from keras.models import load_model
from aiohttp import ClientSession
from numpy import expand_dims as np_expand_dims
from captcha_processor import CaptchaProcessor
from asyncio import get_running_loop
from asyncio import sleep as asyncsleep
from random import randint
import aiofiles
import os

model = load_model("model.h5")
proxies = [
    str(os.environ.get("HTTP_PROXY"))
]

async def get_binary_from_link(link: str) -> bytes:
    async with ClientSession() as session:
        for _ in range(20):
            try:
                a = await session.get(link, proxy=proxies[randint(0, len(proxies)-1)])
                if int(a.status) == 200:
                    print("Got binary")
                    return await a.read()
                else:
                    await asyncsleep(0.125)
            except Exception as e:
                print(e)
        return randint(100000, 999999)


async def predict(url: str, recursion: int = 0, fnfnfn: int = randint(1, 10000000)) -> dict:
    binary = await get_binary_from_link(url)
    if type(binary) == type(0):
        return {
                "WARNING": "PROXY RETURNING INVALID IMAGE. CONTACT OWNER IMMEDIATLY.",
                "prediction": binary,
                "letters_predictions": "PROXY RETURNING INVALID IMAGE. CONTACT OWNER IMMEDIATLY.",
                "full_prediction": binary,
                "recursion": recursion
            }
    
    async with aiofiles.open(f"temp/{fnfnfn}.png", "wb") as outfile:
        print(f"Trying to do smth with {fnfnfn}")
        await outfile.write(binary)
    
    try:
        processor = CaptchaProcessor(binary)
    except Exception as e:
        if recursion > 10:
            return {
                "WARNING": "PROXY RETURNING INVALID IMAGE. CONTACT OWNER IMMEDIATLY.",
                "prediction": binary,
                "letters_predictions": "PROXY RETURNING INVALID IMAGE. CONTACT OWNER IMMEDIATLY.",
                "full_prediction": binary,
                "recursion": recursion
            }
        else:
            print(f"1, {recursion}, {str(e)}")
            return await predict(url, recursion + 1, fnfnfn)
    
    try:
        processor.replace_color(processor.get_background_color(), processor.WHITE_RGB)
        processor.replace_colors(processor.get_letters_color(), processor.WHITE_RGB)
    except Exception as e:
        if recursion > 10:
            return {
                "WARNING": "SOMETHING WENT WRONG. CONTACT OWNER IMMEDIATLY.",
                "prediction": binary,
                "letters_predictions": "SOMETHING WENT WRONG. CONTACT OWNER IMMEDIATLY.",
                "full_prediction": binary,
                "recursion": recursion
            }
        else:
            print(f"2, {recursion}, {str(e)}")
            return await predict(url, recursion + 1, fnfnfn)
    
    try:
        processor.convert_color_space(6)
    except Exception as e:
        if recursion > 10:
            return {
                "WARNING": "SOMETHING WENT WRONG. CONTACT OWNER IMMEDIATLY.",
                "prediction": binary,
                "letters_predictions": "SOMETHING WENT WRONG. CONTACT OWNER IMMEDIATLY.",
                "full_prediction": binary,
                "recursion": recursion
            }
        else:
            print(f"3, {recursion}, {str(e)}")
            return await predict(url, recursion + 1, fnfnfn)
    
    try:
        processor.threshold()
    except Exception as e:
        if recursion > 10:
            return {
                "WARNING": "PROXY RETURNING INVALID IMAGE. CONTACT OWNER IMMEDIATLY.",
                "prediction": binary,
                "letters_predictions": "PROXY RETURNING INVALID IMAGE. CONTACT OWNER IMMEDIATLY.",
                "full_prediction": binary,
                "recursion": recursion
            }
        else:
            print(f"4, {recursion}, {str(e)}")
            return await predict(url, recursion + 1, fnfnfn)
        
    try:
        processor.increase_letters_size(2)
    except IndexError:
        return await predict(url, recursion + 1, fnfnfn)
    letters = processor.slice_letters()
    if len(letters) != 6: return await predict(url, recursion + 1, fnfnfn)
    shorts = []
    final = ""
    letters_solving = [
        get_running_loop().run_in_executor(None, model.predict, np_expand_dims(letter, axis=0))
        for letter in letters
    ]
    letters_solving = [await result for result in letters_solving]
    fulls = [list(map(lambda x: float(x), letter[0])) for letter in letters_solving]
    for prediction in fulls: shorts.append(prediction.index(max(*prediction)))
    for short in shorts: final += str(short)
    return {
        "prediction": final,
        "letters_predictions": shorts,
        "full_prediction": fulls,
        "recursion": recursion
    }