import gradio as gr
#
from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
import matplotlib.pyplot as plt
from matplotlib import gridspec
from PIL import Image
import numpy as np
import tensorflow as tf
import requests

#

feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")

urls = ["http://farm3.staticflickr.com/2523/3705549787_79049b1b6d_z.jpg",
       "http://farm8.staticflickr.com/7012/6476201279_52db36af64_z.jpg",
       "http://farm8.staticflickr.com/7180/6967423255_a3d65d5f6b_z.jpg",
       "http://farm4.staticflickr.com/3563/3470840644_3378804bea_z.jpg",
       "http://farm9.staticflickr.com/8388/8516454091_0ebdc1130a_z.jpg"]
images = []
for i in urls:
    images.append(Image.open(requests.get(i, stream=True).raw))



# inputs = feature_extractor(images=image, return_tensors="pt")
# outputs = model(**inputs)
# logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)

def my_palette():
    return [
        [131, 162, 255],
        [180, 189, 255],
        [255, 227, 187],
        [255, 210, 143],
        [248, 117, 170],
        [255, 223, 223],
        [255, 246, 246],
        [174, 222, 252],
        [150, 194, 145],
        [255, 219, 170],
        [244, 238, 238],
        [50, 38, 83],
        [128, 98, 214],
        [146, 136, 248],
        [255, 210, 215],
        [255, 152, 152],
        [162, 103, 138],
        [63, 29, 56]
    ]


labels_list = []

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

colormap = np.asarray(my_palette())


def greet(input_img):
    inputs = feature_extractor(images=input_img, return_tensors="pt")
    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


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 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]


iface = gr.Interface(
    fn=greet,
    inputs=gr.Image(shape=(640, 1280)),
    outputs=["plot"],
    examples=[images],
    allow_flagging="never")
iface.launch(share=True)