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(
    "nickmuchi/segformer-b4-finetuned-segments-sidewalk"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
    "nickmuchi/segformer-b4-finetuned-segments-sidewalk"
)

def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [
        [255, 0, 0],
        [255, 187, 0],
        [171, 242, 0],
        [29, 219, 22],
        [0, 216, 255],
        [0, 84, 255],
        [95, 0, 255],
        [255, 0, 221],
        [33, 147, 176],
        [255, 183, 76],
        [67, 123, 89],
        [190, 60, 45],
        [134, 112, 200],
        [56, 45, 189],
        [92, 107, 168],
        [17, 118, 176],
        [59, 50, 174],
        [206, 40, 143],
        [44, 19, 142],
        [23, 168, 75],
        [54, 57, 189],
        [144, 21, 15],
        [15, 176, 35],
        [107, 19, 79],
        [204, 52, 114],
        [48, 173, 83],
        [11, 120, 53],
        [206, 104, 28],
        [20, 31, 153],
        [27, 21, 93],
        [11, 206, 138],
        [112, 30, 83],
        [68, 91, 152],
        [153, 13, 43],
        [25, 114, 54],
    ]

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=["sidewalk-1.jpg", "sidewalk-2.jpg", "sidewalk-3.jpg", "sidewalk-4.jpg"],
                    allow_flagging='never')


demo.launch()