app.py

import gradio as gr
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
import torch
from PIL import Image
import numpy as np

def process(input_image, prompt):
    inputs = processor(text=prompt, images=input_image, padding="max_length", return_tensors="pt")
    # predict
    with torch.no_grad():
        outputs = model(**inputs)
    preds = torch.sigmoid(outputs.logits).squeeze().detach().cpu().numpy()
    preds = np.where(preds > 0.5, 255, 0).astype(np.uint8)
    preds = Image.fromarray(preds.astype(np.uint8))
    preds = np.array(preds.resize((input_image.width, input_image.height)))
    print(preds)
    return preds
    
if __name__ == '__main__':
    processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
    model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
    input_image = gr.inputs.Image(label='image', type='pil')
    prompt = gr.Textbox(label='Prompt')
    ips = [
            input_image, prompt
        ]
    outputs = "image"
    input_size = (256, 256)
    output_size = (256, 256)
    iface = gr.Interface(fn=process,
                         inputs=ips,
                         outputs=outputs,
                         input_size=input_size, 
                         output_size=output_size)
    iface.launch()