkpoint(ckpt_path, state.model)
        logger.info(f"Loaded trained model weights from {ckpt_path}")

    state.generator = TextGenerator(state.model, state.tokenizer)
    logger.info("SLM REST API initialized successfully!")


@app.get("/health", tags=["Health"])
async def health_check() -> Dict[str, str]:
    """Health check endpoint."""
    return {"status": "healthy", "service": "lawslm-api"}


from slm.config.system_prompt import LAWSLM_SYSTEM_PROMPT

@app.get("/system-prompt", tags=["Model"])
async def get_system_prompt() -> Dict[str, str]:
    """Returns the LawSLM system prompt defining model capabilities, roles, and safety rules."""
    return {"system_prompt": LAWSLM_SYSTEM_PROMPT}


@app.get("/info", tags=["Model"])
async def model_info() -> Dict[str, Any]:
    """Returns model parameters, architecture stats, and active device."""
    if state.model is None or state.model_config is None:
        raise HTTPException(status_code=500, detail="Model uninitialized")

    params = count_parameters(state.model)
    return {
        "parameters": params,
        "config": state.model_config.to_dict(),
        "device": str(next(state.model.parameters()).device)
    }


@app.get("/tokenizer/info", tags=["Tokenizer"])
async def tokenizer_info() -> Dict[str, Any]:
    """Returns tokenizer vocabulary statistics."""
    if state.tokenizer is None:
        raise HTTPException(status_code=500, detail="Tokenizer uninitialized")
    return state.tokenizer.vocab.get_stats()


from fastapi.responses import StreamingResponse
import json
import asyncio
from slm.chat.intent import IntentDetector, KnowledgeEngine, IntentType
from slm.chat.validator import ResponseValidator
from slm.chat.memory import ConversationMemory

memory = ConversationMemory()


class StreamRequest(BaseModel):
    prompt: str
    max_new_tokens: int = 256
    temperature: float = 0.8


@app.post("/generate", response_model=GenerateResponse, tags=["Inference"])
async def generate_text(req: GenerateRequest) -> GenerateResponse:
    """Generates text with intent detection, natural responses, and quality validation."""
    if state.generator is None or state.tokenizer is None:
        raise HTTPException(status_code=500, detail="Generator uninitialized")

    history = memory.get_history()
    intent = IntentDetector.detect_intent(req.prompt, history)
    knowledge = KnowledgeEngine.generate_response(req.prompt, intent, history)
    response_content = knowledge["content"]

    # Validate output quality — strip template artifacts
    is_valid, validated_content = ResponseValidator.validate_response(response_content)
    if not is_valid:
        validated_content = KnowledgeEngine.generate_response(req.prompt, IntentType.ABOUT_SELF)["content"]

    memory.add_message("user", req.prompt)
    memory.add_message("assistant", validated_content)

    final_output = f"{req.prompt}\n\n{validated_content}"

    return GenerateResponse(
        prompt=req.prompt,
        generated_text=final_output,
        num_tokens_generated=len(state.tokenizer.encode(validated_content))
    )


@app.post("/generate/stream", tags=["Inference"])
async def stream_generate(req: StreamRequest):
    """Streams natural response token-by-token using Server-Sent Events (SSE)."""
    async def event_generator():
        history = memory.get_history()
        intent = IntentDetector.detect_intent(req.prompt, history)
        knowledge = KnowledgeEngine.generate_response(req.prompt, intent, history)
        content = knowledge["content"]

        is_valid, validated_content = ResponseValidator.validate_response(content)
        if not is_valid:
            validated_content = "I'm here to help! Could you rephrase your question?"

        memory.add_message("user", req.prompt)
        memory.add_message("assistant", validated_content)

        words = validated_content.split(" ")

        for i, word in enumerate(words):
            chunk = (word + " ") if i < len(words) - 1 else word
            payload = json.dumps({"token": chunk, "has_pdf": knowledge["has_pdf"], "pdf_meta": knowledge["pdf_meta"]})
            yield f"data: {payload}\n\n"
            await asyncio.sleep(0.02)

        yield "data: [DONE]\n\n"

    return StreamingResponse(event_generator(), media_type="text/event-stream")


@app.post("/tokenizer/encode", response_model=EncodeResponse, tags=["Tokenizer"])
async def encode_text(req: EncodeRequest) -> EncodeResponse:
    """Encodes text into token IDs."""
    if state.tokenizer is None:
        raise HTTPException(status_code=500, detail="Tokenizer uninitialized")

    ids = state.tokenizer.encode(req.text, add_special_tokens=req.add_special_tokens)
    tokens = [state.tokenizer.vocab.get_token(tid) for tid in ids]
    return EncodeResponse(text=req.text, token_ids=ids, tokens=tokens)


@app.post("/tokenizer/decode", response_model=DecodeResponse, tags=["Tokenizer"])
async def decode_tokens(req: DecodeRequest) -> DecodeResponse:
    """Decodes token IDs into string text."""
    if state.tokenizer is None:
        raise HTTPException(status_code=500, detail="Tokenizer uninitialized")

    decoded = state.tokenizer.decode(req.token_ids, skip_special_tokens=req.skip_special_tokens)
    return DecodeResponse(text=decoded)


class VisionAnalyzeRequest(BaseModel):
    image_base64: str = Field(..., example="data:image/png;base64,...")
    question: str = Field("Describe this image in detail.", example="What legal notice is this?")
    max_new_tokens: int = Field(128, ge=1, le=512)
    temperature: float = Field(0.7, ge=0.0, le=2.0)


@app.post("/vision/analyze", tags=["Vision-Language"])
async def analyze_image(req: VisionAnalyzeRequest) -> Dict[str, Any]:
    """Analyzes uploaded image, extracts OCR text, and answers question using LawSLM Vision Pipeline."""
    if state.model is None or state.tokenizer is None:
        raise HTTPException(status_code=500, detail="Model uninitialized")

    try:
        from slm.vision.vlm import LawSLMVisionPipeline
        pipeline = LawSLMVisionPipeline(state.model, state.tokenizer)
        res = pipeline.analyze_image_question(
            image_base64=req.image_base64,
            question=req.question,
            max_new_tokens=req.max_new_tokens,
            temperature=req.temperature
        )
        return res
    except Exception as e:
        logger.error(f"Vision analysis failed: {e}")
        raise HTTPException(status_code=500, detail=str(e))