from PIL import Image
import requests

import torch

import matplotlib.pyplot as plt
import numpy as np
import io

import gradio as gr

from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation

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

def visualize_segmentation(image, prompts, preds):
    fig, ax = plt.subplots(1, len(prompts) + 1, figsize=(3*(len(prompts) + 1), 4))
    [a.axis('off') for a in ax.flatten()]
    ax[0].imshow(image)
    [ax[i+1].imshow(torch.sigmoid(preds[i][0])) for i in range(len(prompts))];
    [ax[i+1].text(0, -15, prompt) for i, prompt in enumerate(prompts)];

    buf = io.BytesIO()
    fig.savefig(buf, format='png')
    plt.close(fig)
    
    return Image.open(buf)
     

def segment(img, clases):
    image = Image.fromarray(img, 'RGB')
    prompts = clases.split(',')

    inputs = processor(text=prompts, images=[image] * len(prompts), padding="max_length", return_tensors="pt")

    
    with torch.no_grad():
        outputs = model(**inputs)
    preds = outputs.logits.unsqueeze(1)

    return visualize_segmentation(image, prompts, preds)

demo = gr.Interface(fn=segment, 
                    inputs=["image", gr.Textbox(label = 'Enter classes separated by ","')], 
                    outputs="image",
                    examples=['desayuno.jpg', 'cutlery, pancakes, blueberries, orange juice']
                   )
demo.launch()