from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
import gradio as gr
from PIL import Image
import torch
import numpy as np
from flask import Flask, request, jsonify, send_file
from io import BytesIO
import threading

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

app = Flask(__name__)

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

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# CLIPSeg: Image Segmentation Using Text and Image Prompts")
    gr.Markdown(article)
    gr.Markdown(description)

    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="pil")
            positive_prompts = gr.Textbox(
                label="Please describe what you want to identify (comma separated)"
            )
            negative_prompts = gr.Textbox(
                label="Please describe what you want to ignore (comma separated)"
            )

            input_slider_T = gr.Slider(
                minimum=0, maximum=1, value=0.4, label="Threshold"
            )
            btn_process = gr.Button(label="Process")

        with gr.Column():
            output_image = gr.Image(label="Result")
            output_mask = gr.Image(label="Mask")

    btn_process.click(
        extract_image,
        inputs=[
            positive_prompts,
            negative_prompts,
            input_image,
            input_slider_T,
        ],
        outputs=[output_image, output_mask],
    )
def run_demo():
    demo.launch()

def run_flask():
    app.run(host='127.0.0.1', port=7860)

if __name__ == '__main__':
    # Run Gradio UI and Flask in separate threads
    gr_thread = threading.Thread(target=run_demo)
    flask_thread = threading.Thread(target=run_flask)

    gr_thread.start()
    flask_thread.start()

    gr_thread.join()
    flask_thread.join()