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

# Dummy functions (replace with actual Material-Map-Generator logic if you have the models)
def generate_normal_map(image):
    # Placeholder: Convert to grayscale and apply Sobel filter
    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)
    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)
    normal = np.dstack((sobelx, sobely, np.ones_like(img) * 255))
    return Image.fromarray(np.uint8(normal))

def generate_displacement_map(image):
    # Placeholder: Convert to grayscale
    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
    return Image.fromarray(img)

def generate_roughness_map(image):
    # Placeholder: Invert grayscale
    img = 255 - cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
    return Image.fromarray(img)

def process_image(input_image):
    normal_map = generate_normal_map(input_image)
    displacement_map = generate_displacement_map(input_image)
    roughness_map = generate_roughness_map(input_image)
    return normal_map, displacement_map, roughness_map

# Gradio Interface
interface = gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=[
        gr.Image(type="pil", label="Normal Map"),
        gr.Image(type="pil", label="Displacement Map"),
        gr.Image(type="pil", label="Roughness Map")
    ],
    title="Material Map Generator",
    description="Upload an image to generate Normal, Displacement, and Roughness maps."
)

interface.launch()