import gradio as gr
import cv2
import traceback
import numpy as np
from transformers import SamModel, SamProcessor


model = SamModel.from_pretrained('facebook/sam-vit-huge')
processor = SamProcessor.from_pretrained('facebook/sam-vit-huge')


def set_predictor(image):
    """
    Creates a Sam predictor object based on a given image and model.
    """
    device = 'cpu'
    inputs = processor(image, return_tensors='pt').to(device)
    image_embedding = model.get_image_embeddings(inputs['pixel_values'])

    return [image, image_embedding, 'Done']


def get_polygon(points, image, image_embedding):
    """
    Returns the points of the polygon given a bounding box and a prediction
    made by Sam, or if an exception was triggered, it returns such exception.
    """
    points = [int(w) for w in points.split(',')]

    device = 'cpu'
    inputs = processor(image, input_boxes=[points], return_tensors="pt").to(device)

    # pop the pixel_values as they are not neded
    inputs.pop("pixel_values", None)
    inputs.update({"image_embeddings": image_embedding})

    with torch.no_grad():
        outputs = model(**inputs)

    masks = processor.image_processor.post_process_masks(
        outputs.pred_masks.cpu(), 
        inputs["original_sizes"].cpu(), 
        inputs["reshaped_input_sizes"].cpu()
    )

    mask = masks[0].squeeze().numpy()

    img = mask.astype(np.uint8)[0]

    contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    points = contours[0]

    polygon = []
    for point in points:
        for x, y in point:
            polygon.append([int(x), int(y)])

    return polygon



with gr.Blocks() as app:
    image = gr.State()
    embedding = gr.State()

    with gr.Tab('Get embedding'):
        input_image = gr.Image(label='Image')
        output_status = gr.Textbox(label='Status')
        predictor_button = gr.Button('Send Image')


    with gr.Tab('Get points'):
        bbox = gr.Textbox(label="bbox")
        polygon = [gr.Textbox(label='Polygon')]
        points_button = gr.Button('Send bounding box')


    predictor_button.click(
        set_predictor, 
        input_image,
        [image, embedding, output_status],
    )

    points_button.click(
        get_polygon, 
        [bbox, image, embedding],
        polygon,
    )

app.queue()
app.launch(debug=True)