import gradio as gr
from PIL import Image
import base64
import io
import glob
import cv2
import numpy as np
import torch
from controlnet_aux import HEDdetector
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler

def predict(sketch, description):
    # Convert sketch to PIL image
    sketch_pil = Image.fromarray(sketch)

    hed = HEDdetector.from_pretrained('lllyasviel/Annotators')

    image = hed(sketch_pil, scribble=True)

    model_id = "runwayml/stable-diffusion-v1-5"
    controlnet_id = "lllyasviel/sd-controlnet-scribble"

    # Load ControlNet model
    controlnet = ControlNetModel.from_pretrained(controlnet_id)

    # Create pipeline with ControlNet model
    pipe = StableDiffusionControlNetPipeline.from_pretrained(model_id, controlnet=controlnet)

    # Use improved scheduler
    pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

    # Enable smart CPU offloading and memory efficient attention
    # pipe.enable_model_cpu_offload()
    # pipe.enable_xformers_memory_efficient_attention()

    # Move pipeline to GPU
    # pipe = pipe.to("cuda")

    result = pipe(description, image, num_inference_steps=10).images[0]
    
    return result
with gr.Blocks() as iface:
    # Define sketchpad with custom size and stroke width
    sketchpad = gr.Sketchpad(shape=(400, 300), brush_radius=5, label="Sketchpad- Draw something")
    txt= gr.Textbox(lines=3, label="Description - Describe your sketch with style")
    im = gr.Image(label="Output Image", interactive=False)
    button = gr.Button(value="Submit")
    button.click(predict, inputs=[sketchpad, txt], outputs=im)
    flag= gr.CSVLogger()
    flag.setup([sketchpad, txt, im], "flagged_data_points")
    button_flag = gr.Button(value="Flag")
    button_flag.click(lambda *args: flag.flag(args), [sketchpad, txt, im], None, preprocess=False)

    # iface = gr.Interface(fn=predict, inputs=[sketchpad, "text"], outputs=im, live=False, title="Sketch2Image")
    ## get all the file path from flagged/sketch folder into a list
    sketch_path = glob.glob("flagged/sketch/*.png")
    # gr.Examples(examples = list(map(lambda x: [x ,"draw in the style of crayon by kids"], sketch_path)), inputs=[sketchpad,txt], outputs=im, fn=predict, cache_examples=True)
iface.launch()