import os
import app_configs as configs
import service
import gradio as gr
import numpy as np
import cv2 
from PIL import Image
import logging 
from huggingface_hub import hf_hub_download

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()

sam = None #service.get_sam(configs.model_type, configs.model_ckpt_path, configs.device)
red = (255,0,0)
blue = (0,0,255)

def load_sam_instance():
    global sam
    if sam is None:
        gr.Info('Initialising SAM, hang in there...')
        if not os.path.exists(configs.model_ckpt_path):
            gr.Info('Downloading weights from  hugging face hub')
            chkpt_path = hf_hub_download("ybelkada/segment-anything", configs.model_ckpt_path)
        else:
            chkpt_path = configs.model_ckpt_path
        sam = service.get_sam(configs.model_type, chkpt_path, configs.device)
    return sam

block = gr.Blocks()
with block:
    # states
    def point_coords_empty():
        return []
    def point_labels_empty():
        return []
    point_coords = gr.State(point_coords_empty)
    point_labels = gr.State(point_labels_empty)
    raw_image = gr.Image(type='pil', visible=False)

    # UI
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label='Input', height=512, type='pil')
            with gr.Row():
                point_label_radio = gr.Radio(label='Point Label', choices=[1,0], value=1)
                reset_btn = gr.Button('Reset')
                run_btn = gr.Button('Run', variant = 'primary')
        with gr.Column():
            with gr.Tab('Cutout'):
                cutout_gallery = gr.Gallery()
            with gr.Tab('Annotation'):
                masks_annotated_image = gr.AnnotatedImage(label='Segments')
            gr.Examples(examples=[['examples/cat-256.png','examples/cat-256.png']],inputs=[input_image, raw_image])
    
    # components
    components = {point_coords, point_labels, raw_image, input_image, point_label_radio, reset_btn, run_btn, cutout_gallery, masks_annotated_image}

    # event - init coords
    def on_reset_btn_click(raw_image):
        return raw_image, point_coords_empty(), point_labels_empty(), None
    reset_btn.click(on_reset_btn_click, [raw_image], [input_image, point_coords, point_labels], queue=False)

    def on_input_image_upload(input_image):
        return input_image, point_coords_empty(), point_labels_empty(), None
    input_image.upload(on_input_image_upload, [input_image], [raw_image, point_coords, point_labels], queue=False)

    # event - set coords
    def on_input_image_select(input_image, point_coords, point_labels, point_label_radio, evt: gr.SelectData):
        x, y = evt.index
        color = red if point_label_radio == 0 else blue
        img = np.array(input_image)
        cv2.circle(img, (x, y), 10, color, -1)
        img = Image.fromarray(img)
        point_coords.append([x,y])
        point_labels.append(point_label_radio)
        return img, point_coords, point_labels
    input_image.select(on_input_image_select, [input_image, point_coords, point_labels, point_label_radio], [input_image, point_coords, point_labels], queue=False)

    # event - inference
    def on_run_btn_click(data):
        sam = load_sam_instance()
        image = data[raw_image]
        if len(data[point_coords]) == 0:
            masks, _ = service.predict_all(sam, image)
        else:
            masks, _ = service.predict_conditioned(sam,
                                                   image, 
                                                   point_coords=np.array(data[point_coords]), 
                                                   point_labels=np.array(data[point_labels]))
        annotated = (image, [(masks[i], f'Mask {i}') for i in range(len(masks))])
        cutouts = [service.cutout(image, mask) for mask in masks]
        return cutouts, annotated, masks
    run_btn.click(on_run_btn_click, components, [cutout_gallery, masks_annotated_image], queue=True)

if __name__ == '__main__':
    block.queue()
    block.launch()