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
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions

feature_extractor = SegformerFeatureExtractor.from_pretrained(
    "nvidia/segformer-b0-finetuned-cityscapes-512-1024"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
    "nvidia/segformer-b0-finetuned-cityscapes-512-1024"
)

def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [
        [56, 123, 45],
        [200, 32, 90],
        [123, 45, 78],
        [200, 156, 56],
        [45, 67, 123],
        [56, 45, 78],
        [45, 56, 123],
        [123, 67, 56],
        [56, 78, 123],
        [210, 90, 32],
        [123, 56, 189],
        [45, 200, 134],
        [67, 123, 56],
        [123, 45, 67],
        [90, 32, 210],
        [200, 45, 78],
        [32, 210, 90],
        [45, 123, 67],
        [165, 42, 87],
    ]

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

    custom_template = "my_custom_template.html"
model = ResNet50()
def classify_image(inp):
    inp = preprocess_input(inp)
    preds = model.predict(inp)
    label = decode_predictions(preds, top=1)[0][0][1]
    return label


demo = gr.Interface(fn=sepia,
                    inputs=gr.Image(shape=(400, 600)),
                    outputs=['plot'],
                    title="SWJIN11 TASK",
                    description="경제학과 202211357 진성원",
                    interpretation="tooltip",
                    examples=["ADE_val_00000001.jpeg", "ADE_val_00001248.jpg", "image1.jpg", "image2.jpg", "image3.jpg", "image4.jpg"],
                    allow_flagging='never')


demo.launch()