import numpy as np
import gradio as gr
from PIL import Image

import torch
from transformers import MobileViTFeatureExtractor, MobileViTForSemanticSegmentation

model_checkpoint = "apple/deeplabv3-mobilevit-small"
feature_extractor = MobileViTFeatureExtractor.from_pretrained(model_checkpoint) #, do_center_crop=False, size=(512, 512))
model = MobileViTForSemanticSegmentation.from_pretrained(model_checkpoint).eval()


palette = np.array(
[
    [  0,   0,   0], [192,   0,   0], [  0, 192,   0], [192, 192,   0],
    [  0,   0, 192], [192,   0, 192], [  0, 192, 192], [192, 192, 192],
    [128,   0,   0], [255,   0,   0], [128, 192,   0], [255, 192,   0],
    [128,   0, 192], [255,   0, 192], [128, 192, 192], [255, 192, 192],
    [  0, 128,   0], [192, 128,   0], [  0, 255,   0], [192, 255,   0],
    [  0, 128, 192]
],
dtype=np.uint8)



def predict(image):
    with torch.no_grad():
        inputs = feature_extractor(image, return_tensors="pt")
        outputs = model(**inputs)

    # Get preprocessed image. The pixel values don't need to be unnormalized
    # for this particular model.
    resized = (inputs["pixel_values"].numpy().squeeze().transpose(1, 2, 0)[..., ::-1] * 255).astype(np.uint8)

    # Class predictions for each pixel.
    classes = outputs.logits.argmax(1).squeeze().numpy().astype(np.uint8)

    # Super slow method but it works
    colored = np.zeros((classes.shape[0], classes.shape[1], 3), dtype=np.uint8)
    for y in range(classes.shape[0]):
        for x in range(classes.shape[1]):
            colored[y, x] = palette[classes[y, x]]

    # Resize predictions to input size (not original size).
    colored = Image.fromarray(colored)
    colored = colored.resize((resized.shape[1], resized.shape[0]), resample=Image.NEAREST)

    # Keep everything that is not background.
    mask = (classes != 0) * 255
    mask = Image.fromarray(mask.astype(np.uint8)).convert("RGB")
    mask = mask.resize((resized.shape[1], resized.shape[0]), resample=Image.NEAREST)

    # Blend with the input image.
    resized = Image.fromarray(resized)
    highlighted = Image.blend(resized, mask, 0.4)

    return colored, highlighted


gr.Interface(
    fn=predict,
    inputs=gr.inputs.Image(label="Upload image"),
    outputs=[gr.outputs.Image(label="Classes"), gr.outputs.Image(label="Highlighted")],
    title="Semantic Segmentation with MobileViT and DeepLabV3",
).launch()


# TODO: combo box with some example images