import numpy as np
from skimage.metrics import structural_similarity as ssim
import gradio as gr
import cv2

# Function to remove text from image (placeholder implementation)
def remove_text(image):
    # Placeholder implementation
    # Implement your own text removal algorithm here
    processed_image = image.copy()
    # Your text removal logic goes here
    return processed_image

# Function to calculate SSIM between two images
def calculate_similarity(img1, img2):
    if len(img1.shape) == 2:
        img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2RGB)
    if len(img2.shape) == 2:
        img2 = cv2.cvtColor(img2, cv2.COLOR_GRAY2RGB)
    max_height = max(img1.shape[0], img2.shape[0])
    max_width = max(img1.shape[1], img2.shape[1])
    img1_resized = cv2.resize(img1, (max_width, max_height))
    img2_resized = cv2.resize(img2, (max_width, max_height))
    
    # Calculate similarity score based on SSIM
    ssim_score = ssim(img1_resized, img2_resized, win_size=3, multichannel=True)
    
    return ssim_score

# Rest of the code remains the same
def image_similarity(img1, img2):
    img1 = img1.astype(np.uint8)
    img2 = img2.astype(np.uint8)
    
    # Remove text from images
    img1_processed = remove_text(img1)
    img2_processed = remove_text(img2)
    
    # Calculate similarity score based on SSIM
    ssim_score = calculate_similarity(img1, img2)
    
    # Calculate similarity score after text removal
    additional_score = calculate_similarity(img1_processed, img2_processed)
    
    result = f"SSIM Score: {ssim_score:.4f}\nAdditional Score (after text removal): {additional_score:.4f}"
    return result

iface = gr.Interface(
    fn=image_similarity,
    inputs=[
        gr.inputs.Image(type="numpy", label="Image 1"),
        gr.inputs.Image(type="numpy", label="Image 2")
    ],
    outputs="text",
    title="Image Similarity Calculator",
    description="Upload two images to compute their similarity."
)

iface.launch()