import numpy as np
import PIL.Image
import torch
from diffusers import LCMScheduler, AutoPipelineForText2Image
from fastapi import FastAPI
import uvicorn
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse, StreamingResponse
import io
import os
from pathlib import Path
from db import Database
import uuid
import logging
from fastapi import FastAPI, Request, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from asyncio import Lock

logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO"))

SPACE_ID = os.environ.get("SPACE_ID", "")
DEV = os.environ.get("DEV", "0") == "1"

DB_PATH = Path("/data/cache") if SPACE_ID else Path("./cache")
IMGS_PATH = DB_PATH / "imgs"
DB_PATH.mkdir(exist_ok=True, parents=True)
IMGS_PATH.mkdir(exist_ok=True, parents=True)

database = Database(DB_PATH)
generate_lock = Lock()


model_id = "segmind/Segmind-Vega"
adapter_id = "segmind/Segmind-VegaRT"

dtype = torch.bfloat16
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    pipe = AutoPipelineForText2Image.from_pretrained(
        model_id, torch_dtype=torch.float16, variant="fp16"
    )
    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
    pipe.to("cuda")
    pipe.load_lora_weights(adapter_id)
    pipe.fuse_lora()


def generate(
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
) -> PIL.Image.Image:

    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        generator=generator,
        num_inference_steps=4,
        guidance_scale=0,
    ).images[0]

    return image


app = FastAPI()
origins = [
    "https://huggingface.co",
    "http://huggingface.co",
    "https://huggingface.co/",
    "http://huggingface.co/",
]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.middleware("http")
async def validate_origin(request: Request, call_next):
    if DEV:
        return await call_next(request)
    if request.headers.get("referer") not in origins:
        raise HTTPException(status_code=403, detail="Forbidden")
    return await call_next(request)


@app.get("/image")
async def generate_image(
    prompt: str, negative_prompt: str = "", seed: int = 2134213213
):
    cached_img = database.check(prompt, negative_prompt, seed)
    if cached_img:
        logging.info(f"Image found in cache: {cached_img[0]}")
        return StreamingResponse(open(cached_img[0], "rb"), media_type="image/jpeg")

    logging.info(f"Image not found in cache, generating new image")
    async with generate_lock:
        pil_image = generate(prompt, negative_prompt, seed)
        img_id = str(uuid.uuid4())
        img_path = IMGS_PATH / f"{img_id}.jpg"
        pil_image.save(img_path)
        img_io = io.BytesIO()
        pil_image.save(img_io, "JPEG")
        img_io.seek(0)
        database.insert(prompt, negative_prompt, str(img_path), seed)

    return StreamingResponse(img_io, media_type="image/jpeg")


@app.get("/")
async def main():
    # redirect to https://huggingface.co/spaces/multimodalart/stable-cascade
    return RedirectResponse(
        "https://multimodalart-stable-cascade.hf.space/?__theme=system"
    )


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)