import gradio as gr
import numpy as np
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
import base64
from PIL import Image
from io import BytesIO
import torch
import clip
import json

# Load the segmentation model
sam_checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)

# Load the CLIP model
model, preprocess = clip.load("ViT-B/32")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device).eval()

def find_similarity(base64_image, text_input):
    try:
        # Decode the base64 image to bytes
        image_bytes = base64.b64decode(base64_image)

        # Convert the bytes to a PIL image
        image = Image.open(BytesIO(image_bytes))

        # Preprocess the image
        image = preprocess(image).unsqueeze(0).to(device)

        # Prepare input text
        text_tokens = clip.tokenize([text_input]).to(device)

        # Encode image and text features
        with torch.no_grad():
            image_features = model.encode_image(image)
            text_features = model.encode_text(text_tokens)

        # Normalize features and calculate similarity
        image_features /= image_features.norm(dim=-1, keepdim=True)
        text_features /= text_features.norm(dim=-1, keepdim=True)
        similarity = (text_features @ image_features.T).squeeze(0).cpu().numpy()

        return similarity
    except Exception as e:
        return json.dumps({"error": str(e)})

def segment_image(input_image, text_input):
    try:
        image_bytes = base64.b64decode(input_image)
        image = Image.open(BytesIO(image_bytes))

        # Convert the image to RGB color mode
        image = image.convert("RGB")

        mask_generator = SamAutomaticMaskGenerator(sam)
        masks = mask_generator.generate(image)

        segmented_regions = []  # List to store segmented regions with similarity scores

        for i, mask_dict in enumerate(masks):
            mask_data = (mask_dict['segmentation'] * 255).astype(np.uint8)
            segmented_region = Image.fromarray(mask_data)  # Convert mask to an image

            x, y, w, h = map(int, mask_dict['bbox'])
            cropped_region = image.crop((x, y, x + w, y + h))

            # Convert to base64 image
            buffered = BytesIO()
            cropped_region.save(buffered, format="PNG")
            segmented_image_base64 = base64.b64encode(buffered.getvalue()).decode()

            # Calculate similarity for the segmented image
            similarity = find_similarity(segmented_image_base64, text_input)

            # Append the segmented image and its similarity score
            segmented_regions.append({"image": segmented_image_base64, "similarity": similarity})

        # Sort the segmented images by similarity in descending order
        segmented_regions.sort(key=lambda x: x["similarity"], reverse=True)

        # Return the segmented images in a JSON format
        return json.dumps(segmented_regions)

    except Exception as e:
        return json.dumps({"error": str(e)})

# Create Gradio components
input_image = gr.Textbox(label="Base64 Image", lines=8)
text_input = gr.Textbox(label="Text Input")  # Use Textbox with a label
output_images = gr.outputs.JSON()

# Create a Gradio interface
gr.Interface(fn=segment_image, inputs=[input_image, text_input], outputs=output_images).launch()