import gradio as gr

from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import tensorflow as tf
from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation

feature_extractor = SegformerFeatureExtractor.from_pretrained(
    "nvidia/segformer-b3-finetuned-cityscapes-1024-1024"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
    "nvidia/segformer-b3-finetuned-cityscapes-1024-1024"
)

def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [
        [255, 0, 0],
        [255, 94, 0],
        [255, 187, 0],
        [255, 228, 0],
        [171, 242, 0],
        [29, 219, 22],
        [0, 216, 255],
        [0, 84, 255],
        [1, 0, 255],
        [95, 0, 255],
        [255, 0, 221],
        [255, 0, 127],
        [0, 0, 0],
        [255, 255, 255],
        [255, 216, 216],
        [250, 224, 212],
        [250, 236, 197],
        [250, 244, 192],
        [228, 247, 186],
        [206, 251, 201],
        [212, 244, 250],
        [217, 229, 255],
        [218, 217, 255],
        [232, 217, 255],
        [255, 217, 250],
        [255, 217, 236],
        [246, 246, 246],
        [234, 234, 234],
        [255, 167, 167],
        [255, 193, 158],
        [255, 224, 140],
        [250, 237, 125],
        [206, 242, 121],
        [183, 240, 177],
        [178, 235, 244],
        [178, 204, 255],
        [181, 178, 255],
        [209, 178, 255],
        [255, 178, 245],
        [255, 178, 217],
        [213, 213, 213],
        [189, 189, 189],
        [241, 95, 95],
        [242, 150, 97],
        [242, 203, 97],
        [229, 216, 92],
        [188, 229, 92],
        [134, 229, 127],
        [92, 209, 229],
        [103, 153, 255],
        [107, 102, 255],
        [165, 102, 255],
        [243, 97, 220],
        [243, 97, 166],
        [166, 166, 166],
        [140, 140, 140],
        [93, 93, 93],
        [116, 116, 116],
        [217, 65, 140],
        [217, 65, 197],
        [128, 65, 217],
        [70, 65, 217],
        [67, 116, 217],
        [61, 183, 204],
        [71, 200, 62],
        [159, 201, 60],
        [196, 183, 59],
        [204, 166, 61],
        [204, 114, 61],
        [204, 61, 61],
        [152, 0, 0],
        [153, 56, 0],
        [153, 112, 0],
        [153, 138, 0],
        [107, 153, 0],
        [47, 157, 39],
        [0, 130, 153],
        [0, 51, 153],
        [5, 0, 153],
        [63, 0, 153],
        [153, 0, 133],
        [153, 0, 76],
        [76, 76, 76],
        [53, 53, 53],
        [25, 25, 25],
        [33, 33, 33],
        [102, 0, 51],
        [102, 0, 88],
        [42, 0, 102],
        [3, 0, 102],
        [0, 34, 102],
        [0, 87, 102],
        [34, 116, 28],
        [71, 102, 0],
        [102, 92, 0],
        [102, 75, 0],
        [102, 37, 0],
        [103, 0, 0]

    ]

labels_list = []

with open(r'labels.txt', 'r') as fp:
    for line in fp:
        labels_list.append(line[:-1])

colormap = np.asarray(ade_palette())

def label_to_color_image(label):
    if label.ndim != 2:
        raise ValueError("Expect 2-D input label")

    if np.max(label) >= len(colormap):
        raise ValueError("label value too large.")
    return colormap[label]

def draw_plot(pred_img, seg):
    fig = plt.figure(figsize=(20, 15))

    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])

    plt.subplot(grid_spec[0])
    plt.imshow(pred_img)
    plt.axis('off')
    LABEL_NAMES = np.asarray(labels_list)
    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)

    unique_labels = np.unique(seg.numpy().astype("uint8"))
    ax = plt.subplot(grid_spec[1])
    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
    ax.yaxis.tick_right()
    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
    plt.xticks([], [])
    ax.tick_params(width=0.0, labelsize=25)
    return fig

def sepia(input_img):
    input_img = Image.fromarray(input_img)

    inputs = feature_extractor(images=input_img, return_tensors="tf")
    outputs = model(**inputs)
    logits = outputs.logits

    logits = tf.transpose(logits, [0, 2, 3, 1])
    logits = tf.image.resize(
        logits, input_img.size[::-1]
    )  # We reverse the shape of `image` because `image.size` returns width and height.
    seg = tf.math.argmax(logits, axis=-1)[0]

    color_seg = np.zeros(
        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
    )  # height, width, 3
    for label, color in enumerate(colormap):
        color_seg[seg.numpy() == label, :] = color

    # Show image + mask
    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
    pred_img = pred_img.astype(np.uint8)

    fig = draw_plot(pred_img, seg)
    return fig

demo = gr.Interface(fn=sepia,
                    inputs=gr.Image(shape=(400, 600)),
                    outputs=['plot'],
                    examples=["cityscape-1.jpg", "cityscape-2.jpg", "cityscape-3.jpg"],
                    allow_flagging='never')


demo.launch()