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

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

def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [
        [255, 0, 0],
        [255, 187, 0],
        [255, 228, 0],
        [29, 219, 22],
        [178, 204, 255],
        [1, 0, 255], 
        [165, 102, 255],
        [217, 65, 197],
        [116, 116, 116],
        [204, 114, 61],
        [206, 242, 121],
        [61, 183, 204],
        [94, 94, 94],
        [196, 183, 59],
        [246, 246, 246],
        [209, 178, 255],
        [0, 87, 102],
        [153, 0, 76],
        [47, 157, 39]
    ]

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]) 
    seg = tf.math.argmax(logits, axis=-1)[0]

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

    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)

    # 각 물체에 대한 예측 클래스와 확률 얻기

    unique_labels = np.unique(seg.numpy().astype("uint8"))
    class_probabilities = {}
    for label in unique_labels:
        mask = (seg.numpy() == label)
        class_name = labels_list[label]
        class_prob = tf.nn.softmax(logits.numpy()[0][:, :, label])  # softmax 적용
        class_prob = np.mean(class_prob[mask])
        class_probabilities[class_name] = class_prob * 100  # 백분율로 변환

    # Gradio Interface에 출력할 문자열 생성
    output_text = "Predicted class probabilities:\n"
    for class_name, prob in class_probabilities.items():
        output_text += f"{class_name}: {prob:.2f}%\n"


    # 정확성이 가장 높은 물체 정보 출력
    max_prob_class = max(class_probabilities, key=class_probabilities.get)
    max_prob_value = class_probabilities[max_prob_class]
    output_text += f"\nPredicted class with highest probability: {max_prob_class} \n Probability: {max_prob_value:.4f}%"

    return fig, output_text

demo = gr.Interface(fn=sepia,
                    inputs=gr.Image(shape=(400, 600)),
                    outputs=['plot', 'text'],
                    examples=["citiscapes-1.jpeg", "citiscapes-2.jpeg", "citiscapes-3.jpeg", "citiscapes-4.jpeg"],
                    allow_flagging='never')

demo.launch()