import subprocess
from PIL import Image

def download_file(url, output_filename):
    command = ['wget', '-O', output_filename, '-q', url]
    subprocess.run(command, check=True)

url1 = 'https://storage.googleapis.com/mediapipe-models/image_segmenter/selfie_multiclass_256x256/float32/latest/selfie_multiclass_256x256.tflite'
url2 = 'https://storage.googleapis.com/mediapipe-models/image_segmenter/selfie_segmenter/float16/latest/selfie_segmenter.tflite'

filename1 = 'selfie_multiclass_256x256.tflite'
filename2 = 'selfie_segmenter.tflite'

download_file(url1, filename1)
download_file(url2, filename2)

import cv2
import mediapipe as mp
import numpy as np
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
import random
import gradio as gr
import spaces
import torch
from diffusers import FluxInpaintPipeline
from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

bfl_repo="black-forest-labs/FLUX.1-dev"

BG_COLOR = (255, 255, 255) # white
MASK_COLOR = (0, 0 , 0) # black

def maskHead(input):
  base_options = python.BaseOptions(model_asset_path='selfie_multiclass_256x256.tflite')
  options = vision.ImageSegmenterOptions(base_options=base_options,
                                       output_category_mask=True)

  with vision.ImageSegmenter.create_from_options(options) as segmenter:
    image = mp.Image.create_from_file(input)

    segmentation_result = segmenter.segment(image)

    hairmask = segmentation_result.confidence_masks[1]
    facemask = segmentation_result.confidence_masks[3]

    image_data = image.numpy_view()
    fg_image = np.zeros(image_data.shape, dtype=np.uint8)
    fg_image[:] = MASK_COLOR
    bg_image = np.zeros(image_data.shape, dtype=np.uint8)
    bg_image[:] = BG_COLOR

    combined_mask = np.maximum(hairmask.numpy_view(), facemask.numpy_view())

    condition = np.stack((combined_mask,) * 3, axis=-1) > 0.2
    output_image = np.where(condition, fg_image, bg_image)

    return output_image

def random_positioning(input, output_size=(1024, 1024)):
    background = cv2.imread("default.jpeg")
    if background is None:
        raise ValueError("Unable to load background image")

    background = cv2.resize(background, output_size, interpolation=cv2.INTER_AREA)
    
    if input is None:
        raise ValueError("Unable to load input image")

    scale_factor = random.uniform(0.5, 1.0)
    new_size = (int(input.shape[1] * scale_factor), int(input.shape[0] * scale_factor))
    
    resized_image = cv2.resize(input, new_size, interpolation=cv2.INTER_AREA)

    if background.shape[2] != resized_image.shape[2]:
        raise ValueError("Input image and background image must have the same number of channels")

    x_offset = random.randint(0, output_size[0] - new_size[0])
    y_offset = random.randint(0, output_size[1] - new_size[1])
    background[y_offset:y_offset+new_size[1], x_offset:x_offset+new_size[0]] = resized_image
    
    return background


def remove_background(image_path, mask):
    image = cv2.imread(image_path)
    inverted_mask = cv2.bitwise_not(mask)
    
    _, binary_mask = cv2.threshold(inverted_mask, 127, 255, cv2.THRESH_BINARY)
    
    result = np.zeros_like(image, dtype=np.uint8)
    
    result[binary_mask == 255] = image[binary_mask == 255]
    
    return result
    
pipe = FluxInpaintPipeline.from_pretrained(bfl_repo, torch_dtype=torch.bfloat16).to(DEVICE)
MAX_SEED = np.iinfo(np.int32).max
TRIGGER = "a photo of TOK"

print(dir(pipe))


@spaces.GPU(duration=100)
def execute(image, prompt, debug=False):
  if not prompt :
        gr.Info("Please enter a text prompt.")
        return None

  if not image :
        gr.Info("Please upload a image.")
        return None

  img = cv2.imread(image)
  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

  imgs = [ random_positioning(img), random_positioning(img)]

  pipe.load_lora_weights("XLabs-AI/flux-RealismLora", weight_name='lora.safetensors')
  response = []

  for image in range(len(imgs)):
    seed_slicer = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed_slicer)

    current_img = imgs[image]
    cv2.imwrite('base_image.jpg', current_img)
    cv2.imwrite("mask.jpg", maskHead('base_image.jpg'))
    
    im = Image.open('base_image.jpg')
    np_arr = np.array(im)
    rgb_image = cv2.cvtColor(np_arr, cv2.COLOR_BGR2RGB)
    im =  Image.fromarray(rgb_image)
    mask = Image.open('mask.jpg')

    result = pipe(
            prompt=f"{prompt} {TRIGGER}",
            image=im,
            mask_image=mask,
            width=1024,
            height=1024,
            strength=0.85,
            generator=generator,
            num_inference_steps=28,
            max_sequence_length=256,
            joint_attention_kwargs={"scale": 0.9},
        ).images[0]

    if debug:
        response.append(im)
        response.append(mask)
    response.append(result)

  return response

# Created by Fountai
# https://x.com/EuFountai
description = "This is an unofficial implementation of the ip face adapter for FLUX DEV and does not explicitly follow the ip face model, I created a wrapper with inpaint and mediapipe, I like to call Fake IP Adapter"
title = "Flux IP Face Adapter"
iface = gr.Interface(
    fn=execute,
    description=description,
    title=title,
    inputs=[
        gr.Image(type="filepath"),
        gr.Textbox(label="Prompt"),
        gr.Checkbox(label="Debug Mode")
    ],
    outputs="gallery"
)

iface.launch()