import gradio as gr

import torch
from fastai.vision.all import *
from PIL import ImageFilter, ImageEnhance, ImageDraw
from diffusers.utils import make_image_grid
from tqdm import tqdm
from diffusers import AutoPipelineForInpainting, LCMScheduler, DDIMScheduler
from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel
import numpy as np
from PIL import Image
from datetime import datetime

preferred_device = "cuda" if torch.cuda.is_available() else "cpu"
preferred_dtype = torch.float32 if preferred_device == 'cpu' else torch.float16

def label_func(fn): return path/"labels"/f"{fn.stem}_P{fn.suffix}"

segmodel = load_learner("camvid-512.pkl")

inpainting_pipeline = AutoPipelineForInpainting.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    revision="fp16",
    torch_dtype=preferred_dtype,
).to(preferred_device)

working_size = (512, 512)

default_inpainting_prompt = "watercolor of a leafy pedestrian mall at golden hour with multiracial genderqueer joggers and bicyclists and wheelchair users talking and laughing"

seg_vocabulary = ['Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building', 'Car',
       'CartLuggagePram', 'Child', 'Column_Pole', 'Fence', 'LaneMkgsDriv',
       'LaneMkgsNonDriv', 'Misc_Text', 'MotorcycleScooter', 'OtherMoving',
       'ParkingBlock', 'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk',
       'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone',
       'TrafficLight', 'Train', 'Tree', 'Truck_Bus', 'Tunnel',
       'VegetationMisc', 'Void', 'Wall']

ban_cars_mask = np.array([0, 0, 0, 0, 0, 1,
                 0, 0, 1, 0, 1,
                 1, 1, 0, 0,
                 1, 0, 1, 1, 1,
                 1, 0, 1, 1,
                 1, 0, 0, 0, 1,
                 0, 1, 0], dtype=np.uint8)

def get_seg_mask(img):
    mask = segmodel.predict(img)[0]
    return mask


def app(img, prompt):
    start_time = datetime.now().timestamp()
    old_size = Image.fromarray(img).size
    img = np.array(Image.fromarray(img).resize(working_size))
    mask = ban_cars_mask[get_seg_mask(img)] * 255
    mask_time = datetime.now().timestamp()
    print(prompt.__class__, img.__class__, mask.__class__, img.shape, mask.shape)
    overlay_img = inpainting_pipeline(
        prompt=prompt,
        image=img,
        mask_image=mask,
        strength=0.95,
        num_inference_steps=20,
    ).images[0]
    end_time = datetime.now().timestamp()
    draw = ImageDraw.Draw(overlay_img)
    # replace spaces with newlines after many words to line break prompt
    prompt = " ".join([prompt.split(" ")[i] if (i+1) % 5 else prompt.split(" ")[i] + "\n" for i in range(len(prompt.split(" ")))])

    draw.text((50, 10), f"Old size: {old_size}\nTotal duration: {int(1000 * (end_time - start_time))}ms\nSegmentation {int(1000 * (mask_time - start_time))}ms / inpainting {int(1000 * (end_time - mask_time))} \n<{prompt}>", fill=(255, 255, 255))
    return overlay_img

#ideally:
#iface = gr.Interface(app, gr.Image(sources=["webcam"], streaming=True), "image", live=True)
iface = gr.Interface(app, [gr.Image(), gr.Textbox(value=default_inpainting_prompt)], "image")
iface.launch()