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

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.responses import HTMLResponse

from fastapi_utils.tasks import repeat_every
from fastapi.middleware.cors import CORSMiddleware
import boto3
from datetime import datetime
from db import Database

AWS_ACCESS_KEY_ID = os.getenv("MY_AWS_ACCESS_KEY_ID")
AWS_SECRET_KEY = os.getenv("MY_AWS_SECRET_KEY")
AWS_S3_BUCKET_NAME = os.getenv("MY_AWS_S3_BUCKET_NAME")


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

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

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

CLASSIFIER_URL = (
    "https://radames-aesthetic-style-nsfw-classifier.hf.space/run/inference"
)
ASSETS_URL = "https://d26smi9133w0oo.cloudfront.net/diffusers-gallery/"

BLOCKED_MODELS_REGEX = re.compile(r"(CyberHarem)", re.IGNORECASE)

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}")
    try:
        async with session.get(image_url) as response:
            if response.status == 200 and (
                response.headers["content-type"].startswith("image")
                or response.headers["content-type"].startswith("application")
            ):
                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
    except Exception as e:
        print(f"Error uploading image {image_url}: {e}")
        return None


def fetch_models(page=0):
    response = requests.get(
        f"https://huggingface.co/models-json?pipeline_tag=text-to-image&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_id):
    response = requests.get(f"https://huggingface.co/{model_id}/raw/main/README.md")
    return response.text


REGEX = re.compile(r'---(.*?)---', re.DOTALL)

def get_yaml_data(text_content):
    matches = REGEX.findall(text_content)
    yaml_block = matches[0].strip() if matches else None
    if yaml_block:
        try: 
            data_dict = yaml.safe_load(yaml_block)
            return data_dict
        except yaml.YAMLError as exc:
            print(exc)
    return {}

async def find_image_in_model_card(text, model_id):
    base_url = f"https://huggingface.co/{model_id}/resolve/main/"
    image_regex = re.compile(r"!\[.*\]\((.*?\.(png|jpg|jpeg|gif|bmp|webp))\)|src=\"(.*?\.(png|jpg|jpeg|gif|bmp|webp))\">", re.IGNORECASE)
    matches = image_regex.findall(text)
    urls = []
    for match in matches:
        for url in match:
            if url: 
                if not url.startswith("http") and not url.startswith("https"):
                    url = base_url + url 
                urls.append(url)

    if len(urls) == 0:
        return []

    print(urls)
    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_classifier(images):
    images = [i for i in images if i is not None]
    if len(images) > 0:
        # classifying only the first image
        images_urls = [ASSETS_URL + images[0]]
        response = requests.post(
            CLASSIFIER_URL,
            json={
                "data": [
                    {"urls": images_urls},  # json urls: list of images urls
                    False,  # enable/disable gallery image output
                    None,  # single image input
                    None,  # files input
                ]
            },
        ).json()

        # data response is array data:[[{img0}, {img1}, {img2}...], Label, Gallery],
        class_data = response["data"][0][0]
        class_data_parsed = {row["label"]: round(row["score"], 3) for row in class_data}

        # update row data with classificator data
        return class_data_parsed
    else:
        return {}


async def get_all_new_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
    new_models = []
    for page in tqdm(range(0, num_pages)):
        print(f"Fetching page {page} of {num_pages}")
        page_models = fetch_models(page)
        new_models += page_models["models"]
    return new_models


async def sync_data():
    print("Fetching models")
    repo.git_pull()
    all_models = await get_all_new_models()
    print(f"Found {len(all_models)} models")
    # save list of all models for ids
    with open(DB_FOLDER / "models.json", "w") as f:
        json.dump(all_models, f)
    # with open(DB_FOLDER / "models.json", "r") as f:
    #     all_models = json.load(f)

    new_models_ids = [model["id"] for model in all_models]
    new_models_ids = [model_id for model_id in new_models_ids if not re.match(BLOCKED_MODELS_REGEX, model_id)]

    # get existing models
    with database.get_db() as db:
        cursor = db.cursor()
        cursor.execute("SELECT id FROM models")
        existing_models = [row["id"] for row in cursor.fetchall()]
    models_ids_to_add = list(set(new_models_ids) - set(existing_models))
    # find all models id to add from new_models
    models = [model for model in all_models if model["id"] in models_ids_to_add]

    print(f"Found {len(models)} new models")
    for model in tqdm(models):
        model_id = model["id"]
        print(f"\n\nFetching model {model_id}")
        likes = model["likes"]
        downloads = model["downloads"]
        print("Fetching model card")
        model_card = fetch_model_card(model_id)
        print("Parsing model card")
        model_card_data = get_yaml_data(model_card)
        print("Finding images in model card")
        images = await find_image_in_model_card(model_card, model_id)

        classifier = run_classifier(images)
        print(images, classifier)
        # update model row with image and classifier data
        with database.get_db() as db:
            cursor = db.cursor()
            cursor.execute(
                "INSERT INTO models(id, data, likes, downloads) VALUES (?, ?, ?, ?)",
                [
                    model_id,
                    json.dumps(
                        {
                            **model,
                            "meta": model_card_data,
                            "images": images,
                            "class": classifier,
                        }
                    ),
                    likes,
                    downloads,
                ],
            )
            db.commit()
    print("\n\n\n\nTry to update images again\n\n\n")
    with database.get_db() as db:
        cursor = db.cursor()
        cursor.execute("SELECT * from models")
        to_all_models = list(cursor.fetchall())
        models_no_images = []
        for model in to_all_models:
            model_data = json.loads(model["data"])
            images = model_data["images"]
            filtered_images = [x for x in images if x is not None]
            if len(filtered_images) == 0:
                models_no_images.append(model)

        for model in tqdm(models_no_images):
            model_id = model["id"]
            model_data = json.loads(model["data"])
            print(f"\n\nFetching model {model_id}")
            model_card = fetch_model_card(model_id)
            print("Parsing model card")
            model_card_data = get_yaml_data(model_card)
            print("Finding images in model card")
            images = await find_image_in_model_card(model_card, model_id)
            classifier = run_classifier(images)
            model_data["images"] = images
            model_data["class"] = classifier
            model_data["meta"] = model_card_data
            # update model row with image and classifier data
            with database.get_db() as db:
                cursor = db.cursor()
                cursor.execute(
                    "UPDATE models SET data = ? WHERE id = ?",
                    [json.dumps(model_data), model_id],
                )
                db.commit()

    print("Update likes and downloads")
    for model in tqdm(all_models):
        model_id = model["id"]
        likes = model["likes"]
        downloads = model["downloads"]
        with database.get_db() as db:
            cursor = db.cursor()
            cursor.execute(
                "UPDATE models SET likes = ?, downloads = ? WHERE id = ?",
                [likes, downloads, model_id],
            )
            db.commit()

    print("Updating DB repository")
    time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    cmd = f"git add . && git commit --amend -m 'update at {time}' && git push --force"
    print(cmd)
    subprocess.Popen(cmd, 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):
#     await sync_data()
#     return "Synced data to huggingface datasets"


MAX_PAGE_SIZE = 30


class Sort(str, Enum):
    trending = "trending"
    recent = "recent"
    likes = "likes"


class Style(str, Enum):
    all = "all"
    anime = "anime"
    s3D = "3d"
    realistic = "realistic"
    nsfw = "nsfw"
    lora = "lora"


@app.get("/api/models")
def get_page(
    page: int = 1, sort: Sort = Sort.trending, style: Style = Style.all, tag: str = None
):
    page = page if page > 0 else 1
    if sort == Sort.trending:
        sort_query = "likes / MYPOWER((JULIANDAY('now') - JULIANDAY(datetime(json_extract(data, '$.lastModified')))) + 2, 2) DESC"
    elif sort == Sort.recent:
        sort_query = "datetime(json_extract(data, '$.lastModified')) DESC"
    elif sort == Sort.likes:
        sort_query = "likes DESC"

    if style == Style.all:
        style_query = "isNFSW = false"
    elif style == Style.anime:
        style_query = "json_extract(data, '$.class.anime') > 0.1 AND isNFSW = false"
    elif style == Style.s3D:
        style_query = "json_extract(data, '$.class.3d') > 0.1 AND isNFSW = false"
    elif style == Style.realistic:
        style_query = "json_extract(data, '$.class.real_life') > 0.1 AND isNFSW = false"
    elif style == Style.lora:
        style_query = "json_extract(data, '$.meta.tags') LIKE '%lora%' AND isNFSW = false"
    elif style == Style.nsfw:
        style_query = "isNFSW = true"

    

    with database.get_db() as db:
        cursor = db.cursor()
        cursor.execute(
            f"""
                SELECT *,
                        COUNT(*) OVER() AS total,
                        isNFSW
                    FROM (
                        SELECT *,
                            json_extract(data, '$.class.explicit') > 0.3 OR json_extract(data, '$.class.suggestive') > 0.3 AS isNFSW
                        FROM models
                    ) AS subquery
                    WHERE (? IS NULL AND likes > 1 OR ? IS NOT NULL)
                    AND {style_query}
                    AND (? IS NULL OR EXISTS (
                            SELECT 1
                            FROM json_each(json_extract(data, '$.meta.tags'))
                            WHERE json_each.value = ?
                        ))
                    ORDER BY {sort_query}
                    LIMIT {MAX_PAGE_SIZE} OFFSET {(page - 1) * MAX_PAGE_SIZE};
        """, 
            (tag, tag, tag, tag),
        )
        results = cursor.fetchall()
        total = results[0]["total"] if results else 0
        total_pages = (total + MAX_PAGE_SIZE - 1) // MAX_PAGE_SIZE
        models_data = []
        for result in results:
            data = json.loads(result["data"])
            images = data["images"]
            filtered_images = [x for x in images if x is not None]
            # clean nulls
            data["images"] = filtered_images
            # update downloads and likes from db table
            data["downloads"] = result["downloads"]
            data["likes"] = result["likes"]
            data["isNFSW"] = bool(result["isNFSW"])
            models_data.append(data)

    return {"models": models_data, "totalPages": total_pages}


@app.get("/")
def read_root():
    # return html page from string
    return HTMLResponse(
        """
    <p>Just a bot to sync data from diffusers gallery please go to
    <a href="https://huggingface.co/spaces/huggingface-projects/diffusers-gallery" target="_blank" rel="noopener noreferrer">https://huggingface.co/spaces/huggingface-projects/diffusers-gallery</a>
    </p>"""
    )


@app.on_event("startup")
@repeat_every(seconds=60 * 60 * 6, wait_first=False)
async def repeat_sync():
    await sync_data()
    return "Synced data to huggingface datasets"