import os
import re
import aiohttp
import requests
import json
import subprocess
import asyncio
from io import BytesIO
import uuid

from math import ceil
from tqdm import tqdm
from pathlib import Path
from huggingface_hub import Repository
from PIL import Image, ImageOps
from fastapi import FastAPI, BackgroundTasks
from fastapi_utils.tasks import repeat_every
from fastapi.middleware.cors import CORSMiddleware
import boto3

from db import Database

AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID')
AWS_SECRET_KEY = os.getenv('AWS_SECRET_KEY')
AWS_S3_BUCKET_NAME = os.getenv('AWS_S3_BUCKET_NAME')


HF_TOKEN = os.environ.get("HF_TOKEN")

S3_DATA_FOLDER = Path("sd-multiplayer-data")

DB_FOLDER = Path("diffusers-gallery-data")

s3 = boto3.client(service_name='s3',
                  aws_access_key_id=AWS_ACCESS_KEY_ID,
                  aws_secret_access_key=AWS_SECRET_KEY)


repo = Repository(
    local_dir=DB_FOLDER,
    repo_type="dataset",
    clone_from="huggingface-projects/diffusers-gallery-data",
    use_auth_token=True,
)
repo.git_pull()

database = Database(DB_FOLDER)


async def upload_resize_image_url(session, image_url):
    print(f"Uploading image {image_url}")
    async with session.get(image_url) as response:
        if response.status == 200 and response.headers['content-type'].startswith('image'):
            image = Image.open(BytesIO(await response.read())).convert('RGB')
            # resize image proportional
            image = ImageOps.fit(image, (400, 400), Image.LANCZOS)
            image_bytes = BytesIO()
            image.save(image_bytes, format="JPEG")
            image_bytes.seek(0)
            fname = f'{uuid.uuid4()}.jpg'
            s3.upload_fileobj(Fileobj=image_bytes, Bucket=AWS_S3_BUCKET_NAME, Key="diffusers-gallery/" + fname,
                              ExtraArgs={"ContentType": "image/jpeg", "CacheControl": "max-age=31536000"})
            return fname
    return None


def fetch_models(page=0):
    response = requests.get(
        f'https://huggingface.co/models-json?pipeline_tag=text-to-image&sort=likesRecent&p={page}')
    data = response.json()
    return {
        "models": [model for model in data['models'] if not model['private']],
        "numItemsPerPage": data['numItemsPerPage'],
        "numTotalItems": data['numTotalItems'],
        "pageIndex": data['pageIndex']
    }


def fetch_model_card(model):
    response = requests.get(
        f'https://huggingface.co/{model["id"]}/raw/main/README.md')
    return response.text


async def find_image_in_model_card(text):
    image_regex = re.compile(r'https?://\S+(?:png|jpg|jpeg|webp)')
    urls = re.findall(image_regex, text)
    if not urls:
        return []

    async with aiohttp.ClientSession() as session:
        tasks = [asyncio.ensure_future(upload_resize_image_url(
            session, image_url)) for image_url in urls[0:3]]
        return await asyncio.gather(*tasks)


def run_inference(endpoint, img):
    headers = {'Authorization': f'Bearer {HF_TOKEN}',
               "X-Wait-For-Model": "true",
               "X-Use-Cache": "true"}

    response = requests.post(endpoint, headers=headers, data=img)
    return response.json() if response.ok else []


async def get_all_models():
    initial = fetch_models(0)
    num_pages = ceil(initial['numTotalItems'] / initial['numItemsPerPage'])

    print(
        f"Total items: {initial['numTotalItems']} - Items per page: {initial['numItemsPerPage']}")
    print(f"Found {num_pages} pages")

    # fetch all models
    models = []
    for page in tqdm(range(0, num_pages)):
        print(f"Fetching page {page} of {num_pages}")
        page_models = fetch_models(page)
        models += page_models['models']

    with open(DB_FOLDER / "models_temp.json", "w") as f:
        json.dump(models, f)

    # fetch datacards and images
    print(f"Found {len(models)} models")
    final_models = []
    for model in tqdm(models):
        print(f"Fetching model {model['id']}")
        model_card = fetch_model_card(model)
        images = await find_image_in_model_card(model_card)
        # style = await run_inference(f"https://api-inference.huggingface.co/models/{model['id']}", images[0])
        style = []
        # aesthetic = await run_inference(f"https://api-inference.huggingface.co/models/{model['id']}", images[0])
        aesthetic = []
        final_models.append(
            {**model, "images": images, "style": style, "aesthetic": aesthetic}
        )
    return final_models


async def sync_data():
    print("Fetching models")
    models = await get_all_models()

    with open(DB_FOLDER / "models.json", "w") as f:
        json.dump(models, f)
    # with open(DB_FOLDER / "models.json", "r") as f:
    #     models = json.load(f)
    # open temp db
    print("Updating database")
    with database.get_db() as db:
        cursor = db.cursor()
        for model in models:
            try:
                cursor.execute("INSERT INTO models(id, data) VALUES (?, ?)",
                               [model['id'], json.dumps(model)])
            except Exception as e:
                print(model['id'], model)
        db.commit()
    print("Updating repository")
    subprocess.Popen(
        "git add . && git commit --amend -m 'update' && git push --force", cwd=DB_FOLDER, shell=True)


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


# @ app.get("/sync")
# async def sync(background_tasks: BackgroundTasks):
#     background_tasks.add_task(sync_data)
#     return "Synced data to huggingface datasets"


MAX_PAGE_SIZE = 30


@ app.get("/api/models")
def get_page(page: int = 1):
    page = page if page > 0 else 1
    with database.get_db() as db:
        cursor = db.cursor()
        cursor.execute("""
            SELECT *, COUNT(*) OVER() AS total
            FROM models
            ORDER BY json_extract(data, '$.likes') DESC
            LIMIT ? OFFSET ?
        """, (MAX_PAGE_SIZE, (page - 1) * MAX_PAGE_SIZE))
        results = cursor.fetchall()
        total = results[0][3] if results else 0
        total_pages = (total + MAX_PAGE_SIZE - 1) // MAX_PAGE_SIZE

    return {
        "models": [json.loads(result[1]) for result in results],
        "totalPages": total_pages
    }


@app.get("/")
def read_root():
    return "Just a bot to sync data from diffusers gallery"


# @app.on_event("startup")
# @repeat_every(seconds=60 * 60 * 24, wait_first=True)
# async def repeat_sync():
#     await sync_data()
#     return "Synced data to huggingface datasets"