from fastapi import FastAPI, UploadFile, File
from fastapi.responses import FileResponse
from fastapi.responses import HTMLResponse, FileResponse
from fastapi.staticfiles import StaticFiles
from typing import Tuple
import cv2
import fastai
from fastai.vision import *
from fastai.utils.mem import *
from fastai.vision import open_image, load_learner, image, torch
import numpy as np
import urllib.request
import PIL.Image
from io import BytesIO
import torchvision.transforms as T
from PIL import Image
import requests
from io import BytesIO
import fastai
from fastai.vision import *
from fastai.utils.mem import *
from fastai.vision import open_image, load_learner, image, torch
import numpy as np
import urllib.request
import PIL.Image
from PIL import Image
from io import BytesIO
import torchvision.transforms as T

app = FastAPI()

class FeatureLoss(nn.Module):
    def __init__(self, m_feat, layer_ids, layer_wgts):
        super().__init__()
        self.m_feat = m_feat
        self.loss_features = [self.m_feat[i] for i in layer_ids]
        self.hooks = hook_outputs(self.loss_features, detach=False)
        self.wgts = layer_wgts
        self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))
              ] + [f'gram_{i}' for i in range(len(layer_ids))]

    def make_features(self, x, clone=False):
        self.m_feat(x)
        return [(o.clone() if clone else o) for o in self.hooks.stored]
    
    def forward(self, input, target):
        out_feat = self.make_features(target, clone=True)
        in_feat = self.make_features(input)
        self.feat_losses = [base_loss(input,target)]
        self.feat_losses += [base_loss(f_in, f_out)*w
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.metrics = dict(zip(self.metric_names, self.feat_losses))
        return sum(self.feat_losses)
    
    def __del__(self): self.hooks.remove()

def add_margin(pil_img, top, right, bottom, left, color):
    width, height = pil_img.size
    new_width = width + right + left
    new_height = height + top + bottom
    result = Image.new(pil_img.mode, (new_width, new_height), color)
    result.paste(pil_img, (left, top))
    return result

from fastapi import HTTPException
from httpx import AsyncClient

MODEL_URL = "https://www.dropbox.com/s/04suaimdpru76h3/ArtLine_920.pkl?dl=1"

async def download_model(url: str, filename: str):
    async with AsyncClient() as client:
        response = await client.get(url)
        if response.status_code == 200:
            with open(filename, "wb") as f:
                f.write(response.content)
        else:
            raise HTTPException(status_code=response.status_code, detail="Failed to download model")

# Define the function to download the model asynchronously
async def setup_model():
    await download_model(MODEL_URL, "ArtLine_920.pkl")

# Run the setup function to download the model before the FastAPI app starts
import asyncio
loop = asyncio.get_event_loop()
loop.run_until_complete(setup_model())

# Now load the learner once the model is downloaded
path = Path(".")
learn = load_learner(path, 'ArtLine_920.pkl')




import gradio as gr
import cv2


def get_filename(prefix="sketch"):
  from datetime import datetime
  from pytz import timezone
  return datetime.now(timezone('Asia/Seoul')).strftime('sketch__%Y-%m-%d %H:%M:%S.jpg')

def predict(img):
  img = PIL.Image.fromarray(img)
  img = add_margin(img, 250, 250, 250, 250, (255, 255, 255))
  img = np.array(img)

  h, w = img.shape[:-1]
  cv2.imwrite("test.jpg", img)
  img_test = open_image("test.jpg")

  p,img_hr,b = learn.predict(img_test)

  res = (img_hr / img_hr.max()).numpy()
  res = res[0]  # take only first channel as result
  res = cv2.resize(res, (w,h))

  output_file = get_filename()

  cv2.imwrite(output_file, (res * 255).astype(np.uint8), [cv2.IMWRITE_JPEG_QUALITY, 50])

  return res, output_file

@app.post("/predict/")
async def predict(file: UploadFile = File(...)) -> Tuple[str, bytes]:
    contents = await file.read()
    img = cv2.imdecode(np.fromstring(contents, np.uint8), cv2.IMREAD_COLOR)
    img = PIL.Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    img = add_margin(img, 250, 250, 250, 250, (255, 255, 255))
    img = np.array(img)

    h, w = img.shape[:-1]
    cv2.imwrite("test.jpg", img)
    img_test = open_image("test.jpg")

    p,img_hr,b = learn.predict(img_test)

    res = (img_hr / img_hr.max()).numpy()
    res = res[0]  # take only first channel as result
    res = cv2.resize(res, (w,h))

    output_file = get_filename()

    cv2.imwrite(output_file, (res * 255).astype(np.uint8), [cv2.IMWRITE_JPEG_QUALITY, 50])

    return output_file, res.tobytes()

app.mount("/", StaticFiles(directory="static", html=True), name="static")

@app.get("/")
def index() -> FileResponse:
    return FileResponse(path="/app/static/index.html", media_type="text/html")