app.py

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

# Paths (Adjust these paths according to your environment)
PCA_MODEL_PATH = "pca_texture_model.npy"

# Load PCA model
pca = np.load(PCA_MODEL_PATH, allow_pickle=True).item()
TEXTURE_SIZE = int(np.sqrt(pca.mean_.shape[0] // 3))

# PCA attributes
mean_texture = pca.mean_
components = pca.components_
n_components = components.shape[0]

# Calculate slider ranges
slider_ranges = [3 * np.sqrt(var) for var in pca.explained_variance_]

def reconstruct_texture(component_values):
    # Reconstruct the texture using the PCA components
    new_texture = mean_texture + np.dot(component_values, components)
    new_texture = np.clip(new_texture, 0, 255).astype(np.uint8)
    new_texture = new_texture.reshape((TEXTURE_SIZE, TEXTURE_SIZE, 3))
    new_texture = cv2.cvtColor(new_texture, cv2.COLOR_BGR2RGB)
    image = Image.fromarray(new_texture)
    return image

def main():
    # Create a list of sliders
    sliders = []
    for i in range(n_components):
        range_limit = slider_ranges[i]
        sliders.append(gr.Slider(minimum=-range_limit, maximum=range_limit, step=10, label=f"Component {i+1}", value=0))
    
    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column():
                # Create sliders
                for slider in sliders:
                    slider.render()
            with gr.Column():
                image_output = gr.Image(label="Generated Texture", type="pil")
                image_output.render()
                randomize_button = gr.Button("Randomize")
                randomize_button.render()
        
        # Update texture when sliders change
        def on_slider_change(*args):
            component_values = np.array([s.value for s in sliders])
            image = reconstruct_texture(component_values)
            image_output.update(value=image)

        for slider in sliders:
            slider.change(fn=on_slider_change, inputs=None, outputs=None)
        
        # Randomize button
        def on_randomize_click():
            sampled_coefficients = np.random.normal(0, np.sqrt(pca.explained_variance_), size=n_components)
            for i, slider in enumerate(sliders):
                slider.update(value=sampled_coefficients[i])
            image = reconstruct_texture(sampled_coefficients)
            image_output.update(value=image)

        randomize_button.click(fn=on_randomize_click, inputs=None, outputs=None)
    
    demo.launch()

if __name__ == "__main__":
    main()