import pandas as pd
from PIL import Image
import streamlit as st
from streamlit_drawable_canvas import st_canvas





def expand2square(imgpath, background_color = (0,0,0)):
    pil_img = Image.open(imgpath)
    width, height = pil_img.size
    if width == height:
        return pil_img
    elif width > height:
        result = Image.new(pil_img.mode, (width, width), background_color)
        result.paste(pil_img, (0, (width - height) // 2))
        return result.resize((700, 700))
    else:
        result = Image.new(pil_img.mode, (height, height), background_color)
        result.paste(pil_img, ((height - width) // 2, 0))
        return result.resize((700, 700))

# Specify canvas parameters in application
drawing_mode = st.sidebar.selectbox(
    "Drawing tool:", ("point", "line", "rect", "circle", "transform")
)

stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 3)
if drawing_mode == 'point':
    point_display_radius = st.sidebar.slider("Point display radius: ", 1, 25, 3)
stroke_color = st.sidebar.color_picker("Stroke color hex: ")
bg_color = st.sidebar.color_picker("Background color hex: ", "#eee")

#bg_image = "./IMG_02099.jpg"
bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])

realtime_update = st.sidebar.checkbox("Update in realtime", True)

# Create a canvas component
canvas_result = st_canvas(
    fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
    stroke_width=stroke_width,
    stroke_color=stroke_color,
    background_color=bg_color,
    background_image=expand2square(bg_image) if bg_image else expand2square("./IMG_02099.jpg"),
    update_streamlit=realtime_update,
    height=700,
    width=700,
    drawing_mode=drawing_mode,
    point_display_radius=point_display_radius if drawing_mode == 'point' else 0,
    key="canvas",
)
test= st.sidebar.write(Image.open(bg_image).size if bg_image else None)

testt = st.image(Image.open(bg_image) if bg_image else Image.open("./IMG_02099.jpg"))

test2 = st.sidebar.write(bg_color)
# Do something interesting with the image data and paths
# if canvas_result.image_data is not None:
#    st.image(canvas_result.image_data)
if canvas_result.json_data is not None:
    objects = pd.json_normalize(canvas_result.json_data["objects"])  # need to convert obj to str because PyArrow
    for col in objects.select_dtypes(include=['object']).columns:
        objects[col] = objects[col].astype("str")
    st.dataframe(objects)