import gradio as gr
import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import io
from sam2.sam2_image_predictor import SAM2ImagePredictor

# Set up device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load SAM 2 model
predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-large")

def fig2img(fig):
    buf = io.BytesIO()
    fig.savefig(buf)
    buf.seek(0)
    img = Image.open(buf)
    return img

def plot_masks(image, masks):
    fig, ax = plt.subplots(figsize=(10, 10))
    ax.imshow(image)
    
    for mask in masks:
        masked = np.ma.masked_where(mask == 0, mask)
        ax.imshow(masked, alpha=0.5, cmap=plt.cm.get_cmap('jet'))
    ax.axis('off')
    plt.close()
    return fig2img(fig)

def segment_everything(input_image):
    try:
        if input_image is None:
            return None, "Please upload an image before submitting."
        
        input_image = Image.fromarray(input_image).convert("RGB")
        
        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
            predictor.set_image(input_image)
            # Use 'everything' prompt
            masks, _, _ = predictor.predict([])
        
        # Plot the results
        result_image = plot_masks(input_image, masks)
        
        return result_image, f"Segmented everything in the image. Found {len(masks)} objects."
    
    except Exception as e:
        return None, f"An error occurred: {str(e)}"

# Create Gradio interface
iface = gr.Interface(
    fn=segment_everything,
    inputs=[
        gr.Image(type="numpy", label="Upload an image")
    ],
    outputs=[
        gr.Image(type="pil", label="Segmented Image"),
        gr.Textbox(label="Status")
    ],
    title="SAM 2 Everything Segmentation",
    description="Upload an image to segment all objects using SAM 2."
)

# Launch the interface
iface.launch()