app.py

import gradio as gr
import cv2
import numpy as np

def compress_image(image, quality):
    quality = int(quality)  # Ensure quality is an integer
    _, encoded_img = cv2.imencode('.jpg', image, [int(cv2.IMWRITE_JPEG_QUALITY), quality])
    compressed_img = cv2.imdecode(encoded_img, 1)
    return compressed_img

def calculate_psnr(original, compressed):
    mse = np.mean((original - compressed) ** 2)
    if mse == 0:
        return float('inf')
    max_pixel_value = 255.0
    psnr = 20 * np.log10(max_pixel_value / np.sqrt(mse))
    return psnr

def process_image(image, compression_level):
    compressed_img = compress_image(image, compression_level)
    psnr_value = calculate_psnr(image, compressed_img)
    return compressed_img, f"{psnr_value:.2f} dB"

with gr.Blocks(theme="bethecloud/storj_theme") as demo:
    with gr.Row():
        image_input = gr.Image(label="Upload Image", type="numpy")
        compression_slider = gr.Slider(minimum=1, maximum=100, value=50, label="Compression Level (Quality)")
    
    compressed_image_output = gr.Image(type="numpy", label="Compressed Image")
    psnr_output = gr.Textbox(label="PSNR Value (dB)")
    
    submit_button = gr.Button("Compress and Calculate PSNR")

    submit_button.click(
        fn=process_image, 
        inputs=[image_input, compression_slider], 
        outputs=[compressed_image_output, psnr_output]
    )
    
    gr.Markdown("PSNR = 10 log10(MAX^2/MSE) = 10 log10(MAX/(MSE)^(1/2))^2 = 20 log10(MAX/(MSE)^(1/2)).")
demo.launch()