app.py

import gradio as gr
from PIL import Image
import numpy as np
import tensorflow as tf
from transformers import AutoFeatureExtractor, TFAutoModelForSemanticSegmentation

# Hugging Face 모델 및 토크나이저
model_name = "nvidia/segformer-b0-finetuned-cityscapes-1024-1024"
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = TFAutoModelForSemanticSegmentation.from_pretrained(model_name)

def label_to_color_image(label, colormap):
    color_seg = np.zeros(
        (label.shape[0], label.shape[1], 3), dtype=np.uint8
    )  # height, width, 3
    for i in range(len(colormap)):
        color_seg[label.numpy() == i, :] = colormap[i]
    return color_seg

def draw_plot(pred_img, seg, colormap, labels_list):
    # your existing draw_plot function, unchanged

def huggingface_model(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]

    # Define the colormap for the cityscapes dataset
    colormap = [
        [128, 64, 128],
        [244, 35, 232],
        [70, 70, 70],
        [102, 102, 156],
        [190, 153, 153],
        [153, 153, 153],
        [250, 170, 30],
        [220, 220, 0],
        [107, 142, 35],
        [152, 251, 152],
        [0, 130, 180],
        [220, 20, 60],
        [255, 0, 0],
        [0, 0, 142],
        [0, 0, 70],
        [0, 60, 100],
        [0, 80, 100],
        [0, 0, 230],
        [119, 11, 32],
    ]

    color_seg = label_to_color_image(seg, colormap)

    # Show image + mask
    pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
    pred_img = pred_img.astype(np.uint8)

    # Draw plot
    fig = draw_plot(pred_img, seg, colormap, labels_list)
    return fig

# 여러분이 가진 labels.txt 파일의 내용을 labels_list에 할당하세요.
labels_list = ["label1", "label2", ...]

demo = gr.Interface(
    fn=huggingface_model,
    inputs=gr.Image(shape=(1024, 1024)),  # 입력 이미지 크기는 모델의 입력 크기에 맞게 조절해야 합니다.
    outputs=["plot"],
    examples=["person-1.jpg", "person-2.jpg", "person-3.jpg", "person-4.jpg", "person-5.jpg"],
    allow_flagging='never'
)

demo.launch()