import gradio as gr
from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation

MODEL_ID = "jonathandinu/face-parsing"
processor = AutoImageProcessor.from_pretrained(MODEL_ID)
model = AutoModelForSemanticSegmentation.from_pretrained(MODEL_ID)

def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [
        [204, 87, 92],[112, 185, 212],[45, 189, 106],[234, 123, 67],[78, 56, 123],[210, 32, 89],
        [90, 180, 56],[155, 102, 200],[33, 147, 176],[255, 183, 76],[67, 123, 89],[190, 60, 45],
        [134, 112, 200],[56, 45, 189],[200, 56, 123],[87, 92, 204],[120, 56, 123],[45, 78, 123], [200, 32, 123]
    ]

labels_list = []
with open("labels.txt", "r", encoding="utf-8") as fp:
    for line in fp:
        labels_list.append(line.rstrip("\n"))

colormap = np.asarray(ade_palette(), dtype=np.uint8)

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_np):
    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_np.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 run_inference(input_img):
    # input: numpy array from gradio -> PIL
    img = Image.fromarray(input_img.astype(np.uint8)) if isinstance(input_img, np.ndarray) else input_img
    if img.mode != "RGB":
        img = img.convert("RGB")

    inputs = processor(images=img, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits  # (1, C, h/4, w/4)

    # resize to original
    upsampled = torch.nn.functional.interpolate(
        logits, size=img.size[::-1], mode="bilinear", align_corners=False
    )
    seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8)  # (H,W)

    # colorize & overlay
    color_seg = colormap[seg]                                # (H,W,3)
    pred_img = (np.array(img) * 0.5 + color_seg * 0.5).astype(np.uint8)

    fig = draw_plot(pred_img, seg)
    return fig

def run_inference(input_img):
    # input: numpy array from gradio -> PIL
    img = Image.fromarray(input_img.astype(np.uint8)) if isinstance(input_img, np.ndarray) else input_img
    if img.mode != "RGB":
        img = img.convert("RGB")

    inputs = processor(images=img, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits  # (1, C, h/4, w/4)

    # resize to original
    upsampled = torch.nn.functional.interpolate(
        logits, size=img.size[::-1], mode="bilinear", align_corners=False
    )
    seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8)  # (H,W)

    # colorize & overlay
    color_seg = colormap[seg]                                # (H,W,3)
    pred_img = (np.array(img) * 0.5 + color_seg * 0.5).astype(np.uint8)

    fig = draw_plot(pred_img, seg)
    return fig

with gr.Blocks(title="🎨 머신러닝 6차 과제", theme=gr.themes.Base(
    primary_hue="blue",      # GitHub 파란색 계열 버튼
    secondary_hue="slate",   # 회색 포인트
    neutral_hue="gray",      # 배경 톤
    text_size=gr.themes.sizes.text_md,
    font=["JetBrains Mono", "sans-serif"],  # GitHub 느낌 폰트
    radius_size=gr.themes.sizes.radius_sm
)) as demo:
    theme=gr.themes.Glass() 
    gr.Markdown("""
    # ⭐ Face Parsing Demo
    얼굴 각 부위를 자동으로 구분하는 Image Segmentation 모델입니다.

    **활용 모델:** `jonathandinu/face-parsing`  
    **컴퓨터공학전공 202111570 조항준** 

    ---

    👍 업로드한 얼굴 이미지를 분석하여, 머리카락·피부·눈·입 등의 얼굴 영역을 각각 다른 색상으로 표시합니다.\n
    👍 본 모델은 유명인사들의 얼굴로 이루어진 CelebAMask-HQ dataset 을 활용하여 학습된 모델입니다.\n
    """)
    
    gr.Markdown("""
        👍 탐지할 수 있는 영역은 아래와 같습니다. \n
    	background / skin / nose / eye_g / l_eye / r_eye / l_brow / r_brow / l_ear / r_ear / mouth / u_lip / l_lip / hair / hat	/ ear_r / neck_l / neck	/ cloth
    """)
    
    gr.Interface(
        fn=run_inference,
        inputs=gr.Image(type="numpy", label="Input Image"),
        outputs=gr.Plot(label="Overlay + Legend"),
        examples=[
            "test-1.jpg",
            "test-2.jpg",
            "test-3.jpg",
            "test-4.jpg",
            "test-5.jpg"
        ],
        flagging_mode="never",
        cache_examples=False,
    )

if __name__ == "__main__":
    demo.launch()