import gradio as gr
import cv2
import numpy as np

def enhance_image(image, method, brightness=0, contrast=1.0):
    if image is None:
        return None
    img = np.array(image, dtype=np.uint8)

    if method == "Original":
        return img
    elif method == "Grayscale":
        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
        return cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
    elif method == "Histogram Equalization":
        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
        eq = cv2.equalizeHist(gray)
        return cv2.cvtColor(eq, cv2.COLOR_GRAY2RGB)
    elif method == "Gaussian Blur":
        return cv2.GaussianBlur(img, (7, 7), 0)
    elif method == "Median Filtering":
        return cv2.medianBlur(img, 5)
    elif method == "Brightness/Contrast":
        adjusted = cv2.convertScaleAbs(img, alpha=contrast, beta=brightness)
        return adjusted
    else:
        return img

def process(image, method, brightness, contrast):
    return enhance_image(image, method, brightness, contrast)

with gr.Blocks() as demo:
    gr.Markdown("## 🖼️ Image Enhancement & Restoration GUI")
    with gr.Row():
        with gr.Column():
            img_input = gr.Image(type="numpy", label="Upload Image")
            method = gr.Radio(
                ["Original", "Grayscale", "Histogram Equalization", 
                 "Gaussian Blur", "Median Filtering", "Brightness/Contrast"],
                value="Original", label="Choose Method"
            )
            brightness = gr.Slider(-100, 100, value=0, label="Brightness")
            contrast = gr.Slider(0.5, 3.0, value=1.0, step=0.1, label="Contrast")
            btn = gr.Button("Enhance Image")
        with gr.Column():
            img_output = gr.Image(type="numpy", label="Output Image")
    btn.click(fn=process, inputs=[img_input, method, brightness, contrast], outputs=img_output)
    img_input = gr.Image(type="numpy", label="Upload Image", value="demo.png")


demo.launch()