from typing import Optional

import numpy as np
import cv2
import streamlit as st
from PIL import Image

from sdfile import PIPELINES, generate

DEFAULT_PROMPT = "belted shirt black belted portrait-collar wrap blouse with black prints"
DEAFULT_WIDTH, DEFAULT_HEIGHT = 512,512
OUTPUT_IMAGE_KEY = "output_img"
LOADED_IMAGE_KEY = "loaded_img"

def get_image(key: str) -> Optional[Image.Image]:
    if key in st.session_state:
        return st.session_state[key]
    return None

def set_image(key:str, img: Image.Image):
    st.session_state[key] = img

def prompt_and_generate_button(prefix, pipeline_name: PIPELINES, **kwargs):
    prompt = st.text_area(
        "Prompt",
        value = DEFAULT_PROMPT,
        key = f"{prefix}-prompt"
    )
    negative_prompt = st.text_area(
        "Negative prompt",
        value = "",
        key =f"{prefix}-negative_prompt",
    )
    col1,col2 =st.columns(2)
    with col1:
        steps = st.slider(
            "Number of inference steps",
            min_value=1,
            max_value=200,
            value=30,
            key=f"{prefix}-inference-steps",
        )
    with col2:
        guidance_scale = st.slider(
            "Guidance scale",
            min_value=0.0,
            max_value=20.0,
            value= 7.5,
            step = 0.5,
            key=f"{prefix}-guidance-scale",
        )
    enable_cpu_offload = st.checkbox(
        "Enable CPU offload if you run out of memory",
        key =f"{prefix}-cpu-offload",
        value= False,
    )

    if st.button("Generate Image", key = f"{prefix}-btn"):
        with st.spinner("Generating image ..."):
            image = generate(
                prompt,
                pipeline_name,
                negative_prompt=negative_prompt,
                num_inference_steps=steps,
                guidance_scale=guidance_scale,
                enable_cpu_offload=enable_cpu_offload,
                **kwargs,
            )
            set_image(OUTPUT_IMAGE_KEY,image.copy())
        st.image(image)
def width_and_height_sliders(prefix):
    col1, col2 = st.columns(2)
    with col1:
        width = st.slider(
            "Width",
            min_value=64,
            max_value=1600,
            step=16,
            value=512,
            key=f"{prefix}-width",
        )
    with col2:
        height = st.slider(
            "Height",
            min_value=64,
            max_value=1600,
            step=16,
            value=512,
            key=f"{prefix}-height",
        )
    return width, height

def image_uploader(prefix):
    image = st.file_uploader("Image", ["jpg", "png"], key=f"{prefix}-uploader")
    if image:
        image = Image.open(image)
        print(f"loaded input image of size ({image.width}, {image.height})")
        return image

    return get_image(LOADED_IMAGE_KEY)

def sketching():
    image = image_uploader("Controlnet")

    if not image:
        return None,None
    image = cv2.imread(image)
    image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    image_blur = cv2.GaussianBlur(image,(5,5),0)
    sketch = cv2.adaptiveThreshold(image_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRES_BINARY,11,2)
    sketch_pil = Image.fromarray(sketch)
    return sketch_pil

def txt2img_tab():
    prefix = "txt2img"
    width, height = width_and_height_sliders(prefix)
    prompt_and_generate_button(prefix,"txt2img",width=width,height=height)

def sketching_tab():
    prefix = "sketch2img"
    col1,col2 = st.columns(2)
    with col1:
        sketch_pil = sketching()
    with col2:
        if sketch_pil:
            controlnet_conditioning_scale = st.slider(
                "Strength or dependence on the input sketch",
                min_value=0.0,
                max_value= 1.0,
                value = 0.5,
                step = 0.05,
                key=f"{prefix}-controlnet_conditioning_scale",
            )
            prompt_and_generate_button(
                prefix,
                "sketch2img",
                sketch_pil=sketch_pil,
                controlnet_conditioning_scale=controlnet_conditioning_scale,
            )

def main():
    st.set_page_config(layout="wide")
    st.title("Fashion-SDX: Playground")

    tab1,tab2 = st.tabs(
        ["Text to image", "Sketch to image"]
    )
    with tab1:
        txt2img_tab()
    with tab2:
        sketching_tab()

    with st.sidebar:
        st.header("Most Recent Output Image")
        output_image = get_image((OUTPUT_IMAGE_KEY))
        if output_image:
            st.image(output_image)
        else:
            st.markdown("no output generated yet")
if __name__ =="__main__":
    main()