app.py

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

processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")

def process_image(image, prompt):
    inputs = processor(
        text=prompt, images=image, padding="max_length", return_tensors="pt"
    )
    with torch.no_grad():
        outputs = model(**inputs)
        preds = outputs.logits
    pred = torch.sigmoid(preds)
    mat = pred.cpu().numpy()
    mask = Image.fromarray(np.uint8(mat * 255), "L")
    mask = mask.convert("RGB")
    mask = mask.resize(image.size)
    mask = np.array(mask)[:, :, 0]
    mask_min = mask.min()
    mask_max = mask.max()
    mask = (mask - mask_min) / (mask_max - mask_min)
    return mask

def get_masks(prompts, img, threshold):
    prompts = prompts.split(",")
    masks = []
    for prompt in prompts:
        mask = process_image(img, prompt)
        mask = mask > threshold
        masks.append(mask)
    return masks

def extract_image(pos_prompts, neg_prompts, img, threshold):
    positive_masks = get_masks(pos_prompts, img, 0.5)
    negative_masks = get_masks(neg_prompts, img, 0.5)
    
    pos_mask = np.any(np.stack(positive_masks), axis=0)
    neg_mask = np.any(np.stack(negative_masks), axis=0)
    final_mask = pos_mask & ~neg_mask
    
    final_mask = Image.fromarray(final_mask.astype(np.uint8) * 255, "L")
    output_image = Image.new("RGBA", img.size, (0, 0, 0, 0))
    output_image.paste(img, mask=final_mask)
    return output_image, final_mask

iface = gr.Interface(
    fn=extract_image,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Textbox(label="Positive Prompts (comma separated)"),
        gr.Textbox(label="Negative Prompts (comma separated)"),
        gr.Slider(minimum=0, maximum=1, default=0.4, label="Threshold"),
    ],
    outputs=[
        gr.Image(type="pil", label="Output Image"),
        gr.Image(type="pil", label="Output Mask"),
    ],
)

# Launch Gradio UI
iface.launch()

# Define API interface
api_interface = gr.Interface(
    fn=extract_image,
    inputs=[
        gr.Image(type="pil", label="Input Image"),
        gr.Textbox(label="Positive Prompts (comma separated)"),
        gr.Textbox(label="Negative Prompts (comma separated)"),
        gr.Slider(minimum=0, maximum=1, default=0.4, label="Threshold"),
    ],
    outputs=[
        gr.Image(type="pil", label="Output Image"),
        gr.Image(type="pil", label="Output Mask"),
    ],
    live=True  # Setting live to True enables the API endpoint
)

# Launch API
api_interface.launch(share=True)  # share=True allows external access to the API