import os
import gradio as gr
import numpy as np
import cv2 
from PIL import Image
import torch
from inference import SegmentPredictor



sam = SegmentPredictor() #service.get_sam(configs.model_type, configs.model_ckpt_path, configs.device)
red = (255,0,0)
blue = (0,0,255)


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

    # UI
    with gr.Column():
        with gr.Row():
            input_image = gr.Image(label='Input', height=512, type='pil')
            masks_annotated_image = gr.AnnotatedImage(label='Segments', height=512)
            cutout_galary = gr.Gallery(label='Cutouts', object_fit='contain', height=512)
        with gr.Row():
            with gr.Column(scale=1):
                point_label_radio = gr.Radio(label='Point Label', choices=[1,0], value=1)
                reset_btn = gr.Button('Reset')
                sam_sgmt_everything_btn = gr.Button('Segment Everything!', variant = 'primary')
                sam_encode_btn = gr.Button('Encode', variant = 'primary')
                sam_decode_btn = gr.Button('Predict using points!')

    # components
    components = {point_coords, point_labels, raw_image, masks, cutout_idx, input_image,
                  point_label_radio, reset_btn, sam_sgmt_everything_btn, sam_encode_btn,
                  sam_decode_btn, 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):
        # encode image on upload
        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), 5, 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_click_sam_encode_btn(inputs):
        image = inputs[raw_image]
        sam.encode(image)

    def on_click_sam_dencode_btn(inputs):
        image = inputs[raw_image]
        generated_masks, _ = sam.cond_pred(pts=np.array(inputs[point_coords]), lbls=np.array(inputs[point_labels]))
        annotated = (image, [(generated_masks[i], f'Mask {i}') for i in range(len(generated_masks))])
        return {masks_annotated_image:annotated, 
                masks: generated_masks, 
                cutout_idx: set()}
    sam_encode_btn.click(on_click_sam_encode_btn, components, [masks_annotated_image, masks, cutout_idx], queue=True)
    sam_decode_btn.click(on_click_sam_dencode_btn, components, [masks_annotated_image, masks, cutout_idx], queue=True)
    #sam_sgmt_everything_btn.click(on_sam_sgmt_everything_click, components, [masks_annotated_image, masks, cutout_idx], queue=True)


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