app.py

import gradio as gr
import numpy as np
import torch
from CCAgT_utils.types.mask import Mask
from PIL import Image
from torch import nn
from transformers import SegformerFeatureExtractor
from transformers import SegformerForSemanticSegmentation


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model_hub_name = 'lapix/segformer-b3-finetuned-ccagt-400-300'

model = SegformerForSemanticSegmentation.from_pretrained(
    model_hub_name,
).to(device)
feature_extractor = SegformerFeatureExtractor.from_pretrained(
    model_hub_name,
)


def query_image(image):
    image = np.array(image)
    img = Image.fromarray(image)

    pixel_values = feature_extractor(
        image,
        return_tensors='pt',
    ).to(device)

    with torch.no_grad():
        outputs = model(pixel_values=pixel_values)

    logits = outputs.logits

    upsampled_logits = nn.functional.interpolate(
        logits,
        size=img.size[::-1],  # (height, width)
        mode='bilinear',
        align_corners=False,
    )

    segmentation_mask = upsampled_logits.argmax(dim=1)[0]

    results = Mask(segmentation_mask).colorized() / 255

    return results


title = 'SegFormer (b3) - CCAgT dataset'
description = f"""
This is demo for the SegFormer fine-tuned on sub-dataset from
[CCAgT dataset](https://huggingface.co/datasets/lapix/CCAgT). This model
was trained to segment cervical cells silver-stained (AgNOR technique)
images with resolution of 400x300. The model was available at HF hub at
[{model_hub_name}](https://huggingface.co/{model_hub_name}).
"""
examples = [
    [f'https://hf.co/{model_hub_name}/resolve/main/sampleA.png'],
    [f'https://hf.co/{model_hub_name}/resolve/main/sampleB.png'],
]

demo = gr.Interface(
    query_image,
    inputs=[gr.Image()],
    outputs='image',
    title=title,
    description=description,
    examples=examples,
)

demo.launch()