app.py

import gradio as gr
import cv2, numpy as np
from PIL import Image

def clahe_uniform(image):
    # Convert PIL → OpenCV BGR
    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)

    # --- Convert to LAB color space ---
    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
    L, A, B = cv2.split(lab)

    # --- Apply CLAHE (Contrast Limited Adaptive Histogram Equalization) ---
    clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(18,18))
    L2 = clahe.apply(L)

    # --- Merge back channels and convert to RGB ---
    merged = cv2.merge([L2, A, B])
    result = cv2.cvtColor(merged, cv2.COLOR_LAB2BGR)
    result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)

    return [image, Image.fromarray(result_rgb)]

title = "🧵 Saree AI – Uniform Lighting (CLAHE)"
description = """
Upload a saree image with uneven illumination.
This app applies **CLAHE (adaptive histogram equalization)** to balance lighting locally
while preserving texture and color richness.
"""

demo = gr.Interface(
    fn=clahe_uniform,
    inputs=gr.Image(label="Upload Saree Image", type="pil"),
    outputs=[gr.Image(label="Original"), gr.Image(label="CLAHE Corrected")],
    title=title,
    description=description,
)

if __name__ == "__main__":
    demo.launch()