app.py

import gradio as gr
import torch
from transformers import (SegformerFeatureExtractor,
                          SegformerForSemanticSegmentation)
from PIL import Image


MODEL_PATH="./best_model_test/"

device = torch.device("cpu")

preprocessor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
model = SegformerForSemanticSegmentation.from_pretrained(MODEL_PATH)
model.eval()


def query_image(img):
    """Función para generar predicciones a la escala origina"""
    inputs = preprocessor(images=img, return_tensors="pt")
    with torch.no_grad():
        preds = model(inputs.unsqueeze(0).to(device))["logits"]
        #preds = model(image.unsqueeze(0).to(device))["logits"]
    preds_upscale = upscale_logits_modified(preds, image.shape[2])
    predict_label = torch.argmax(preds_upscale, dim=1).to(device)
    return predict_label[0,:,:].detach().cpu().numpy()


def visualize_instance_seg_mask(mask):
    return mask

demo = gr.Interface(
    query_image,
    inputs=[gr.Image()],
    outputs="image",
    title="SegFormer Model for rock glacier image segmentation"
)

demo.launch()